Adaptive Continuous Control Research Articles

Fixed-time fault-tolerant attitude control for flexible spacecraft without angular velocity sensor

This paper deals with the appealing problem of fixed-time fault-tolerant attitude control, using attitude-information-only, for a flexible spacecraft under the influence of inertial parametric variations, external disturbances, and multiple actuator faults while suppressing the flexible appendages’ vibrations without using additional sensors or smart vibration suppression actuators. First, an adaptive fixed-time model-free observer (AFTMFO) is designed, using the attitude information, for rapid estimation of unavailable angular velocity. A novel adaptive continuous control is then synthesized based on an anti-unwinding fast fixed-time nonsingular sliding surface (AFFTNSS), utilizing variable gains in both the control law and sliding surface; that simultaneously alleviates the chattering but also improves the convergence speed when compared to existing fixed-time approaches. The proposed scheme offers superior performance characteristics such as velocity sensor-free fixed-time attitude maneuvering with high pointing accuracy, fault tolerance, vibration suppression, nonsingular and chattering-free control. The spacecraft can carry out the coveted control objective in a predeterminable time independent of the knowledge of initial states while overcoming the unwinding effect to reduce the control effort and time. The fixed-time closed-loop stability of the proposed scheme is corroborated via Lyapunov techniques. Finally, a comparative simulation analysis with the existing results elucidated the proposed scheme’s efficacy.

Synchronization of stochastic switching complex networks with a target of nonzero inputs

SummaryIn this article, we study the mean square synchronization problems for complex networks with a target of nonzero inputs under stochastic switching topology that is driven by an ergodic continuous‐time Markov process. First, we design an adaptive continuous controller based on relative full states that is consist of a synchronization term and an uncertainty elimination term, where the latter term can completely eliminate the effects of target's nonzero inputs and is continuous by adding an exponential decay function with positive initial values into the denominator. By developing a stochastic Lyapunov function, we show that zero error synchronization in the mean square sense can be achieved if the union of switching graphs contains a directed spanning tree with the target being the root. Second, we design an adaptive continuous controller based on relative outputs. Finally, we give two simulation examples to validate the theoretical results.

Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems

A new adaptive-like neural control strategy for motion reference trajectory tracking for a nonlinear electromagnetic suspension dynamic system is introduced. Artificial neural networks, differential flatness and sliding modes are strategically integrated in the presented adaptive neural network control design approach. The robustness and efficiency of the magnetic suspension control system on desired smooth position reference profile tracking can be improved in this fashion. A single levitation control parameter is tuned on-line from a neural adaptive perspective by using information of the reference trajectory tracking error signal only. The sliding mode discontinuous control action is approximated by a neural network-based adaptive continuous control function. Control design is firstly developed from theoretical modelling of the nonlinear physical system. Next, dependency on theoretical modelling of the nonlinear dynamic system is substantially reduced by integrating B-spline neural networks and sliding modes in the electromagnetic levitation control technique. On-line accurate estimation of uncertainty, unmeasured external disturbances and uncertain nonlinearities are conveniently evaded. The effective performance of the robust trajectory tracking levitation control approach is depicted for multiple simulation operating scenarios. The capability of active disturbance suppression is furthermore evidenced. The presented B-spline neural network trajectory tracking control design approach based on sliding modes and differential flatness can be extended to other controllable complex uncertain nonlinear dynamic systems where internal and external disturbances represent a relevant issue. Computer simulations and analytical results demonstrate the effective performance of the new adaptive neural control method.

CONTINUOUS ADAPTIVE CONTROL OF THE KNEE JOINT MOVEMENT

In this paper, we develop a control law for Functional Electrical Stimulation (FES) application fields. This work aims to command the knee joint movement of patients in the seated position suffering from neurological disorders that lead to a partial or total paralysis of the limb. For this purpose, we propose the synthesis of an adaptive continuous control that permits to the knee joint the tracking of a time-varying desired angle. The knee movement is represented by the dynamical model of the lower limb. The designed control ensures an asymptotic stability while considering constraints such as unknown model parameters, unknown perturbation effects and unknown spasticity and fatigue parameters. The applicability and the effectiveness of the designed control law are shown through the simulation results.

