This paper develops a multisensor data fusion-based deep learning algorithm to locate and classify faults in a leader-following multiagent system. First, sequences of one-dimensional data collected from multiple sensors of followers are fused into a two-dimensional image. Then, the image is employed to train a convolution neural network with a batch normalisation layer. The trained network can locate and classify three typical fault types: the actuator limitation fault, the sensor failure and the communication failure. Moreover, faults can exist in both leaders and followers, and the faults in leaders can be identified through data from followers, indicating that the developed deep learning fault diagnosis is distributed. The effectiveness of the deep learning-based fault diagnosis algorithm is demonstrated via Quanser Servo 2 rotating inverted pendulums with a leader-follower protocol. From the experimental results, the fault classification accuracy can reach 98.9%.

As a data-driven design method, model-free optimal control based on reinforcement learning provides an effective way to find optimal control strategies. The design of model-free optimal control is sensitive to system data because it relies on data rather than detailed dynamic models. A prerequisite for generating applicable data is that the system must be open-loop stable (with a stable equilibrium point), which restricts the data-based control design methods in actual control problems and leads to rare experimental studies or verification in the literature. To improve this situation and enrich its applications, we propose a pre-stabilized mechanism and apply it to the motion control of a mechanical system together with a reinforcement learning-based model-free optimal control method, which constitutes a so-called hierarchical control structure. We design two real-time control experiments on an underactuated system to verify its effectiveness. The control results show that the proposed hierarchical control is quite promising in controlling this mechanical system, even though it is open-loop unstable with unknown dynamics.

In this paper, the attitude tracking control problem of a rigid body is investigated where the states are quantized. An adaptive backstepping based control scheme is developed and a new approach to stability analysis is developed by constructing a new compensation scheme for the effects of the vector state quantization. It is shown that all closed-loop signals are ensured uniformly bounded and the tracking errors converge to a compact set containing the origin. Experiments on a 2 degrees-of-freedom helicopter system illustrate the proposed control scheme.

In this paper, the finite-time stabilization of the disturbed and uncertain rotary-inverted-pendulum system is studied based on the adaptive backstepping sliding mode control procedure. For this purpose, first of all, the dynamical equation of the rotary-inverted-pendulum system is obtained in the state-space form in the existence of external disturbances and model uncertainties with unknown bound. Afterward, a novel command filter is defined to enhance the control strategy by consideration of a virtual control input. Therefore, the differential signal is replaced by the output of the command filter to reduce the complicated computing in the control process. Hence, the finite-time convergence of the sliding surface to the origin is attested by using the backstepping sliding mode control scheme according to the Lyapunov theory. Besides, the unknown upper bound of the exterior perturbation and uncertainty is approximated providing the adaptive control technique. Finally, simulations and experimental results are done to demonstrate the impression and proficiency of the suggested method.

Before the pandemic, the United Nations Sustainable Development Goals included cutting learning poverty in half by 2030. The COVID-19 pandemic has had severe negative impact on education throughout the world and set back progress toward this goal. Science, Technology, Engineering and Mathematics (STEM) education include laboratory experiences. Engineering and Technology program Accreditation Agencies deem labs critical to an engineer's education and require it in the criteria for international accreditation. While converting traditional instruction to virtual instruction posed a challenge to all, developing countries faced higher constraints of limited bandwidth, connectivity, and household access to technology. Once the access problems are resolved, universities still have the challenge of providing inclusive access to online laboratory experiments, particularly for engineering students. This paper presents current solutions for online Engineering laboratories and proposes an online lab management system and a federated lab model appropriate for developing countries that the Latin American and Caribbean Consortium of Engineering Institutions (LACCEI) is currently developing and piloting.

In this paper, an integrated fault-tolerant control (FTC) strategy is proposed for unmanned helicopter system under aggressive maneuvers. In order to handle the effect of strong nonlinearities caused by large pitch angle, linear parameter varying (LPV) technique is adopted in the helicopter system modeling. Then, LPV model-based unknown input observer (UIO) design is conducted to simultaneously realize actuator faults and system state estimation, based on which an active fault-tolerant controlleris also constructed. Considering bi-directional robustness interactions between fault as well as state observer and active fault-tolerant controller, an integrated fault-tolerant controller design is further developed. Besides, actuator saturation is also considered in LPV modeling as well as integrated fault-tolerant controller design of the helicopter system. In order to guarantee the robust performance of proposed integrated fault tolerant controller design of helicopter system, energy-to-energy strategy is adopted. And the linear matrix inequality (LMI) toolbox is utilized for gain calculation. Finally, comparative experimental tests are carried out to show the effectiveness and advantages of the proposed FTC strategy.

In this paper, an actuator fault accommodation controller is developed to solve the trajectory tracking problem in Quad-Rotors under the effects of faults in multiple actuators and external disturbances. The faults are modeled as partial loss of effectiveness. The proposed fault accommodation approach is composed of a fault identification module and a baseline robust-nominal controller. The fault identification module is based on a finite-time sliding-mode observer that provides a set of residuals using only the output information. The fault accommodation strategy uses fault identification to partially compensate the actuator faults allowing the usage of a baseline robust-nominal controller that deals with the external disturbances. Numerical simulations show the performance of the proposed control strategy.

