Robust Adaptive Sliding Mode Control Research Articles

Rehabilitation of the Parkinson's tremor by using robust adaptive sliding mode controller: a simulation study.

One of the efficient methods in controlling the Parkinson's tremor is Deep Brain Stimulation (DBS) therapy. The stimulation of Basal Ganglia (BG) by DBS brings no feedback though the existence of feedback reduces the additional stimulatory signal delivered to the brain. So this study offers a new adaptive architecture of a closed-loop control system in which two areas of BG are stimulated simultaneously to decrease the following three indicators: hand tremor, the level of a delivered stimulation signal in the disease condition, and the level of a delivered stimulation signal in health condition to the disease condition. One area (STN: subthalamic nucleus) is stimulated with an adaptive sliding mode controller and the other area (GPi: Globus Pallidus internal) with partial state feedback controller. The simulation results of stimulating two areas of BG showed satisfactory performance.

Robust Adaptive Sliding Mode Control for Switched Networked Control Systems With Disturbance and Faults

In this paper, the problem of switched networked control systems (SNCSs) with external disturbance and actuator /sensor faults is investigated. Meanwhile, the communication constraints such as network-induced delay, packet dropouts, and packet disorder are considered in a communication network. A robust adaptive sliding mode control method is proposed for disturbance damping and faults tolerance, which is designed on an observer and second-order discrete-time adaptive sliding mode function. Furthermore, the reachability of sliding motion is proved. Then, the networked predictive control method is employed to compensate the communication constraints. Finally, the stability of the closed-loop system is proved, and a numerical example and a mechanical system simulation example are executed to verify the effectiveness of the proposed method.

Active and Reactive Power Control of Wind Turbine based on Doubly Fed Induction Generator using Adaptive Sliding Mode Approach

In this work, a robust Adaptive sliding mode controller (ASMC) is proposed to improve the dynamic performance of the Doubly Fed Induction generator (DFIG) based wind system under variable wind speed conditions. Firstly, the dynamic modeling of the main components of the system is performed. Thereafter, the ASMC is designed to control the active and reactive powers of the machine stator. The structure of these controllers was improved by adding two integral terms. Their sliding gains are determined using Lyapunov stability theorem to make them automatically adjusted in order to tackle the external disturbances. Maximum Power Point Tracking (MPPT) strategy was also applied to enhance the power system efficiency. Then, a comparison study with the Field Oriented Control (FOC) based on conventional PI control was conducted to assess the robustness of this technique under the DFIG parameters variations. Finally, a computer simulation was achieved in MATLAB/SIMULINK environment using 2MW wind system model. Satisfactory performances of the proposed strategy were clearly confirmed under variable operating conditions.

A Finite-Time Robust Adaptive Sliding Mode Control for Electro-Optical Targeting System With Friction Compensation

In this paper, a novel finite-time robust adaptive nonsingular fast terminal sliding mode control strategy is designed to achieve high performance control for an electro-optical targeting system which is subjected to nonlinear friction and disturbance torque. First, a robust adaptive control technique is constructed to compensate modified LuGre model and disturbance torque. Then, a nonsingular terminal fast sliding mode is integrated into the robust adaptive control technique to implement response rapidity and enhance the robustness of the closed-loop system. Furthermore, the control strategy is proved by Lyapunov criterion within finite time. Finally, simulation and experimental results indicate that high accuracy, fast response and stable control performance are obtained by the proposed control strategy for the electro-optical targeting system.

