Design of fractional robust adaptive intelligent controller for uncertain fractional-order chaotic systems based on active control technique

In this paper, a robust fractional-order adaptive intelligent controller is proposed for stabilization of uncertain fractional-order chaotic systems. The intelligent neuro-fuzzy network is used to estimate unknown dynamics of system, while the neuro-fuzzy network parameters as well as the upper bounds of the model uncertainties, disturbances and approximation errors are adaptively estimated via separate adaptive rules. An SMC scheme, with a fractional-order sliding surface, is employed, as the controller to improve the velocity and performance of the proposed control system and to eliminate the unknown but bounded uncertainties, external disturbances and approximation errors. The Lyapunov stability theorem has been also employed to show the stability of the closed-loop system, robustness against uncertainties, external disturbances and approximation errors, while the control signal remains bounded. Explanatory examples and simulation results are given to confirm the effectiveness of the proposed procedure, which consent well with the analytical results.     

Fuzzy adaptive output feedback control for robotic systems based on fuzzy adaptive observer

In this paper, a fuzzy adaptive output feedback control scheme based on fuzzy adaptive observer is proposed to control robotic systems with parameter uncertainties and external disturbances. It is supposed that only the joint positions of the robotic system can be measured, whereas the joint velocities are unknown and unmeasured. First, a fuzzy adaptive nonlinear observer is presented to estimate the joint velocities of robotic systems, and the observation errors are analyzed using strictly positive real approach and Lyapunov stability theory. Next, based on the observed joint velocities, a fuzzy adaptive output feedback controller is developed to guarantee stability of closed-loop system and achieve a certain tracking performance. Based on the Lyapunov stability theorem, it is proved that all the signals in closed-loop system are bounded. Finally, simulation examples on a two-link robotic manipulator are presented to show the efficiency of the proposed method.     

Robust chaos synchronization of fractional-order chaotic systems with unknown parameters and uncertain perturbations

Chaotic systems in practice are always influenced by some uncertainties and external disturbances. This paper investigates the problem of practical synchronization of fractional-order chaotic systems. Based on Lyapunov stability theory and a fractional-order differential inequality, a modified adaptive control scheme and adaptive laws of parameters are developed to robustly synchronize coupled fractional-order chaotic systems with unknown parameters and uncertain perturbations. This synchronization approach is simple, global and theoretically rigorous. Simulation results for two fractional-order chaotic systems are provided to illustrate the effectiveness of the proposed scheme.     

Finite-time stabilization of uncertain non-autonomous chaotic gyroscopes with nonlinear inputs

Gyroscopes are one of the most interesting and everlasting nonlinear nonautonomous dynamical systems that exhibit very complex dynamical behavior such as chaos.In this paper,the problem of robust stabi...     

Adaptive interval type-2 fuzzy sliding mode control for unknown chaotic system

In this paper, a novel adaptive interval type-2 fuzzy sliding mode control (AIT2FSMC) methodology is proposed based on the integration of sliding mode control and adaptive interval type-2 fuzzy control for chaotic system. The AIT2FSMC system is comprised of a fuzzy control design and a hitting control design. In the fuzzy control design, an interval type-2 fuzzy controller is designed to mimic a feedback linearization (FL) control law. In the hitting control design, a hitting controller is designed to compensate the approximation error between the FL control law and the interval type-2 fuzzy controller. The parameters of the interval type-2 fuzzy controller, as well as the uncertainty bound of the approximation error, are tuned adaptively. The adaptive laws are derived in the sense of Lyapunov stability theorem, thus the stability of the system can be guaranteed. The proposed control system compared to adaptive fuzzy sliding mode control (AFSMC). Simulation results show that the proposed control systems can achieve favorable performance and robust with respect to system uncertainties and external disturbances.     

Adaptive control of rotary inverted pendulum system with time-varying uncertainties

In this paper, an adaptive controller is proposed to balance a rotary inverted pendulum with time-varying uncertainties. The goal of the control is to bring the pendulum close to the upright position regardless of the various uncertainties and disturbances. Its underactuated dynamics is first decoupled by Olfati’s transformation into a cascade form, and then an adaptive controller is designed to deal with the uncertainties in the new space. Based on the Lyapunov-like theory, the closed loop stability and boundedness of all internal signals can be proved. The simulation results show that the proposed scheme is capable of giving good performance, as desired.     

Practical adaptive synchronization of a class of uncertain chaotic systems

This paper studies the practical adaptive synchronization of a class of uncertain chaotic systems in the drive-response framework. An adaptive response system is designed to practically synchronize a given drive chaotic system with uncertainties. An improved adaptation law on the upper bound of uncertainties is proposed to guarantee the boundedness of both the synchronization error and the estimated feedback coupling gains. The efficiency and effectiveness of the proposed approach is illustrated by computer simulation.     

