Bo-Jyun Huang

Robust fuzzy control subject to state variance and passivity constraints for perturbed nonlinear systems with multiplicative noises

The multi-constrained robust fuzzy control problem is investigated in this paper for perturbed continuous-time nonlinear stochastic systems. The nonlinear system considered in this paper is represented by a Takagi-Sugeno fuzzy model with perturbations and state multiplicative noises. The multiple performance constraints considered in this paper include stability, passivity and individual state variance constraints. The Lyapunov stability theory is employed to derive sufficient conditions to achieve the above performance constraints. By solving these sufficient conditions, the contribution of this paper is to develop a parallel distributed compensation based robust fuzzy control approach to satisfy multiple performance constraints for perturbed nonlinear systems with multiplicative noises. At last, a numerical example for the control of perturbed inverted pendulum system is provided to illustrate the applicability and effectiveness of the proposed multi-constrained robust fuzzy control method.

Variance and Passivity Constrained Fuzzy Control for Nonlinear Ship Steering Systems with State Multiplicative Noises

The variance and passivity constrained fuzzy control problem for the nonlinear ship steering systems with state multiplicative noises is investigated. The continuous-time Takagi-Sugeno fuzzy model is used to represent the nonlinear ship steering systems with state multiplicative noises. In order to simultaneously achieve variance, passivity, and stability performances, some sufficient conditions are derived based on the Lyapunov theory. Employing the matrix transformation technique, these sufficient conditions can be expressed in terms of linear matrix inequalities. By solving the corresponding linear matrix inequality conditions, a parallel distributed compensation based fuzzy controller can be obtained to guarantee the stability of the closed-loop nonlinear ship steering systems subject to variance and passivity performance constraints. Finally, a numerical simulation example is provided to illustrate the usefulness and applicability of the proposed multiple performance constrained fuzzy control method.

Fuzzy Stabilization for Nonlinear Discrete Ship Steering Stochastic Systems Subject to State Variance and Passivity Constraints

For nonlinear discrete-time stochastic systems, a fuzzy controller design methodology is developed in this paper subject to state variance constraint and passivity constraint. According to fuzzy model based control technique, the nonlinear discrete-time stochastic systems considered in this paper are represented by the discrete-time Takagi-Sugeno fuzzy models with multiplicative noise. Employing Lyapunov stability theory, upper bound covariance control theory, and passivity theory, some sufficient conditions are derived to find parallel distributed compensation based fuzzy controllers. In order to solve these sufficient conditions, an iterative linear matrix inequality algorithm is applied based on the linear matrix inequality technique. Finally, the fuzzy stabilization problem for nonlinear discrete ship steering stochastic systems is investigated in the numerical example to illustrate the feasibility and validity of proposed fuzzy controller design method.

Multi-constrained fuzzy intelligent control for uncertain discrete systems with complex noises: an application to ship steering systems

ABSTRACT In this paper, a performance constrained fuzzy controller design is investigated for the nonlinear uncertain discrete-time ship steering system with complex noises. The performance constraints considered in this paper include individual state variance constraint and passivity constraint. The nonlinear discrete-time ship steering system considered in this paper is represented by a discrete-time Takagi–Sugeno fuzzy model with perturbations, additional noise and multiplicative noise. According to this complex nonlinear discrete-time stochastic system, a fuzzy controller design approach is investigated based on Lyapunov theory, passivity theory and covariance control theory. Some sufficient conditions are derived to satisfy stability constraint, individual state variance constraint and passivity constraint, simultaneously. These sufficient conditions are constructed as linear matrix inequality forms that can be solved by the convex optimal programming algorithm. Finally, some simulation results are provided to verify the validity and applicability of the proposed fuzzy control approach with multiple performance constraints.

Passive fuzzy controller design with variance constraint for nonlinear synchronous generator systems

A passive fuzzy controller design methodology is developed in this paper to achieve state variance constraint for continuous-time Takagi-Sugeno (T-S) fuzzy models. The proposed fuzzy controller is constructed by the concept of Parallel Distributed Compensation (PDC). Based on the Lyapunov theory, the sufficient conditions are derived to guarantee the stability of the closed-loop system. Besides, the passivity and variance constraints are also considered in the derivations of these sufficient conditions. These sufficient conditions belong to the Linear Matrix Inequality (LMI) forms, which can be solved by the convex optimal programming algorithm. Finally, the feasibility and validity of the proposed method are illustrated with a numerical simulation example.