In this paper, a fixed-time dual closed-loop attitude control method is investigated for a quadrotor unmanned aerial vehicle. Firstly, a fixed-time adaptive fast super-twisting disturbance observer is presented for estimating the unknown external disturbance. A modified adaptive law is employed based on an equivalent control method to obtain proper observer gains. Secondly, a fixed-time controller is designed by using a universal barrier Lyapunov function to satisfy asymmetric tracking error constraints. Then, a tracking differentiator is utilised to arrange the transition process. Finally, the implementation of the developed method in a quadrotor unmanned aerial vehicle is performed. Through stability analysis and simulation results, the effectiveness and superiority of the proposed fixed-time control method are validated.

In robot manipulators, the joint flexibility may be or not introduced intentionally. Thus, flexible joint robots (FJRs) are useful in aerospace applications and human rehabilitation, for example. Besides, FJRs appear in industrial manipulators. The feedback linearization control has been applied to many mechatronics systems, including FJRs. However, if spring damping between the links and rotors is present, the state feedback linearization design is no longer feasible. In order to overcome this situation, in this paper, an input-output feedback linearization approach is developed to achieve trajectory tracking control of FJRs. The study is complemented with simulation results, which validates the proposed theory. By assuming the presence of spring damping, a comparison between the known state feedback linearization technique and the proposed input-output feedback linearization is given, showing better results for the introduced approach.

This paper presents an approximation-free control scheme for nonlinear helicopter systems with unknown dynamics. Without using the function approximation methods (e.g., neural network (NN) or fuzzy logic system (FLS)), the developed approximation free controller has a simple proportional-like structure, which can reduce the computational complexity, thus suitable for practical applications. Finally, comparative simulations are provided to show the effectiveness and superior performance of the proposed control scheme.

Restoring and improving the ability to walk is a top priority for individuals with movement impairments due to neurological injuries. Powered exoskeletons coupled with functional electrical stimulation (FES), called hybrid exoskeletons, exploit the benefits of activating muscles and robotic assistance for locomotion. In this paper, a cable-driven lower-limb exoskeleton is integrated with FES for treadmill walking at a constant speed. A nonlinear robust controller is used to activate the quadriceps and hamstrings muscle groups via FES to achieve kinematic tracking about the knee joint. Moreover, electric motors adjust the knee joint stiffness throughout the gait cycle using an integral torque feedback controller. For the hip joint, a robust sliding-mode controller is developed to achieve kinematic tracking using electric motors. The human-exoskeleton dynamic model is derived using Lagrangian dynamics and incorporates phase-dependent switching to capture the effects of transitioning from the stance to the swing phase, and vice versa. Moreover, low-level control input switching is used to activate individual muscles and motors to achieve flexion and extension about the hip and knee joints. A Lyapunov-based stability analysis is developed to ensure exponential tracking of the kinematic and torque closed-loop error systems, while guaranteeing that the control input signals remain bounded. The developed controllers were tested in real-time walking experiments on a treadmill in three able-bodied individuals at two gait speeds. The experimental results demonstrate the feasibility of coupling a cable-driven exoskeleton with FES for treadmill walking using a switching-based control strategy and exploiting both kinematic and force feedback.

The aim of this paper is to propose a new data-driven control scheme for multi-input-multi-output linear time-invariant systems whose system model are completely unknown. Using a non-minimal input-output realization, the proposed method can be applied to the case where the system order is unknown, provided that its upper bound is known. A workaround against measurement noise is proposed and it is shown through simulation study that the proposed method is superior to the conventional methods when dealing with input/output data corrupted by measurement noise.

This body of research is built around the development of a roaming robot for indoor settings. Quanser's ground robotics system, QBot 2e, was utilized to map parts of a room along with collecting temperature and light intensity data while automatically navigating around various obstacles. Our research includes characterization of drift in obstacle mapping, heading correction, integration of sensors, and pathfinding. The models used were implemented in MATLAB and Simulink using QUARC and run on an onboard Raspberry Pi in the QBot 2e.

The paper presents a new approach to the design of the classical PD controller for the plant in the closed loop control system. Proposed controller has two unknown adjustable parameters which are designed by the algebraic method in the two dimensional parameter space, by using the newly discovered characteristic polynomial of the row nondegenerate full transfer function matrix. The system relative stability, in regard to the chosen damping coefficient is achieved. The optimality criterion is the minimal value of the performance index and sum of squared error is taken as an objective function. The output error used in the performance index is influenced by all actions on the system at the same time: by nonzero initial conditions and by nonzero inputs. In the classical approach the output error is influenced only by nonzero inputs.
Experimental results confirm that by taking into account nonzero initial conditions, an optimal solution is obtained, which is not the case with the classical method where
optimization is performed at zero initial conditions

The ability of drones to navigate through remote terrain and avoid ground-based vehicular traffic has led to an increase in remote sensing studies and drone-based transportation services. Such applications require a payload to be suspended from the drone via a cable which can introduce instability in the drone's flight resulting from the sway of the suspended payload. Therefore, to maintain a stable flight it is key to measure the position of the payload relative to the drone. This study uses a Vicon motion capture system to measure the position of the drone and simulated Light Detection and Ranging (LiDAR) measurements for the position of the payload. LiDAR measurements are simulated using a LiDAR noise model developed from experimental results using the Velodyne VLP-16 sensor. A nonlinear dynamic model of a drone-based single cable-suspended payload is developed and a discrete Extended Kalman Filter is used to obtain an accurate estimate of the position of the drone and the position of the payload relative to the drone.