Robust synchronization of Rossler systems with mismatched time-varying parameters

This paper presents robust synchronization algorithms for the Rossler systems in the presence of unknown time-varying parameters. First, an adaptive synchronization algorithm based on the Lyapunov theory is introduced for identical Rossler systems with mismatched uncertainties. This method does not require a priori information regarding the bound of uncertainties. In addition, this technique is such that the states of the synchronization error system are uniformly ultimately bounded. Since in practice the parameters of the drive and response systems are not necessarily the same, two synchronization approaches are used for the drive and response systems with different parameters. In the first approach, a simple controller is designed for the nominal error system, as if there is no uncertainty in the system. The stability analysis is then investigated as the uncertainties are reintroduced, and it is shown that the size of the uncertainties directly affects the synchronization performance. To deal with this problem, an H _∞ controller is designed in which the effects of unknown bounded uncertainties can be attenuated at an appropriate level. It is shown that, using these two approaches, the Rossler systems can be synchronized effectively and the synchronization error is uniformly ultimately bounded. Numerical simulations confirm the effectiveness of the proposed methods.     

Active disturbance rejection adaptive control of uncertain nonlinear systems: theory and application

This paper proposes an active disturbance rejection adaptive controller for tracking control of a class of uncertain nonlinear systems with consideration of both parametric uncertainties and uncertain nonlinearities by effectively integrating adaptive control with extended state observer via backstepping method. Parametric uncertainties are handled by the synthesized adaptive law and the remaining uncertainties are estimated by extended state observer and then compensated in a feedforward way. Moreover, both matched uncertainties and unmatched uncertainties can be estimated by constructing an extended state observer for each channel of the considered nonlinear plant. Since parametric uncertainties can be reduced by parameter adaptation, the learning burden of extended state observer is much reduced. Consequently, high-gain feedback is avoided and improved tracking performance can be expected. The proposed controller theoretically guarantees a prescribed transient tracking performance and final tracking accuracy in general while achieving asymptotic tracking when the uncertain nonlinearities are not time-variant. The motion control of a motor-driven robot manipulator is investigated as an application example with some suitable modifications and improvements, and comparative simulation results are obtained to verify the high tracking performance nature of the proposed control strategy.     

Decentralized adaptive neural network control for mechanical systems with dead-zone input

We propose a decentralized adaptive robust controller for trajectory tracking of mechanical systems with dead-zone input in this paper. The considered mechanical systems are with high-order interconnections and unknown non-symmetric nonlinear input. In each local controller, the neural network control is introduced to estimate the uncertainties and disturbances, meanwhile the siding mode control and adaptive technical are designed to compensate for the approximation errors. A nonlinear function is chosen to deal with the interconnections. Following, the stability and robustness are verified by using Lyapunov stability theorem. Finally, simulations are provided to support the theoretical results     

Decentralized sliding mode control of fractional-order large-scale nonlinear systems

This paper presents some novel discussions on fully decentralized and semi-decentralized control of fractional-order large-scale nonlinear systems with two distinctive fractional derivative dynamics. First, two decentralized fractional-order sliding mode controllers with different sliding surfaces are designed. Stability of the closed-loop systems is attained under the assumption that the uncertainties and interconnections among the subsystems are bounded, and the upper bound is known. However, determining the interconnections and uncertainties bound in a large-scale system is troublesome. Therefore in the second step, two different fuzzy systems with adaptive tuning structures are utilized to approximate the interconnections and uncertainties. Since the fuzzy system uses the adjacent subsystem variables as its own input, this strategy is known as semi-decentralized fractional-order sliding mode control. For both fully decentralized and semi-decentralized control schemes, the stability of closed-loop systems has been analyzed depend on the sliding surface dynamics by integer-order or fractional-order stability theorems. Eventually, simulation results are presented to illustrate the effectiveness of the suggested robust controllers.     

Adaptive sliding mode control of dynamic system using RBF neural network

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network (NN) for a class of time varying system in the presence of model uncertainties and external disturbance. Adaptive RBF neural network controller that can learn the unknown upper bound of model uncertainties and external disturbances is incorporated into the adaptive sliding mode control system in the same Lyapunov framework. The proposed adaptive sliding mode controller can on line update the estimates of system dynamics. The asymptotical stability of the closed-loop system, the convergence of the neural network weight-updating process, and the boundedness of the neural network weight estimation errors can be strictly guaranteed. Numerical simulation for a MEMS triaxial angular velocity sensor is investigated to verify the effectiveness of the proposed adaptive RBF sliding mode control scheme.     

Discrete adaptive fuzzy control for asymptotic tracking of robotic manipulators

This paper presents a novel discrete adaptive fuzzy controller for electrically driven robot manipulators. It addresses how to overcome the nonlinearity, uncertainties, discretizing error and approximation error of the fuzzy system for asymptotic tracking control of robotic manipulators. The proposed controller is model-free in the form of discrete Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned using an adaptive mechanism derived by stability analysis. A robust control term is used to compensate the approximation error of the fuzzy system for asymptotic tracking of a desired trajectory. The controller is robust against all uncertainties associated with the robot manipulator and actuators. It is easy to implement since it requires only the joint position feedback. Compared with fuzzy controllers which employ all states to guarantee stability, the proposed controller is very simpler. Stability analysis and simulation results show its efficiency in the tracking control.     