Robust Tracking Control of a Quadrotor UAV Based on Adaptive Sliding Mode Controller

In this paper, a robust adaptive sliding mode control scheme is developed for attitude and altitude tracking of a quadrotor unmanned aerial vehicle (UAV) system under the simultaneous effect of parametric uncertainties and consistent external disturbance. The underactuated dynamic model of the quadrotor UAV is first built via the Newton–Euler formalism. Considering the nonlinear and strongly coupled characteristics of the quadrotor, the controller is then designed using a sliding mode approach. Meanwhile, additional adaptive laws are proposed to further improve the robustness of the proposed control scheme against the parametric uncertainties of the system. It is proven that the control laws can eliminate the altitude and attitude tracking errors, which are guaranteed to converge to zero asymptotically, even under a strong external disturbance. Finally, numerical simulation and experimental tests are performed, respectively, to verify the effectiveness and robustness of the proposed controller, where its superiority to linear quadratic control and active disturbance rejection control has been demonstrated clearly.

Reduced order synchronisation for circular restricted three body problem using adaptive sliding mode controller

This paper studies the reduced order synchronisation for a circular restricted three body problem and Lorenz system as drive and response systems respectively via a robust adaptive sliding mode control in which controllers are based on negligible chattering phenomenon. The entire study has been carried out under the effect of both unknown model uncertainties and external disturbance. Also for the same drive-response system, a secure communication scheme has been presented.

Robust adaptive sliding mode control technique for combination synchronisation of non-identical time delay chaotic systems

This manuscript presents the methodology in which robust adaptive sliding mode control technique is used for implementing combination synchronisation of non-identical time-delay chaotic systems. To justify this methodology, modified Lorenz chaotic time delay system and Genesio time delay system are used. The stability condition for the error dynamics is analysed using Lyapunov stability theory and detailed mathematical theory. Numerical simulations are carried out to demonstrate the efficiency of the proposed approach that supports the analytical results.

Reduced order synchronisation for circular restricted three body problem using adaptive sliding mode controller

This paper studies the reduced order synchronisation for a circular restricted three body problem and Lorenz system as drive and response systems respectively via a robust adaptive sliding mode control in which controllers are based on negligible chattering phenomenon. The entire study has been carried out under the effect of both unknown model uncertainties and external disturbance. Also for the same drive-response system, a secure communication scheme has been presented.

Robust Adaptive Sliding Mode Control Design with Genetic Algorithm for Brushless DC Motor

Robust Adaptive Sliding Mode Control Design with Genetic Algorithm for Brushless DC Motor

Robust Adaptive Sliding Mode Control Design with Genetic Algorithm for Brushless DC Motor

Robust Adaptive Sliding Mode Control Design with Genetic Algorithm for Brushless DC Motor

Robust adaptive sliding mode control for a human-driven knee joint orthosis

PurposeAssistive technology products are designed to provide additional accessibility to individuals who have physical or cognitive difficulties, impairments and disabilities. The purpose of this paper is to deal with the control of a knee joint orthosis intended to be used for rehabilitation and assistive purpose; this control aims to reduce the influence of the uncertainties and eliminating the external disturbances in the system.Design/methodology/approachThis paper deals with the robust adaptive sliding mode controller (ASMC) of human-driven knee joint orthosis system with mismatched uncertainties and external disturbances. The shank-orthosis system has been modeled and its parameters have been identified. This control reduces the effect of parameter uncertainties and external disturbances on the system performance and improves the system robustness as results. The ASMC was designed to offer the possibility to track the state of the reference model. Moreover, the Lyapunov stability theory was used to study the asymptotical stability of the ASMC.FindingsThe advantage of the robust ASMC method is the tracking precision and reducing the required time for eliminating external disturbances and uncertainties. The experimental results show in real-time in terms of stability and present that the advantages of this control approach are the position tracking and robustness.Originality/valueIn this paper, to deal with the parameter uncertainties of the human-driven knee joint orthosis, an ASMC was successfully applied based on sliding mode and Lyapunov stability theory. It has good dynamic response and tracking performance. Besides, the adaptive algorithm is simple, easy to achieve and has good adaptability and robustness against the parameter variations and external disturbances. The design technique is simple and efficient. The development of this control takes into consideration the perturbation, allowing to track a desired trajectory.

Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators

A robust adaptive control method is proposed in this paper based on recurrent fuzzy wavelet neural networks (RFWNNs) system for industrial robot manipulators (IRMs) to improve high accuracy of the tracking control. The RFWNNs consist of four layers, and second layer has the feedback connections. Wavelet basis function is used as fuzzy membership function. In general, it is not easy to adopt a model-based method to achieve this control object due to the uncertainties of the IRM, such as unknown dynamic, disturbances and parameter variations. To solve this problem, all the parameters of the RFWNNs system are tuned online by an adaptive learning algorithm, and online adaptive control laws are determined by Lyapunov stability theorem. In addition, the robust controller is designed to deal with the approximation error, optimal parameter vectors and higher-order terms in Taylor series. Therefore, with the proposed control, the desired tracking performance, stability and robustness of the closed-loop manipulators system are guaranteed. The simulations and experimental performed on a three-link IRMs are provided in comparison with fuzzy wavelet neural network and robust neural fuzzy network to demonstrate the effectiveness and robustness of the proposed RFWNNs methodology.

Robust adaptive sliding mode control for uncertain systems with unknown time-varying delay input

This article focuses on robust adaptive sliding mode control law for uncertain discrete systems with unknown time-varying delay input, where the uncertainty is assumed unknown. The main results of this paper are divided into three phases. In the first phase, we propose a new sliding surface is derived within the Linear Matrix Inequalities (LMIs). In the second phase, using the new sliding surface, the novel Robust Sliding Mode Control (RSMC) is proposed where the upper bound of uncertainty is supposed known. Finally, the novel approach of Robust Adaptive Sliding ModeControl (RASMC) has been defined for this type of systems, where the upper limit of uncertainty which is assumed unknown. In this new approach, we have estimate the upper limit of uncertainties and we have determined the control law based on a sliding surface that will converge to zero. This novel control laws are been validated in simulation on an uncertain numerical system with good results and comparative study. This efficiency is emphasized through the application of the new controls on the two physical systems which are the process trainer PT326 and hydraulic system two tanks.

Torque Ripple Minimization of PMSM Based on Robust ILC Via Adaptive Sliding Mode Control

Torque ripples due to cogging torque, current measurement errors, and flux harmonics restrict the application of the permanent magnet synchronous motor (PMSM) that has a high-precision requirement. The torque pulsation varies periodically along with the rotor position, and it results in speed ripples, which further degrade the performance of the PMSM servo system. Iterative learning control (ILC), in parallel with the classical proportional integral (PI) controller (i.e., PI-ILC), is a conventional method to suppress the torque ripples. However, it is sensitive to the system uncertainties and external disturbances, i.e., it is paralyzed to nonperiodic disturbances. Therefore, this paper proposes a robust ILC scheme achieved by an adaptive sliding mode control (SMC) technique to further reduce the torque ripples and improve the antidisturbance ability of the servo system. ILC is employed to reduce the periodic torque ripples and the SMC is used to guarantee fast response and strong robustness. An adaptive algorithm is utilized to estimate the system lumped disturbances, including parameter variations and external disturbances. The estimated value is utilized to compensate the robust ILC speed controller in order to eliminate the effects of the disturbance, and it can suppress the sliding mode chattering phenomenon simultaneously. Experiments were carried out on a digital signal processor-field programmable gate array based platform. The obtained experimental results demonstrate that the robust ILC scheme has an improved performance with minimized torque ripples and it exhibits a satisfactory antidisturbance performance compared to the PI-ILC method.

Generalization of combination‐combination synchronization of n‐dimensional time‐delay chaotic system via robust adaptive sliding mode control

In this manuscript, the methodology to achieve combination‐combination synchronization of time‐delay chaotic system via robust adaptive sliding mode control is introduced. The methodology is implemented by taking identical multiple time‐delay Lorenz chaotic system. By using rigorous mathematical theory, the sufficient condition is drawn for the stability of error dynamics based on Lyapunov stability theory. Theoretical results are supported with the numerical simulations.