With the increasing applications of wheeled mobile manipulators (WMMs), consisting of a mobile platform (MP) and a manipulator, in diverse fields, new challenges have arisen in achieving multiple tasks such as obstacle avoidance in a constrained environment during the end-effector (EE) operation. A WMM is usually redundant due to the combination of the MP and the manipulator, making multitask control possible via employing its null space. Dual-user/two-handed teleoperation of a WMM is desirable for tasks where it is important to simultaneously control the poses of both the MP and the EE. The existing teleoperation approaches for WMMs are mostly executed at the kinematic level, without considering the nonlinear rigid-body dynamics of the WMMs. In this article, a task-priority-based dual-user teleoperation framework for a WMM is implemented to perform tasks in a constrained environment. It can simultaneously manipulate the MP and the EE, the overground obstacles are avoided by telecontrolling the MP using the WMM's null space. Any residual redundancy can be further employed for other tasks such as singularity avoidance. The stability of the entire teleoperation design is rigorously proved even with arbitrary time delays. Experiments with a dual-user teleoperation system, consisting of two local robots and an omnidirectional WMM, are conducted to verify the proposed approach's feasibility and effectiveness.

This paper discusses about a data-driven control method which is called Estimated Response Iterative Tuning (ERIT). ERIT focuses on a two-degree of freedom control system, and updates the feedforward controller from one-shot experiment. The main contribution of this paper is to propose a pre-processing for ERIT to improve robustness against noise. In particular, this paper proposes to project the measured output onto a subspace, and regard the projected signal as a noise-free output. How to design this subspace is also proposed in this paper. A practical experiment with flexible link system is shown to demonstrate the effectiveness of the proposed method.

Based on a single degree of freedom system, the inerter principles of an inertial mass damper and clutch inerter damper are introduced. The motion equations of the systems are derived, and the rotational speed and damping are considered. In addition, a reducer is innovatively combined with clutch inerter damper to significantly improve the inertance. Accordingly, an innovative reducer clutch inerter damper is proposed. Shaking table experiments are carried out on the uncontrolled inertial mass damper, clutch inerter damper, and reducer clutch inerter damper structures under the inputs of harmonic and seismic waves. Simulation models of the four types of structures are developed, and the validity of the theoretical models is verified by a comparison between the simulation and experiment. Moreover, the nonlinear models of clutch inerter damper and reducer clutch inerter damper are discussed. Finally, according to the test results, the vibration reduction effects of the three inerters are analyzed, and the reasons why they are different from the ideal clutch inerter damper are also explained. The results show that clutch inerter damper, especially reducer clutch inerter damper, has a good vibration damping performance.

Dopaminergic replacement therapies (e.g. levodopa) provide limited to no response for
axial motor symptoms including gait dysfunction and freezing of gait (FOG) in
Parkinson’s disease (PD) and Richardson’s syndrome progressive supranuclear palsy
(PSP-RS) patients. Dopaminergic-resistant FOG may be a sensorimotor processing issue
that does not involve basal ganglia (nigrostriatal) impairment. Recent studies suggest that
spinal cord stimulation (SCS) has positive yet variable effects for dopaminergic-resistant
gait and FOG in parkinsonian patients. Further studies investigating the mechanism of
SCS, optimal stimulation parameters, and longevity of effects for alleviating FOG are
warranted. The hypothesis of the research described in this thesis is that mid-thoracic,
dorsal SCS effectively reduces FOG by modulating the sensory processing system in gait
and may have a dopaminergic effect in individuals with FOG. The primary objective was
to understand the relationship between FOG reduction, improvements in upper limb
visual-motor performance, modulation of cortical activity and striatal dopaminergic
innervation in 7 PD participants. FOG reduction was associated with changes in upper
limb reaction time, speed and accuracy measured using robotic target reaching choice
tasks. Modulation of resting-state, sensorimotor cortical activity, recorded using
electroencephalography, was significantly associated with FOG reduction while
participants were OFF-levodopa. Thus, SCS may alleviate FOG by modulating cortical
activity associated with motor planning and sensory perception. Changes to striatal
dopaminergic innervation, measured using a dopamine transporter marker, were
associated with visual-motor performance improvements. Axial and appendicular motor
features may be mediated by non-dopaminergic and dopaminergic pathways, respectively.
The secondary objective was to demonstrate the short- and long-term effects of SCS for
alleviating dopaminergic-resistant FOG and gait dysfunction in 5 PD and 3 PSP-RS
participants without back/leg pain. SCS programming was individualized based on which
setting best improved gait and/or FOG responses per participant using objective gait
analysis. Significant improvements in stride velocity, step length and reduced FOG
frequency were observed in all PD participants with up to 3-years of SCS. Similar gait
and FOG improvements were observed in all PSP-RS participants up to 6-months. SCS is
iii
a promising therapeutic option for parkinsonian patients with FOG by possibly
influencing cortical and subcortical structures involved in locomotion physiology.

