Trajectory tracking for an autonomous airship using fuzzy adaptive sliding mode control

In this paper, an adaptive fuzzy sliding mode control approach which combines fuzzy control with the sliding mode control, is applied for the tracking control of pneumatic muscle actuator (PMA). This actuator is widely used in the rehabilitation robots while hard to control due to its strong nonlinearity. The switching-type control term in the sliding mode control law is approximated by a fuzzy system. Based on Lyapunov theory, the stability of the closed-loop PMA system can be guaranteed. Both simulation and experimental studies were carried out to verify the proposed methods. The experiments were conducted on a real PMA system, which was connected with the xPC target system. The results demonstrate the validity of the PMA as well as the effectiveness of the control algorithm even there exist system uncertainties and external bounded disturbances.

This paper presents an application of adaptive fuzzy sliding mode control (AFSMC) for a benchmark problem on a seismically excited highway bridge. This approach combines great advantages of sliding mode control (SMC), adaptive control and fuzzy control without compromising the stability or robustness. Switching-type control law of the conventional SMC and uncertainty part of the equivalent control is approximated by fuzzy controller to attenuate the chattering phenomenon of SMC and harmful effects caused by uncertainties. In order to reduce the complexity of the fuzzy rule bases, adaptive law based on Lyapunov function and Barbalat lemma is designed. AFSMC is also integrated with clipped-optimal strategy to illustrate its efficiency for semi-active MR dampers. Robustness of the AFSMC controller has been demonstrated analyzing ±30% perturbation in the stiffness of the bridge. Numerical results indicate that the response quantities of the highway bridge can be reduced to a satisfactory level superior to those of the sample LQG and LQG/clipped-optimal algorithms. Copyright © 2009 John Wiley & Sons, Ltd.

This paper is concerned with the issue of adaptive fuzzy sliding mode control for fuzzy underactuated translational oscillator with rotating actuator (TORA). Since the system considered in this paper is accompanied by strong inputs coupling and nonlinear characteristics, the feedback decoupling and fuzzy technique are introduced to establish a linear fuzzy underactuated TORA system. The two-order fuzzy sliding surface with adaptive parameters is designed, where the adaptive control is introduced to adjust the parameters of sliding surface such that the system performance could be improved. The sliding mode control law is synthesized to guarantee the sliding mode dynamics to be asymptotically stable and robust, and the reachability analysis is presented to ensure that the system trajectories can be driven onto the sliding mode surface and remain there for the subsequent time. Simulation results are provided to compare with the existing results and validate the effectiveness and feasibility of the proposed scheme.

A reduced-order approach to the adaptive fuzzy sliding mode control of the constrained manipulator is proposed. Based on the Udwadia–Kalaba motion constraint equation, the dynamic equation of the constrained manipulator with both ideal and non-ideal constraints is obtained. Considering the uncertainty of the terminal non-ideal constrained force and the chattering phenomenon of sliding mode control, the adaptive fuzzy and the sliding mode control method are combined to control the constrained manipulator. Because the system is constrained, the model order reduction method is innovatively used in the control algorithm. The stability of the system is proved by Lyapunov theorem. For demonstrating the effectiveness of the control algorithm, the 2-degree-of-freedom manipulator is taken as the research object. Finally, the high-precision control of the manipulator is achieved and the chattering phenomenon caused by the sliding mode control is weakened.

This article proposed a novel control approach for trajectory tracking of robotic airships. First, the nonlinear dynamics model of a robotic airship is derived and formulated. Second, a sliding mode controller is designed to track the commanded trajectory for its robustness against parametric variations and external disturbances. To address the chattering problem results from the sliding mode controller, a radial basis function neural network is adopted to construct the neural network self-gain-scheduling sliding mode controller in which the control gains are scheduled synchronously with the sliding surface via radial basis function neural network according to the learning algorithm, with switching sliding surface and its differential as radial basis function neural network inputs and control gains as radial basis function neural network outputs. In addition, the stability and convergence of the closed-loop controller are proven using the Lyapunov stability theorem. Finally, the effectiveness and robustness of the proposed controller are demonstrated via numerical simulations. Contrasting simulation results indicate that the neural network self-gain-scheduling sliding mode controller attenuates the chattering effectively and has better performance against the sliding mode controller.

Sliding mode control has been shown to be a robust and effective control approach for stabilization of nonlinear systems. However the dynamic performance of the controller is a complex function of the system parameters, which is often uncertain or partially known. This paper presents an adaptive fuzzy sliding mode control for a class of underactuated nonlinear mechanical systems. An adaptive fuzzy system is used to approximate the uncertain parts of the underactuated system. The adaptive law is designed based on the Lyapunov method. The proof for the stability and the convergence of the system is presented. Robust performance of the adaptive fuzzy sliding mode control is illustrated using a gantry crane system. Simulation results demonstrate that the system output can track the reference signal in the presence of modelling uncertainties, external disturbances and parameter variation.

Observer-based indirect adaptive fuzzy sliding mode control with state variable filters for unknown nonlinear dynamical systems

The increasing use of nonlinear loads entails serious electrical energy quality problems in power grids. Active power filter could lead to significant improvement in harmonic compensation performance. In this paper, an adaptive second-order sliding mode fuzzy control based on linear feedback strategy for a three-phase active power filter is proposed to eliminate harmonics. This scheme is constituted by adaptive controller, second-order sliding mode controller, fuzzy controller, and linear feedback controller. The linear feedback controller can compensate the system’s nonlinearities and make the new system have a linear behavior between the input and output. Moreover, the second-order sliding mode controller could transfer the discontinuities in the controller and reduce system chattering. Simulation results illustrate the proposed controller has better performance compared with fuzzy control and adaptive sliding mode fuzzy control.