Performance constrained fuzzy control for discrete nonlinear stochastic systems with multiplicative noises and perturbations

A performance constrained fuzzy controller design problem for discrete-time perturbed nonlinear stochastic systems with multiplicative noises is studied in this paper. The purpose of this approach is to design fuzzy controllers to simultaneously achieve variance and passivity constraints. Some sufficient conditions are derived based on the Lyapunov theory. These sufficient conditions are developed in term of the linear matrix inequality forms, thus they can be solved by the convex optimal programming algorithm. Besides, the proposed fuzzy controllers are constructed by using the technique of parallel distributed compensation. Finally, a numerical simulation example is given to show the effectiveness and applicability of the proposed fuzzy control methodology.

Variance and passivity constrained fuzzy control for continuous perturbed fuzzy systems with multiplicative noises

In this paper, a fuzzy controller design problem for continuous perturbed nonlinear stochastic systems with state multiplicative noises is investigated. In order to simultaneously achieve variance and passivity constrained performances, some sufficient conditions are derived based on the Lyapunov theory. These sufficient conditions are in term of the linear matrix inequality forms that can be solved by the convex optimal programming algorithm. Besides, the parallel distributed compensation concept is employed to construct the fuzzy controllers. Finally, a numerical simulation example is given to show the effectiveness and applicability of the proposed fuzzy control methodology.

Intelligent Fuzzy Control with Multiple Constraints for a Model Car System with Multiplicative Disturbance

A multiple constrained fuzzy controller design methodology is developed in this paper to achieve state variance constraint and passivity constraint for a model car system. The model car system considered in this paper is represented by a discrete-time Takagi-Sugeno fuzzy model with multiplicative disturbance. The proposed fuzzy controller is accomplished by using the technique of parallel distributed compensation. Based on the parallel distributed compensation technique, the sufficient conditions are derived to achieve variance constraint, passivity constraint and stability performance constraint, simultaneously. By solving these sufficient conditions with linear matrix inequality technique, the multiple constrained fuzzy controllers can be obtained for the model car system. At last, a numerical simulation example is provided to illustrate the feasibility and validity of the proposed fuzzy control method.

Passive and individual variance constrained fuzzy control for discrete-time fuzzy systems with multiplicative noise

This paper studies the passive and individual variance constrained fuzzy control problem for discrete-time stochastic Takagi-Sugeno fuzzy systems. The performances considered in this paper include passivity constraint and individual state variance constraint. The proposed fuzzy control is accomplished by using the concept of parallel distributed compensation. Based on the Lyapunov stability theory, the sufficient conditions are derived to achieve the above multiple performance constraints. In order to illustrate the applicability and validity of the proposed fuzzy control method, a numerical control simulation for a discrete-time Takagi-Sugeno fuzzy system with multiplicative noise is provided.

Imperfect Premise Matching fuzzy control for nonlinear stochastic ship steering systems

This paper deals with the control problem of nonlinear stochastic ship steering system. The system exhibits nonlinear interaction on three degrees of freedom (surge, sway and yaw) by means of main propellers aft of the ship. For guaranteeing the global stability, the Takagi-Sugeno (T-S) fuzzy model is employed to represent nonlinear ship steering system. Using the technique of Imperfect Premise Matching (IPM), the fuzzy controller is designed without limitation of sharing the same membership function of the fuzzy model. In other words, the IPM technique provides a generalization in designing the fuzzy controller. Besides, the fuzzy controller design can be enhanced more flexibility and robustness than one applies Parallel Distributed Compensation (PDC) approach. Based on the Lyapunov theory, the stability conditions are derived into Linear Matrix Inequality (LMI) problems for applying the convex optimal algorithm. At last, simulation results are given to show the effectiveness of the proposed design method.