As passive control techniques, both inerter device (ID) and negative stiffness device (NSD) produce forces that have a phase delay of π from common springs, i.e., negative stiffness behavior, when subjected to harmonic excitations. Thus, these two are both able to assist the motion of viscous damper in typical tuned inerter-based or negative stiffness-based absorbers, like tuned viscous mass damper (TVMD) or negative stiffness amplifying damper (NSAD), leading to a damping magnification effect. However, the negative stiffness value of NSD is frequency-independent; while that value of ID is frequency-dependent. In this regard, ID and NSD will perform differently due to structural frequency variation caused by soil-structure-interaction (SSI), which in turn further affects the energy dissipation capabilities and the seismic performance of TVMD and NSAD. Based on these effects, this paper systematically compares and discusses the optimal design and seismic performance of TVMD and NSAD from the point of SSI.

Haptic interfaces are necessary to deliver tactile and kinesthetic feedback to the user of a telerobotic system or to interact with virtual objects in a simulation environment. To achieve this goal, static and dynamic forces have to be exerted to the user’s skin—in most cases to fingers and hand. The skin is able to distinguish normal and shear force, which has to be considered. The stimulation of the mechanoreceptors in the skin has to be accomplished in a frequency range up to 1 kHz. However, the sense of the direction of a force is only given in the very low-frequency range. To achieve a good haptic sensation, the latency between haptic and visual stimulation must not exceed certain thresholds.

The chapter deals with basic engineering requirements of haptic interfaces, technical solutions for basic engineering problems, and a short review of existing devices.

This paper proposes an innovative integrated path planning and trajectory tracking control framework for a quadrotor unmanned aerial vehicle (UAV) in the presence of environmental and systematic uncertainties to achieve integrated guidance and control. Firstly, in order to perform real-time path planning, a computationally cost-effective planning algorithm is designed to find an optimal and smooth path while avoiding both static and dynamic obstacles. Then, by employing the pure-pursuit path following approach, the generated geometric path is converted to a trajectory profile related to time, which serves as the reference commands for the low-level trajectory tracking controller. Finally, a novel adaptive sliding mode trajectory tracking controller is proposed to compensate model uncertainties and maintain the desired tracking performance for the studied quadrotor UAV. With the proposed adaptive schemes, overestimation of uncertain parameters can be avoided, which further contributes to avoiding control chattering of the system. The performance of the proposed framework is validated through comparative simulation and experimental tests based on a quadrotor UAV subject to model uncertainties and environmental obstacles, which confirms the effectiveness and superiority of the proposed approach for practical applications.

Experiences in managing hands-on laboratory activ-ities across five different Electrical and Computer Engineering courses or subjects in three different Australian universities during the COVID-19 pandemic are presented. With different financial, logistical and other resource constraints and lock down conditions in different state jurisdictions, a multitude of approaches were used which included: at-home labs with hardware kits posted by teaching coordinators or acquired directly from suppliers by students; remote tutor interactions; remote test-bed; remote and cloud-based assessments; and remotely accessed and manipulated lab equipment. This paper aims to share experience in maintaining hands-on learning objectives intact during mostly online teaching while providing lessons learnt and qualitative comparisons among different approaches utilised.

This paper discusses the nonlinear optimal tracking control problem for a class of Euler-Lagrange mechanical systems, expressed as the state-dependent coefficient factorization (SDCF), to solve the associated state-dependent Riccati equation derived from the nonlinear optimal tracking control problem. The nonlinear optimal tracking control's effectiveness is demonstrated through simulation and real-time experimental results, specifically by applying the proposed control theory to the well-known rotary inverted pendulum, a highly nonlinear and non-minimum phase system to perform time-varying reference signal tracking.

The online frequency estimation of forced harmonic vibrations on a building-like structure, using a nonlinear flexible vibration absorber in a cantilever beam configuration, is addressed in this article. Algebraic formulae to compute online the harmonic excitation frequency on the nonlinear vibrating mechanical system using solely available measurement signals of position, velocity, or acceleration are presented. Fast algebraic frequency estimation can, thus, be implemented to tune online a semi-active dynamic vibration absorber to obtain a high attenuation level of undesirable vibrations affecting the main mechanical system. A semi-active vibration absorber can be tuned for application where variations of the excitation frequency can be expected. Adaptive vibration absorption for forced harmonic vibration suppression for operational scenarios with variable excitation frequency can be then performed. Analytical, numerical, and experimental results to demonstrate the effectiveness and efficiency of the operating frequency estimation, as well as the acceptable attenuation level achieved by the tunable flexible vibration absorber, are presented. The algebraic parametric estimation approach can be extended to add capabilities of variable frequency vibration suppression for several configurations of dynamic vibration absorbers.

This study exhibits an order reduction output-feedback attitude stabilization scheme for 2-degree-of-freedom (2-DOF) helicopters by considering system nonlinearity and parameter variations in the controller and observer design tasks. As the first merit, the proposed observer for the angular velocity contains the disturbance estimation mechanism as a subsystem assisting the main estimator, which is independent of the system parameter information. As the second advantage, the resultant observer renders it possible to design an improved proportional-derivative-type controller that includes the active damping term and nonlinearly structured design parameters, reducing the closed-loop system order to 1. The Quanser 2-DOF helicopter system experimentally validates the practical merit of the proposed technique.

