Lian-Wang Lee

A hierarchical structure of observer-based adaptive fuzzy-neural controller for MIMO systems

An observer-based adaptive controller developed from a hierarchical fuzzy-neural network (HFNN) is employed to solve the controller time-delay problem for a class of multi-input multi-output (MIMO) non-affine nonlinear systems under the constraint that only system outputs are available for measurement. By using the implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the HFNN adaptive controller are derived. According to the design of the HFNN hierarchical fuzzy-neural network, the observer-based adaptive controller can alleviate the online computation burden. Moreover, the common adaptive controller is utilized to control all the MIMO subsystems. Hence, the number of adjusted parameters of the HFNN can be further reduced. In this paper, we prove that the proposed observer-based adaptive controller can guarantee that all signals involved are bounded and that the outputs of the closed-loop system track asymptotically the desired output trajectories.

Adaptive fuzzy controller with self-tuning fuzzy sliding-mode compensation for position control of an electro-hydraulic displacement-controlled system

The electro-hydraulic displacement-controlled system EHDCS performs specific non-linear and time-varying characteristics such that an exact model-based controller is complicated to be realized and the servo control is difficult to be implemented. In this study, the design method and experimental implementation of an adaptive fuzzy controller with self-tuning fuzzy sliding-mode compensation AFC-STFSMC are proposed which has on-line tuning ability for dealing with the system time-varying and non-linear uncertain behaviours for adjusting the control rule parameters. This control strategy employs the adaptive fuzzy approximation technique to design the equivalent controller of the conventional sliding-mode control SMC. Furthermore, the fuzzy sliding-mode control scheme with self-tuning ability is introduced to compensate the approximation error of the equivalent controller for improving the control performance. The proposed AFC-STFSMC scheme can design the sliding-mode controller with no requirement of the system dynamic model, be free from chattering, be stable tracking control performance, and be robust to uncertainties. Moreover, the stability proof of the proposed scheme using Lyapunov method is presented. The experimental results of the position control and the path control in EHDCS with different strokes and external disturbance forces show that the proposed AFC-STFSMC approach can achieve excellent control performance and robustness with regard to parameter variations and external disturbance.

A Fourier series-based adaptive sliding-mode controller for nonlinear pneumatic servo systems

The behavior of nonlinearity and time-varying cause the pneumatic actuator systems are difficult to be controlled. This paper proposes a Fourier series-based adaptive sliding-mode controller for nonlinear pneumatic servo systems. The Fourier series-based functional approximation technique can approximate an unknown function, thus bypassing the model-based prerequisite. The learning laws for the coefficients of the Fourier-series functions are derived from a Lyapunov function to guarantee the system stability. Consequently, practical experiments on a rodless pneumatic servo system are successfully implemented with different path tracking profiles, which validates the proposed method.

Adaptive Fuzzy Sliding-Mode Control for Variable Displacement Hydraulic Servo System

The variable displacement hydraulic servo system performs specific characteristics on non-linearity and time-varying. An exact model-based controller is difficult to be realized. In this study, the design method and experimental implementation of an adaptive fuzzy sliding-mode controller (AFSMC) are presented, which has on-line learning ability for dealing with the system time-varying and non-linear uncertainty behaviors for adjusting the control rule parameters. The tuning algorithms are derived in the sense of the Lyapunov stability theorem; thus, the stability of the system can be guaranteed. The experimental results show that the AFSMC can perform excellent position control and path control for the variable displacement hydraulic servo system.

Intelligent switching adaptive control for uncertain nonlinear dynamical systems

(a) Structure of the Hopfield dynamic neural network (HDNN); (b) structure of the simple HDNN. A switching adaptive control scheme using a Hopfield-based dynamic neural network (SACHNN) for nonlinear systems with external disturbances is proposed.The IACs limitation of g ? ( x ) e can be solved by simply switching the IAC to the DAC, where ? is a positive desired value.The Hopfield dynamic neural network (HDNN) is used to not only design DAC but also approximate the unknown plant nonlinearities in IAC design. In this paper, we aim at proposing a switching adaptive control scheme using a Hopfield-based dynamic neural network (SACHNN) for nonlinear systems with external disturbances. In our proposed scheme, an auxiliary direct adaptive controller (DAC) ensures the system stability when the indirect adaptive controller (IAC) is failed; that is, g ? ( x ) approaches to zero, where g ? ( x ) is the denominator of an indirect adaptive control law. The IACs limitation of g ? ( x ) e then can be solved by simply switching the IAC to the DAC, where ? is a positive desired value. The Hopfield dynamic neural network (HDNN) is used to not only design DAC but also approximate the unknown plant nonlinearities in IAC design. The designed simple structure of HDNN keeps the tracking performance well and also makes the practical implementation much easier because of the use of less and fixed number of neurons.