Robust adaptive backstepping synchronization for a class of uncertain chaotic systems using fuzzy disturbance observer

This paper proposes a robust adaptive backstepping synchronization method for a class of uncertain chaotic systems. Unknown factors including system uncertainties and external disturbances are estimated by a fuzzy disturbance observer. By use of the fuzzy disturbance observer, any prior information about the unknown factors is not need. The proposed method using the estimated values guarantees the global synchronization for chaotic systems with mismatched uncertainties in the sense of uniform ultimate boundedness. Finally, numerical examples are presented to show the effectiveness of the method.     

Robust adaptive synchronization of uncertain unified chaotic systems

The problem of synchronizing a unified chaotic system in the presence of parameter variations, unstructured uncertainties, and external disturbances is addressed. To tackle such perturbations whose bounds may be unknown, two robust adaptive algorithms are proposed. The stability analysis is presented based on the Lyapunov stability theorem. Simulation results demonstrate the performance of the developed synchronization schemes.     

空间连续型机器人自适应鲁棒容错控制

针对空间连续型机器人系统三臂节执行器并发故障的问题,提出一种自适应鲁棒容错控制算法.采用非奇异快速终端滑模控制器,并通过自适应RBF(Radial Basis Function)神经网络在线调整控制器的切换项增益,使控制器在模型参数摄动和外部干扰下依旧具有较高的跟踪精度和较强的鲁棒性.基于Lyapunov稳定性理论,证...     

Fuzzy adaptive high-gain-based observer backstepping control for SISO nonlinear systems with dynamical uncertainties

In this paper, a fuzzy adaptive output feedback control approach is developed for a class of SISO strict-feedback nonlinear systems with unmeasured states, unmodeled dynamics, and dynamical disturbances. In the backstepping recursive design, fuzzy logic systems are used to approximate the unknown nonlinear functions, a fuzzy adaptive high-gain observer is designed to estimate the unmeasured states; a dynamic signal is incorporated into the control scheme to dominate the dynamic uncertainties. Using the states estimates and combining the backstepping design technique, a fuzzy adaptive output feedback control is constructed recursively. It is proved that the proposed fuzzy adaptive output feedback control scheme can guarantee the all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SUUB), and the observer and tracking error converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated via an example.     

Adaptive control of a chaotic permanent magnet synchronous motor

This paper proposes a simple adaptive controller design method for a chaotic permanent magnet synchronous motor (PMSM) based on the sliding mode control theory which has given an effective means to design robust controllers for nonlinear systems with bounded uncertainties. The proposed sliding mode adaptive controller does not require any information on the PMSM parameter and load torque values, thus it is insensitive to model parameter and load torque variations. Simulation results are given to verify that the proposed method can be successfully used to control a chaotic PMSM under model parameter and load torque variations.     

System modeling and tracking control of mobile manipulator subjected to dynamic interaction and uncertainty

Trajectory tracking of a mobile manipulator is a challenging research because of its complex nonlinearity and dynamics. This paper presents an adaptive control strategy for trajectory tracking of a mobile manipulator system that consists of a wheeled platform and a modular manipulator. When a robot system moves in the presence of sliding, it is difficult to accurately track its trajectory by applying the backstepping approach, even if we employ a non-ideal kinematic model. To address this problem, we propose using a combination of adaptive fuzzy control and backstepping approach based on a dynamic model. The proposed control scheme considers the dynamic interaction between the platform and manipulator. To accurately track the trajectory, we propose a fuzzy compensator in order to compensate for modeling uncertainties such as friction and external disturbances. Moreover, to reduce approximation errors and ensure system stability, we include a robust term to the adaptive control law. Simulation results obtained by comparing several cases reveal the presence of the dynamic interaction and confirm the robustness of the designed controller. Finally, we demonstrate the effectiveness and merits of the proposed control strategy to counteract the modeling uncertainties and accurately track the trajectory.     

Fuzzy-approximation-based adaptive control of the chaotic permanent magnet synchronous motor

This paper focus on the problem of position tracking control for the chaotic permanent magnet synchronous motor drive system with parameter uncertainties. Fuzzy logic systems are used to approximate the nonlinearities and the adaptive backstepping technique is employed to construct controllers. The proposed adaptive fuzzy controllers guarantee that the tracking error converges to a small neighborhood of the origin. Compared with the conventional backstepping, the designed fuzzy controllers?? structure is very simple. Simulation results show that the proposed control scheme can suppress chaos of PMSM and guarantee the perfect tracking performance even under the unknown parameters.