In this paper, an adaptive fuzzy sliding-mode control (AFSMC) system is investigated for an islanded inverter to achieve a high-performance power supply. A sliding mode control (SMC) law is designed initially to obtain both the voltage tracking error and the current tracking error of the inverter involved, to realize both the output-voltage regulation and the current protection with global stability. Moreover, to deal with uncertainties in the practical inverter system without the chattering phenomenon, an adaptive fuzzy system embedded with a self-adjustive translation width is developed to replace the switch term of the SMC. In addition, the adaptation laws, derived from the Lyapunov stability theorem, adjust the AFSMC parameters online to guarantee optimal and robust performance. Furthermore, the superior control performance of the proposed AFSMC is verified by a numerical simulation in MATLAB, producing experimental results on the prototype in comparison with the conventional SMC.

In this paper, the proposed observer-based robust adaptive fuzzy sliding mode control scheme can deal with the problem of robust stability for a class of uncertain multi-input multi-output (MIMO) nonlinear systems whose states are not available. The fuzzy basis function is used to approximate an unknown nonlinear function according to some adaptive laws, and then the state observer is designed for estimating the states of the plant, upon which a adaptive fuzzy sliding mode controller is investigated. Based on the Lyapunov stability theorem and the theory of variable structure control, the proposed robust adaptive fuzzy sliding mode control scheme can guarantee the robust stability of the whole closed-loop system and obtain good tracking performance. Finally, the simulation results are carried out on a two-link robot manipulator to illustrate the performance of the proposed approach.

In this paper, an equivalent multi‐body dynamics model of high‐speed train with uncertain parameter is established. Based on this detailed model and neural network minimum parameter learning algorithm, an adaptive sliding mode control method is designed for speed and position tracking control of high‐speed train, and the stability of the proposed control method is proved strictly. Then an adaptive fuzzy sliding mode control method (AFSMC) is presented to avoid chattering caused by excessive robust switching gain. Finally, numerical tests are carried out on the proposed AFSMC under different scenarios and operation strategies, and model reference adaptive control (MRAC), PID control and sliding mode control are also tested under the same condition for performance comparison. The results show that the proposed control method is superior to the currently used method. More specifically, compared with MRAC and PID, the average speed tracking accuracy of AFSMC is improved by 0.25 km/h and 0.18 km/h, and the average position tracking accuracy is improved by 0.68 m and 0.7 m, respectively.

A new adaptive fuzzy sliding mode control scheme is proposed, which combines the fuzzy logic with sliding-mode control together using fuzzy logic system to approximate the difference of the equivalent control between the nominal and the real system. Lyapunov stability theorem is used to prove the stability of the system and the adaptive laws are deduced which possess the characteristics of sliding-mode control. In addition, for better control performance, the upper bound of the uncertainty is estimated. Finally, the correctness and effectiveness of the method have been verified by simulation using nonlinear overload model of pitch channel of some BTT missile.

An indirect adaptive fuzzy sliding mode controller is presented for trajectory tracking control of uncertain multi-link robots. This approach combines adaptive fuzzy control and global fast sliding mode control, and adopts a global fast sliding mode manifold which can drive system to reach balance point in finite time. Three fuzzy systems are designed to learn the uncertain robot parameters and external disturbances. So the control system can automatically track the robot parameters and disturbances, and reduces chattering of the controller. Prediction estimation for robot parameters and disturbances is not needed too. The control speed is quickened by simplifying the robot structure and decreasing learning parameters of the uncertain robot. Moreover, the system stability is proved by Lyapunov principle. Simulation results verify the validity of the control scheme.

This paper presents an adaptive Fuzzy Sliding Mode Control approach for an Assist-as-Needed (AAN) strategy to achieve effective human–exoskeleton synergy. The proposed strategy employs an adaptive instance-based learning algorithm to estimate muscle effort, based on surface Electromyography (sEMG) signals. To determine and control the inverse dynamics of a highly nonlinear 4-degrees-of-freedom exoskeleton designed for upper-limb therapeutic exercises, a modified Recursive Newton-Euler Algorithm (RNEA) with Sliding Mode Control (SMC) was used. The exoskeleton position error and raw sEMG signal from the bicep’s brachii muscle were used as inputs for a fuzzy inference system to produce an output to adjust the sliding mode control law parameters. The proposed robust control law was simulated using MATLAB-Simulink, and the results showed that it could instantly adjust the necessary support, based on the combined motion of the human–exoskeleton system’s muscle engagement, while keeping the state trajectory errors and input torque bounded within ±5×10−2 rads and ±5 N.m, respectively.

A new adaptive fuzzy sliding-mode control (FSMC) scheme was proposed, which incorporated the fuzzy logic into sliding-mode control using fuzzy logic to approximate the equivalent control and the upper bound of uncertainty which involved the disturbance and approximation error, thus the influence of modeling error was reduced and the gain of sliding-mode control part was more fitting, such that the chattering effects could be alleviated . Besides, inspired by the thought of fully tuned RBF neural networks, a fully tuned fuzzy logic was proposed which enhanced the approximating ability of fuzzy logic system. Lyapunov stability theorem was used to prove the stability of the system and the adaptive laws were deduced. Finally, simulation results of some BTT missile were included to illustrate the effectiveness of the adaptive fuzzy sliding-mode control scheme.