The positioning control of an electro-hydraulic variable rotational speed pump-controlled system using adaptive fuzzy controller with self-tuning fuzzy sliding mode compensation

The electro-hydraulic variable rotational speed pump-controlled system (EHVRSPCS) performs specific characteristics on non-linearity and time-varying. An exact model-based controller is difficult to be realized. In this study, the design method and experimental implementation of an adaptive fuzzy controller with on-line self-tuning fuzzy sliding-mode compensation (AFC-STFSMC) are proposed to deal with the system time-varying and non-linear uncertain behaviours by adjusting the control rule parameters. This control strategy employs the adaptive fuzzy approximation technique to design the equivalent controller of the conventional sliding-mode control (SMC). Furthermore, the fuzzy sliding-mode control scheme with self-tuning ability is introduced to compensate the approximation error of the equivalent controller for improving the control performance. The tuning algorithms are derived in the sense of the Lyapunov stability theorem; thus, the stability of the system can be guaranteed. The experimental results show that the AFC-STFSMC can perform excellent positioning control for the EHVRSPCS.

Design and Implementation of a Interval Type-2 Adaptive Fuzzy Controller for a Novel Pneumatic Active Suspension System

In order to reduce vibration and to increase performance for vehicles, a pneumatic muscle (PM) is integrated with an active vehicle suspension system (AVSS) in this paper. The behavior of nonlinearity and time-varying causes the PM-based AVSS (PM-AVSS) is difficult to be controlled. In this paper, therefore, the design and implementation of an interval type-2 adaptive fuzzy controller (IT2AFC) are proposed for PM-VSS for dealing with the system time-varying and nonlinear uncertain behaviors. The proposed controller has on-line tuning ability for adjusting the control rule parameters. The proposed the IT2AFC is implemented in a self-made quarter-car AVSS, and the experimental results show that the built active vehicle system has good performance in vibration reduction.

Thermal dynamic stability of parametrically excited laminated composite plates with temperature-dependent properties.

ABSTRACT Thermal dynamic stability analysis is performed on periodically-loaded laminated composite plates with temperature-dependent properties. The periodic load is taken to be a combination of periodic axial and bending stress. A set of differential equations of Mathieu–Hill type is formed to determine the dynamic instability regions based on Bolotins method. The thermal dynamic instability of laminate plates with respective temperature-dependent and temperature-independent properties is examined. The effects of various parameters on the instability region and dynamic instability index are also discussed. The results show that the temperature-dependent properties have a significant influence on the thermal dynamic behavior of laminated plates.

Hybrid adaptive control based on a Hopfield dynamic neural network for nonlinear dynamical systems

In this paper, we propose a hybrid adaptive control scheme based on Hopfield-based dynamic neural network (HACHNN) for SISO nonlinear systems. An auxiliary direct adaptive controller is proposed to ensure the stability in the time-interval of when an indirect adaptive controller is failed because of ĝ(x)→0. The weights of the Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov theorem, so that the stability of the closed-loop system can be guaranteed, and all signals in the closed-loop system are bounded. The designed structure of the Hopfield-based dynamic neural network maintains the tracking performance of the control scheme, and it also makes the practical implementation much easier.

Velocity Control for a Variable Displacement Hydraulic Servo System Using Adaptive Fuzzy Sliding-Mode Control

Abstract The variable displacement hydraulic servo system performs specific characteristics on nonlinearity and time-varying. An exact model-based controller is difficult to be realized. In this study, the design method and experimental implementation of an adaptive fuzzy sliding-mode controller (AFSMC) are presented, which has on-line learning ability for dealing with the system time-varying and non-linear uncertainty behaviors for adjusting the control rule parameters. The tuning algorithms are derived in the sense of the Lyapunov stability theorem; thus, the stability of the system can be guaranteed. The experimental results show that the AFSMC can perform excellent velocity control for the variable displacement hydraulic servo system.