In this paper, two loop Anti-windup PID controller is designed for Set-point tracking of Rotating arm and balancing vertical position of Pendulum of Rotary Single Inverted Pendulum. Controller ((feedback)) gains are obtained via pole placement and tuned by using the LQR technique. Comparative analysis of observer based pole placement controller, LQR and two-loop Anti-windup PID controller are experimentally illustrated. Steady-state error persists during the operation of two loop PD controller. In order to reduce this steady state error, integral term is added in controller. Due to violation of voltage constraints, an undesirable phenomena known as integral windup occurs. To overcome integral windup, Anti-windup technique (conditional integration) is used with two loop PID controller. Experimental result shows that proposed two loop Anti-windup PID controller gives better set-point tracking and a minimum steady-state error as compared to other controllers.

To control unmanned aerial systems, we rarely have a perfect system model. Safe and aggressive planning is also challenging for nonlinear and under-actuated systems. Expert pilots, however, demonstrate maneuvers that are deemed at the edge of plane envelope. Inspired by biological systems, in this paper, we introduce a framework that leverages methods in the field of control theory and reinforcement learning to generate feasible, possibly aggressive, trajectories. For the control policies, Dynamic Movement Primitives (DMPs) imitate pilot-induced primitives, and DMPs are combined in parallel to generate trajectories to reach original or different goal points. The stability properties of DMPs and their overall systems are analyzed using contraction theory. For reinforcement learning, Policy Improvement with Path Integrals (PI2) was used for the maneuvers. The results in this paper show that PI2 updated policies are a feasible and parallel combination of different updated primitives transfer the learning in the contraction regions. Our proposed methodology can be used to imitate, reshape, and improve feasible, possibly aggressive, maneuvers. In addition, we can exploit trajectories generated by optimization methods, such as Model Predictive Control (MPC), and a library of maneuvers can be instantly generated. For application, 3-DOF (degrees of freedom) Helicopter and 2D-UAV (unmanned aerial vehicle) models are utilized to demonstrate the main results.

A transmissibility is a mathematical model that relates a subset of a system's outputs to another subset of outputs of the same system without knowledge of the external excitation or the dynamics of the system. This study investigates fault detection, localization, and mitigation of connected autonomous vehicles (CAV) platoons using transmissibility operators. A CAV platoon is a network of connected autonomous vehicles that communicate together to move in a specific path with a desired velocity. % Failure in a physical component of a vehicle, or failure in the form of an {internal delay}, a cyber-attack, or a communication time-delay affects the safety and security of the CAV platoons. % In this paper, we use measurements from sensors available in CAV platoons to identify transmissibility operators, which are used for health monitoring, fault localization, and fault mitigation in the platoon. We first consider the case of vehicle-to-cloud communication (V2C) to monitor the platoon health. Then, we assume that the platoon loses communication with the cloud, and we monitor the health of the platoon based on vehicle-to-vehicle (V2V) communication. We apply the proposed technique to a model of the platoon obtained using the bond graph approach, and an experimental setup consisting of three connected autonomous robots.

This manuscript describes several approaches to tune the parameters of a class of passivity-based controllers for standard nonlinear mechanical systems. In particular, we are interested in controllers that preserve the mechanical system structure in closed-loop. To this end, first, we provide tuning rules for stabilization, i.e., the rate of convergence (exponential stability) and stability margin (input-to-state stability). Then, we provide guidelines to remove the overshoot while prescribing the rise time. Additionally, we propose a methodology to tune the gyroscopic-related parameters. We also provide remarks on the damping phenomena to facilitate the practical implementation of our approaches. We conclude this paper with experimental results obtained from applying our tuning rules to an underactuated and a fully-actuated mechanical configuration.

This paper presents a common framework for designing tune mass dampers (TMD) and active mass dampers (AMD) to control the vibrations of shear buildings. This common framework is used for tuning the parameters of TMD and AMD devices in order to achieve a particular control objective. Thus, TMD and AMD can be better compared in practical applications. As an application example, two scaled TMD and AMD prototypes are tuned and installed in a two-storey building model. The parameters of the TMD and AMD are obtained by using this common framework and the same control objective. Finally, the performance assessment of these scaled prototypes is presented, highlighting the theoretical limitations and implementation problems, which can be used as practical guidelines for future real implementations.

In this paper, mathematical modeling of multi-input multi-output Quanser AERO system is obtained using Euler–Lagrange equation. Model obtained is nonlinear, and there exits cross-coupling. This nonlinearity and cross-coupling are challenging tasks for designing the controller for the Quanser AERO system. LQR controller has been widely used in literature, but it is not able to meet the desired performance specifications. To overcome this, SMC has been implemented in addition to LQR, and their performance has been compared.