Adaptive robust control of fractional‐order swarm systems in the presence of model uncertainties and external disturbances

This study investigates the asymptotic swarm stabilisation of fractional-order swarm systems in the presence of two different kinds of model uncertainties and external disturbances while the upper bound of the uncertainties is a linear function of pseudo-states norms with unknown coefficients. To this end, first a fractional-integral sliding manifold is constructed and then an adaptive-robust sliding mode controller is designed to guarantee the asymptotic swarm stability in a fractional-order linear time-invariant swarm system. The stability analysis of the proposed control system is done based on the Lyapunov stability theorem. Using the proposed controller, the coefficients of the upper bound function of the uncertainties are estimated by adaptive laws. Simulation results are provided to show the effectiveness of the proposed controller.

Quadcopter Robust Adaptive Second Order Sliding Mode Control Based on PID Sliding Surface

We present a robust adaptive second-order sliding mode controller that rejects external disturbances and uncertainties to improve the tracking performance of attitude and altitude in a quadcopter based on a Proportional–Integral–Derivative sliding surface. The algorithm provides a rapid adaptation and strict robustness of the flight control for the vehicle under the effect of perturbations. The proposed controller design is based on the theory of second order sliding mode technique that eliminates the chattering phenomenon present in first-order sliding mode controllers. In addition, we derive an adaptive law from the Lyapunov stability to ensure the robust control for the quadcopter even without knowing the upper bound for disturbances. Applying the same external disturbances, we use a numerical simulation to compare our algorithm to recent alternatives, such as normal adaptive sliding mode control, super-twisting sliding mode control, modified super-twisting sliding mode control, and nonsingular terminal sliding mode control. The results demonstrate the effectiveness of our proposed algorithm.

Robust adaptive state feedback sliding-mode control of memristor-based Chua’s systems with input nonlinearity

This paper deals with the state feedback stabilization of a class of uncertain memristor-based Chua’s circuit systems. By the combination of the characteristics of the memristor, the system model of the uncertain memristive Chua’s circuit with input nonlinearity is built. Under the assumption that the gain reduction parameter of the input nonlinearity is unknown a prior, the robust adaptive sliding-mode control (SMC) schemes are proposed to ensure that the system trajectory of the uncertain memristive Chua’s circuit can be driven into the sliding surface and subsequently sliding into the origin. Finally, the effectiveness of the proposed method is verified via simulation.

Robust Adaptive Sliding Mode Control for Mobile Manipulators

This paper proposes a robust adaptive sliding mode control for the trajectory tracking of a nonholonomic wheeled mobile manipulator in task space coordinate. The proposed algorithm is robust adaptive control strategy where parametric uncertainties and disturbances are compensated by adaptive update technique. The adaptive law is designed based on the Lyapunov method. Simulations results are given to illustrate the effectiveness of the proposed robust adaptive based controller in comparison with a robust sliding mode based controller.

Nonlinear robust adaptive sliding mode control design for miniature unmanned multirotor aerial vehicle

This paper addresses the stability and tracking control problem of miniature unmanned multirotor aerial vehicle (MUMAV) in the presence of bounded uncertainty. The uncertainty may appear from unmodeled dynamics, underactuated property, input disturbance and flying environment. Nonlinear robust adaptive sliding mode control algorithm is designed by using Lyapunov function. Robust adaptation laws are designed to learn and compensate the bounded parametric uncertainty. Lyapunov analysis shows that the proposed algorithms can guarantee asymptotic stability and tracking control property of the linear and angular dynamics of MUMAV system. Compared with other existing control methods, the proposed design is very simple and easy to implement as it does not require multiple design steps, augmented auxiliary signals and exact bound of the uncertainty. Experimental results on a miniature unmanned quadrotor aerial vehicle are presented to illustrate of effectiveness of the proposed design for real-time applications.