Adaptive magnetorheological absorber in vehicle suspension with adjustable stiffness

A method for adaptive continuous control of an absorber with a magnetorheological fluid in a vehicle suspension is described, which can be used under the conditions of the current parametric uncertainty. A control is used, that is built according to a scheme with a current identification algorithm, with a given implicit reference model. The reference model additionally takes into account the position and speed of the sprung mass relative to the unsprung mass, and also has an additional selectable parameter that provides the set quality «softness — rigidity» of the suspension. In a model example the suspension behavior with the proposed control method is compared with a passive suspension and a suspension controlled by the SkyHook method. Keywords controlled suspension, semi-active suspension control system, magnetorheological fluid, control algorithm, adaptive control system

A novel continuous control set model predictive control to guarantee stability and robustness for buck power converter in DC microgrids

In this paper, an adaptive continuous control set model predictive control (CCS-MPC) is proposed to eliminate the instability problem caused by the constant power load (CPL) in DC–DC​ buck converter applied in DC microgrids. Based on the dynamic model of energy storage elements(inductor and capacitor) in circuit, a parallel feedforward algorithm (PFA) is designed to estimate the variation of the load and input voltage. By supplying the information of the variation to the CCS-MPC controller, the large signal stability and fast recovery performance can be ensured under the existence of disturbances. To verify the control robustness of the adaptive CCS-MPC controller, the MATLAB simulations, hardware-in-loop (HIL) experiments and rapid-control-prototype (RCP) experimental results are conducted. Moreover, the comparative simulations and HIL experiments towards other nonlinear controller are conducted to investigates its dynamic performance.

Stabilization of Constant Power Loads in DC Microgrid Systems Using an Adaptive Continuous Control Set Model Predictive Control

This paper represents an adaptive continuous control set model predictive control (CCS-MPC) to solve the disturbance-caused instability problems in a DC microgrid consisting of symmetrical parallel buck converters, constant voltage loads (CVL), and constant power loads (CPL). The symmetric model of the system is founded at first to describe and analyze the disturbance-caused instability problem. To mitigate the instability by taking the disturbances into consideration, the proposed adaptive controller is made from a double loop feedback controller and a feedforward estimation algorithm. In the voltage loop of the double loop controller, a capacitor dynamic-based voltage controller is developed, while in the current loop, a CCS-MPC algorithm is modified and applied. Meanwhile, a feedforward algorithm is developed to estimate the disturbance information and send it to the double loop controller to improve its robustness, so the composite controller can maintain the bus voltage fixed at its reference and the symmetrical equal current sharing. Finally, comparative MATLAB simulations and OPAL-RT hardware-in-the-loop experiments are conducted to verify the effectiveness and dynamic performance of the proposed algorithm towards other previous nonlinear controllers.

PWM-Based Adaptive Trajectory Tracking of Switched Buck Power Converters

Based on pulse width modulation (PWM) technique, adaptive trajectory tracking of switched Buck power converters with parameter uncertainty is investigated in this article. By the aid of the backstepping technique, an equivalent continuous adaptive tracking controller is designed to guarantee the asymptotic stability of the closed-loop error continuous system. For the switched Buck converter with unknown parameters, PWM-based adaptive tracking with the equivalent adaptive continuous control input is proposed such that the closed-loop error digital system is practically asymptotically stable, and the tracking error converges to an arbitrarily small neighborhood of the origin. Simulation results are shown to illustrate the effectiveness of the proposed new control schemes.

Unified Visual Servoing Tracking and Regulation of Wheeled Mobile Robots With an Uncalibrated Camera

In this paper, a vision-based strategy is proposed for the unified tracking and regulation control of a wheeled mobile robot (WMR) equipped with an uncalibrated monocular camera. Specifically, an online implementable method based on the projection homography technique is first proposed to estimate the partial camera intrinsic parameters. By exploiting the obtained camera intrinsic parameters, the orientation and scaled position information of the WMR is extracted from the current, the reference, and the desired images to construct the error system. Then, an adaptive continuous controller is designed to address both the trajectory tracking and regulation problems with input saturation for the WMR. The system uncertainties regarding the distance information and the camera intrinsic parameters are fully taken into consideration as well as the nonholonomic constraint. Moreover, the Lyapunov-based stability analysis is utilized to prove that the proposed controller can achieve asymptotic tracking and regulation in the presence of system uncertainties. Simulation and experimental results validate the effectiveness of the proposed strategy.

Containment control of linear multi‐agent systems with multiple leaders of bounded inputs using distributed continuous controllers

SummaryThis paper considers the containment control problem for multi‐agent systems with general linear dynamics and multiple leaders whose control inputs are possibly nonzero and time varying. Based on the relative states of neighboring agents, a distributed static continuous controller is designed, under which the containment error is uniformly ultimately bounded and the upper bound of the containment error can be made arbitrarily small, if the subgraph associated with the followers is undirected and, for each follower, there exists at least one leader that has a directed path to that follower. It is noted that the design of the static controller requires the knowledge of the eigenvalues of the Laplacian matrix and the upper bounds of the leaders’ control inputs. In order to remove these requirements, a distributed adaptive continuous controller is further proposed, which can be designed and implemented by each follower in a fully distributed fashion. Extensions to the case where only local output information is available and to the case of multi‐agent systems with matching uncertainties are also discussed. Copyright © 2014 John Wiley & Sons, Ltd.