Unmanned aerial vehicles (UAVs) due to their hovering and vertical take-off and landing, have acquired a lot of interest from researchers. In order to implement UAVs, different techniques and methods are used. QNET VTOL 2.0 board in the field of unmanned aerial vehicles is an important platform designed for NI ELVIS II. In this research a comparative study of PID tunning for QNET VTOL 2.0 board, using LabVIEW is presented. Three tuning methods heuristic(error and trial) method, intelligent tunning using fuzzy logic and ANFIS(adaptive neuro fuzzy inference system) are chosen for comparison on the basis of literature. The proposed PID tunning methods are applied to the existing PID controller for controlling the pitch angle of QNET VTOL 2.0 board. The main purpose of this study is to compare the performance of proposed three tuning methods on the basis of step characteristic (rise time settling time , peak time and maximum overshoot). From the experimental results it is found that implementation of ANFIS has improved the simulation results significantly as compare to the self-tuned fuzzy PID and conventional PID.

This paper presents a state feedback control design with an event-triggered scheme for the Maglev Train (MagT.) system with a saturated control input. In a state feedback control design that precisely tracks the position of the coil current and coil voltage of actuated levitation system, we first derived the mathematical model in the form of a state–space model from the MagT. model; then, we implemented the saturated control input to encounter the uncertainties. A new event-triggered mechanism, in which the threshold parameter remains constant with resulting states, is utilized to save the constrained network resources and improve information exchange efficiency. Using the Lyapunov–Krasovskii functional method, a defensive control is established in the form of linear matrix inequalities (LMIs) for the resultant system. Finally, it is shown through the most practical approach example that the proposed method in this paper is effective for ensuring better system performance.

Obtaining dynamics models is essential for robotics to achieve accurate model-based controllers and simulators for planning. The dynamics models are typically obtained using model specification of the manufacturer or simple numerical methods such as linear regression. However, this approach does not guarantee physically plausible parameters and can only be applied to kinematic chains consisting of rigid bodies. In this article, we describe a differentiable simulator that can be used to identify the system parameters of real-world mechanical systems with complex friction models, holonomic as well as non-holonomic constraints. To guarantee physically consistent parameters, we utilize virtual parameters and gradient-based optimization. The described Differentiable Newton-Euler Algorithm (DiffNEA) can be applied to a class of dynamical systems and guarantees physically plausible predictions. The extensive experimental evaluation shows, that the proposed model learning approach learns accurate dynamics models of systems with complex friction and non-holonomic constraints. Especially in the offline reinforcement learning experiments, the identified DiffNEA models excel. For the challenging ball in a cup task, these models solve the task using model-based offline reinforcement learning on the physical system. The black-box baselines fail on this task in simulation and on the physical system despite using more data for learning the model.

This thesis proposes a testbed capable of validating experimental controller designs.Because it is undesirable to implement unvalidated controller designs on physical hardware, the need for an effective testbed arises. For the testbed to be useful, it should be easy to use and contain all the hardware and software necessary to implement a controller and analyze its response. To accomplish these goals, a testbed is created by joining the National Instruments ELVIS III board with the Quanser Controls Board. Three controllers are implemented to evaluate the usefulness of the testbed. The controllers are designed using the neoclassical design technique. The neoclassical technique is an experimental design methodology that, until now, has never been implemented on a physical system. The neoclassical technique uses principles from both modern and classical approaches to control to match the transfer function of a system to an optimal transfer function; thus, simplifying the design process. Neoclassical controllers are designed in continuous time, discrete time, and discrete time with a state estimator. A PID controller is designed and used to evaluate the usefulness of the neoclassical design technique. PID controllers have been around for many years and are a highly common method of control. They have been studied extensively and there are several techniques available for controller design. Because the PID is well-known, it provides a good standard to evaluate the neoclassical technique against. A design and performance analysis is then conducted after all controllers have been implemented. The testbed is evaluated on how well it performed as well as the difficulty required to implement the individual controller designs. The performance of the neoclassical controllers is also considered. A comparison is made between the neoclassical controllers and PID controller to determine if the neoclassical method improves efficiency of controller design. It will be shown that while the testbed analyzed in this thesis may be useful as an instructional aid, it is not particularly useful in validating experimental controllers.

Most robotic systems consist of heavy machine elements that help reduce the vibrations generated by motion and that support good positional accuracy. Large-sized actuators are needed for such systems. Energy consumption significantly increases in oversized robotic systems. The proposed solution to which is to design robotic systems in lightweight and flexible forms. This makes possible smaller overall structure and actuator sizes. However, such systems suffer from residual vibrations. One well-known control solution to residual vibration problem could be input shaping techniques. In this study, a new Fuzzy Logic-based intelligent input shaping selection technique has been proposed. With this proposed approach, it is possible to set optimum settling time, positioning accuracy of robotic systems, and minimum residual vibrations by implementing the crucial open-loop control approach. Comprehensive experience is needed to overcome design issues on command shaping and system modeling. Based on these design criteria constraints and extensive knowledge requirements, the recommended solution is a Fuzzy Logic-based intelligent input shaping technique for accurate modeling and parameter estimation of the systems. The proposed approach is a Fuzzy Logic-based intelligent input shaper selection and input shaper setting determination algorithm. The applicability of the proposed method was verified on the Quanser Flexible Link experimental setup.

