Tong Shao-cheng

Fuzzy adaptive observer backstepping control for MIMO nonlinear systems

In this paper, a fuzzy adaptive backstepping output feedback control approach is developed for a class of multi-input and multioutput (MIMO) nonlinear systems with unmeasured states. Using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive observer is designed for state estimation aswell as system identification. Combiningwith the backstepping design techniques, a fuzzy adaptive output feedback control is constructed recursively. It is proved that the proposed fuzzy adaptive control approach can guarantee the semi-global uniform ultimate boundedness for all the signals and the tracking error to a small neighborhood of the origin. Simulation studies illustrate the effectiveness of the proposed approach.

Fuzzy adaptive output tracking control of nonlinear systems

A stable adaptive fuzzy output tracking control scheme is developed for a single-input-single-output unknown nonlinear system. The main characteristics of the proposed adaptive fuzzy control are: i) it does not need the assumption that all the states of the system are available for full feedback, but introduces a high gain observer to estimate them; ii) it is composed of a robust control term and an equivalence fuzzy control so that it not only ensures the stability of the closed-loop system, but also attenuates the effect of fuzzy approximation error on the tracking error of the system to an arbitrary small level; and iii) it is proved that the designed output feedback adaptive fuzzy control can recover the performance achieved under the state feedback controller.

Fuzzy adaptive output feedback control for MIMO nonlinear systems

In this paper, two observer-based adaptive fuzzy output feedback control schemes are presented for a class of uncertain continuous-time multi-input-multi-output (MIMO) nonlinear dynamics systems whose states are not available. Within these schemes, fuzzy logic systems are employed to approximate the plants unknown nonlinear functions and then the state observer is designed for estimating the states of the plant, upon which a fuzzy adaptive output feedback controller is firstly investigated. In order to overcome the controller singularity problem and relax the requirement of bounding parameter values, a second modified fuzzy adaptive output feedback controller is proposed by using a regularized inverse and a robustifying control term. All parameter adaptive laws and robustifying control terms are derived based on Lyapunov stability analysis, so that convergence to zero of tracking errors and boundedness of all signals in the closed-loop system can be guaranteed. Simulations performed on a two-link robot manipulator illustrate the approach and exhibit its performance.

Global impulsive exponential synchronization of stochastic perturbed chaotic delayed neural networks

In this paper, the global impulsive exponential synchronization problem of a class of chaotic delayed neural networks (DNNs) with stochastic perturbation is studied. Based on the Lyapunov stability theory, stochastic analysis approach and an efficient impulsive delay differential inequality, some new exponential synchronization criteria expressed in the form of the linear matrix inequality (LMI) are derived. The designed impulsive controller not only can globally exponentially stabilize the error dynamics in mean square, but also can control the exponential synchronization rate. Furthermore, to estimate the stable region of the synchronization error dynamics, a novel optimization control algorithm is proposed, which can deal with the minimum problem with two nonlinear terms coexisting in LMIs effectively. Simulation results finally demonstrate the effectiveness of the proposed method.

Fuzzy Adaptive Fault Tolerant Sliding Mode Control for SISO Nonlinear Systems

In this paper, we have developed an adaptive fuzzy fault tolerant control approach for a class of SISO unknown nonlinear systems based on the idea of a corrective control law which activated in the presence of a fault. Online approximators, in the forms of fuzzy logic systems, are used to learn the unknown dynamics and the unknown fault functions on-line and provide the fault tolerant control law. The closed-loop stability of the proposed fault tolerant control scheme is rigorously proved using lyapunov theory and illustrated based on a simulation example.

Robust lag synchronization between two different chaotic systems via dual-stage impulsive control

In this paper, an improved impulsive lag synchronization scheme for different chaotic systems with parametric uncertainties is proposed. Based on the new definition of synchronization with error bound and a novel impulsive control scheme (the so-called dual-stage impulsive control), some new and less conservative sufficient conditions are established to guarantee that the error dynamics can converge to a predetermined level, which is more reasonable and rigorous than the existing results. In particular, some simpler and more convenient conditions are derived by taking the same impulsive distances and control gains. Finally, some numerical simulations for the Lorenz system and the Chen system are given to demonstrate the effectiveness and feasibility of the proposed method.

Adaptive Fuzzy Robust Control for Nonlinear System with Dynamic Uncertainties Based on Backstepping

In this paper, a fuzzy adaptive backstepping design procedure is proposed for a class of nonlinear systems with three types of uncertainties: (i) unknown nonlinear functions; (ii) uncertain nonlinearities; (iii) unmodeled dynamics. The fuzzy logic systems are used to approximate the unknown nonlinear functions, nonlinear damping terms are used to counteract the uncertain nonlinearities. The derived fuzzy adaptive control approach guarantees the global bounded property for all the signals and the states and at the same time, steers the output to a small neighborhood of the origin.

Analysis and design of fuzzy robust observer for uncertain nonlinear systems

This paper addresses the analysis and the design of fuzzy robust observer for a class of uncertain nonlinear systems on the basis of Takagi-Sugeno fuzzy model, which considers both the states unobservable problem and the uncertainty problem of fuzzy model. It is shown that the designed robust observers possess the stability and the state estimation errors converge to zero.

An improved impulsive control approach to nonlinear systems with time-varying delays ∗

A scheme for the impulsive control of nonlinear systems with time-varying delays is investigated in this paper. Based on the Lyapunov-like stability theorem for impulsive functional differential equations (FDEs), some sufficient conditions are presented to guarantee the uniform asymptotic stability of impulsively controlled nonlinear systems with time-varying delays. These conditions are more effective and less conservative than those obtained. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method.

Rough Set Approach for Processing Information Table

To deal with the problem of completing the information table, a new method was studied and proposed. First, define discernible vector and its addition rule by the indiscernible relation in rough set. Second, scan discernible vectors just only one time by the discernible vector addition rule in order to obtain the core attribute set and the important attributes. Then obtain a reduced attribute set by deleting redundant attributes. Finally, according to the dependence relation of condition and decision attributes, select the important breaking points,and complete the information table with the constraints of classification quality. The illustration and experiment results indicate that the method is effective and efficient.