Most robotic systems consist of heavy machine elements that help reduce the vibrations generated by motion and that support good positional accuracy. Large-sized actuators are needed for such systems. Energy consumption significantly increases in oversized robotic systems. The proposed solution to which is to design robotic systems in lightweight and flexible forms. This makes possible smaller overall structure and actuator sizes. However, such systems suffer from residual vibrations. One well-known control solution to residual vibration problem could be input shaping techniques. In this study, a new Fuzzy Logic-based intelligent input shaping selection technique has been proposed. With this proposed approach, it is possible to set optimum settling time, positioning accuracy of robotic systems, and minimum residual vibrations by implementing the crucial open-loop control approach. Comprehensive experience is needed to overcome design issues on command shaping and system modeling. Based on these design criteria constraints and extensive knowledge requirements, the recommended solution is a Fuzzy Logic-based intelligent input shaping technique for accurate modeling and parameter estimation of the systems. The proposed approach is a Fuzzy Logic-based intelligent input shaper selection and input shaper setting determination algorithm. The applicability of the proposed method was verified on the Quanser Flexible Link experimental setup.

A general controller scheme for stabilizing a non-linear system, which has its origin from the linear system theory, is proposed in this paper. The proposed controller can stabilize the non-linear system subjected to initial conditions. An effective way to obtain the controller parameters is presented with the knowledge
of the system model. The controller is designed for the linear time-invariant (LTI) system, which can reject any disturbance acting on it. Paper emphasis the idea of an integrator controller in disturbance rejection. The concept is extended to the application to non-linear systems where the non-linearities are assessed as the disturbance to the refined linear part of the system. Boundedness and convergence of the non-linear system with the controller are proved to justify system stabilization. Hardware implementation of the controller on the 2 dof helicopter model is presented with experimental results, which validates the
proposed control scheme.

This paper explores a novel system architecture of heterogeneous collaborative system for powerline inspection using human-robotic teaming. The research uses Quanser’s Autonomous QCar and QDrone to demonstrate the results. This heterogeneous powerline inspection system consists of three separately controlled elements that work collaboratively within a centralized controlled space and those individual elements are as follows: 1) A control system operated by the human that takes cares of the Autonomous car and drone functionality, 2) An autonomous unmanned ground vehicle system that will take tasks from the human control system to drive through the path determined, and 3) An autonomous unmanned aerial vehicle system that conducts the powerline search and inspection within a specified range. All three systems operate together as one heterogeneous system, which demonstrates the application of human-robotic teaming. This paper will demonstrate how the proposed system architecture is constructed and the results obtained from it in an indoor environment is discussed.

We study the problem of estimating and tracking an unknown trajectory with a multi-rotor UAV in the presence of modeling error and external disturbances. The reference trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume the only measurements that are available are the position and orientation of the multi-rotor and the position of the reference system. We adopt an extended high-gain observer (EHGO) estimation frame work to estimate the unmeasured multi-rotor states, modeling error, external disturbances, and the reference trajectory. We design a robust output feedback controller for trajectory tracking that comprises a feedback linearizing controller and the EHGO. The proposed control method is rigorously analyzed to establish its stability properties. Finally, we illustrate our theoretical results through numerical simulation and experimental validation in which a multi-rotor tracks a moving ground vehicle with unknown trajectory and dynamics and successfully lands on the vehicle while in motion.

Reinforcement learning is the process of using trialand-error with finite rewards to encourage an agent to take the optimal action when presented with the state of its environment. Our agent is a single-inverted pendulum, attached to a cart on a one-dimensional track, with a 4-dimensional continuous state space:
[x,α, x˙,α˙] – corresponding to the x-position (right positive), angle (counterclockwise positive from 0 vertical), x-velocity, and angular velocity, as indicated in Figure 1.

Position resolution is a major problem in teleoperation applications with significant disparity between master and slave workspace. While rate mode control is suitable for slave free motion operations, it poses significant stability and performance challenges for task manipulation. In this paper, we propose a hybrid control scheme that offers seamless transition between position and rate control modes based on the environment location information collected from a range sensor. The system incorporates the strengths of position and rate control modes while masking their shortcomings. Experiments to determine the viability of this method are carried out on a single degree-of-freedom teleoperation test-bed.

Unmanned aircraft have had a great impact on society, their applications in various fields are increasing day by day, so it becomes necessary to propose, develop or validate new designs and concepts that help minimize operating errors or improve the safety of aircraft. One way to understand the operating principle of these unmanned aircraft is through laboratory prototypes or test benches; these prototypes allow experimentation and testing in controlled environments to minimize errors in their operation. This article presents a novel configuration for a laboratory prototype of a tandem-rotor helicopter with two degrees of freedom (DOF), and the dynamic modeling, control, signal filtering, and implementation of the control and filtering system using a microcontroller and a low-cost prototype made with easily accessible materials. This is to promote education, research, and technological development in this area of engineering.

This paper introduces a new continuous integral sliding mode control algorithm, where the discontinuous function of the super-twisting control law is replaced with a continuous disturbance observer for the substantial chattering attenuation. In the present integral sliding mode control, the discontinuous function generates chattering that is undesirable for several real-time applications. The proposed control strategy decreases the amplitude of the controller gain compared to the existing integral sliding mode controls, and as a consequence of this, the attenuation of chattering is achieved to a great extent. The efficacy of the proposed control algorithm is validated successfully on the single-input single-output Inverted Pendulum and 2-DOF Helicopter nonlinear coupled multi-input multi-output systems. The simulation and experimental results demonstrate the successful application of the proposed control approach to follow reference inputs and acquire robustness and stabilization of the system in the presence of limited matched perturbations and nonlinearities.

In this paper, for a class of continuous-time multivariable linear systems with strong coupling property, a new optimal tracking controller is proposed, which can simultaneously decouple the closed-loop system in its dynamic, and provide optimal performance. Firstly, a new quadratic performance index considering decoupling design is introduced, and then the optimal tracking controller is derived by minimizing the new index according to the minimum principle. Finally, the weighting matrices are provided, by using which decoupling and tracking can both be realized. Experiments on a Coupled-Tank are conducted, whose results show the superiority of the proposed method comparing with the conventional linear quadratic tracking (LQT) controller.

High-rate global navigation satellite system (GNSS) has emerged as an effective method to recover seismic waveforms without saturation and drifts, but it has the limitation of relatively lower sampling rate and higher noise level compared to seismic instruments. In this study, we present a new seismogeodetic method by integrating GNSS and accelerometer data to obtain optimal real-time seismic waveforms. Unlike traditional integration methods based on GNSS techniques of relative positioning or precise point positioning, the new method uses a GNSS time difference technique and inherits its unique advantage in real-time and high-accuracy velocity solutions. Furthermore, by incorporating the tightly coupled structure, it can overcome the cascading problem and provide more accurate and robust waveforms compared to its loosely coupled counterpart. The performance of this method is first compared with the traditional loosely coupled approach in challenging environments through a set of shake table experiments. With three GNSS satellites, this approach method can improve the accuracy of velocities and displacements by 42 and 87 per cent, respectively. With four or more GNSS satellites, the average improvements of the method reach 25 and 41 per cent for the velocities and displacements, respectively. We then validate the full performances of the method through simulated shake table experiments and collocated GNSS and accelerometer data during the 2016 Mw 6.6 central Italy earthquake. The simulated and real-event analyses demonstrate that the new integration method can take full advantage of the complementary characteristics of GNSS and accelerometer sensors. By providing more accurate and broad-band velocity and displacement waveforms in a real-time or near-real-time manner, this method is quite promising in earthquake early warning and rapid source inversion.

In this paper, we propose a new class nonlinear hybrid controller (NHC) for swinging-up and stabilizing the (under-actuated) rotary inverted pendulum system. First, the swing-up controller, which drives the pendulum up towards the desired upright position, is designed based on the feedback linearization and energy control methods. Then, the modified super-twisting sliding mode control is proposed based on the new sliding surface to stabilize both the fully-actuated (the rotary arm) and under-actuated (the pendulum) state variables. In the proposed NHC, around the upright position, the stabilization controller is applied, and in different circumstances aside from the upright position, the swing-up controller is used. We show that with the proposed NHC: (i) in the swing-up stage, the pendulum is able to reach the desired upright position; and (ii) in the stabilization stage, the closed-loop rotary inverted pendulum is asymptotically stable. We demonstrate the effectiveness of the proposed NHC through extensive experiments. In particular, (i) the faster swing-up under the similar control effort is obtained, compared with the existing fuzzy logic swing-up controller; (ii) the better stabilization control performance for the convergence of the angular positions of the rotary arm and pendulum is attained and the chattering is alleviated, compared with the existing sliding mode stabilization controllers; (iii) the better stabilization control accuracy with the faster convergence time and lower peak overshoot is accomplished, compared with the existing Fuzzy-LQR controller; and (iv) the good robustness against sudden external disturbances is achieved.

This research concerns a novel attitude stabilization structure for a ducted-fan aerial robot to work against modeling error and strong external transient disturbance, and it focuses on two main control targets: modeling error compensation, and the improvement of disturbance resistance along the rolling channel. For the first research objective, we proposed an adaptive nominal controller with the reconfigurable control law design based on the estimation of the modeling error found in the closed-loop. Results of simulations and corresponding flight tests verified that the proposed adaptive control structure is robust against both constant and time-varying modeling error. For the other research objective, a SAC (stability augmentation structure) was devised based on the CMG (control moment gyroscope) theory in order to provide extra moment which effectively withstands the transient disturbance beyond the CDG (critical disturbance gain). Furthermore, we studied the corresponding controller for the SAC via the SMC (sliding mode control) theory, while the working mechanism and performance of the SAC were verified through a specially devised prototype.

This paper presents a novel decentralized fixed-time tracking control approach, which realizes the advantages of modular robot manipulators (MRMs) with fixed-time convergence, strong robustness, and high tracking performance. First, to estimate the total uncertainty of MRMs, the fixed-time observer based on the extended state is developed. Then, combined with the disturbance observer, a novel decentralized control method based on a fixed-time control strategy was devised to accomplish global fixed-time convergence of MRMs. And, stability analysis based on Lyapunov is utilized to obtain the fixed-time stability as well as convergence time of MRMs. Finally, numerical analysis and experiment respectively verify the excellent tracking ability of the presented decentralized fixed-time tracking control.