Unknown Control Gain Sign Research Articles

Fuzzy adaptive control of fractional order chaotic systems with unknown control gain sign using a fractional order Nussbaum gain

In this paper we propose an improved fuzzy adaptive control strategy, for a class of nonlinear chaotic fractional order &#x0028 SISO &#x0029 systems with unknown control gain sign. The online control algorithm uses fuzzy logic sets for the identification of the fractional order chaotic system, whereas the lack of a priori knowledge on the control directions is solved by introducing a fractional order Nussbaum gain. Based on Lyapunov stability theorem, stability analysis is performed for the proposed control method for an acceptable synchronization error level. In this work, the Grunwald-Letnikov method is used for numerical approximation of the fractional order systems. A simulation example is given to illustrate the effectiveness of the proposed control scheme.

Adaptive fuzzy control method for a linear switched reluctance motor

A new direct adaptive fuzzy speed control method for a linear switched reluctance motor with unknown control gain sign is presented. The proposed control system consists of two parts: a fuzzy system and an adaptive law. Fuzzy system approximates an unknown ideal control law with unknown adjustable parameters. After determination of the control law, an appropriate adaptation law is designed to update the adjustable parameters to achieve control objectives. The adaptation law is designed using the error between the unknown ideal control law and fuzzy control law. Unlike previous works, in this study, the unknown control gain sign is taken as an unknown constant parameter and is determined online by an appropriate adaptation law. Proposed control method along with a fuzzy sliding mode speed control and a proportional integral speed control strategies are experimentally studied and the performance index is used to evaluate each strategy. The robustness of the proposed method to the parameters and external load force change has been verified via simulation and experiments. The results indicate that the proposed control method has an acceptable performance.

Adaptive prescribed performance control of output feedback systems including input unmodeled dynamics

In this paper, an adaptive prescribed performance tracking control scheme is investigated for a class of output feedback nonlinear systems with input unmodeled dynamics based on dynamic surface control method. A transformation with respect to tracking error is introduced to implement prescribed performance tracking control. A novel description based on Lyapunov function for unmodeled dynamics is presented to deal with state unmodeled dynamics. The negative impact of nonlinear input unmodeled dynamics is offset by using a normalization signal. With the aid of Nussbaum function, the unknown control gain sign is effectively handled in the controller design. By the theoretical analysis, not only the signals in the closed-loop system are proved to be semi-globally uniformly ultimately bounded, but also the transient tracking performance can be guaranteed. Two simulation examples are provided to show the effectiveness of the proposed approach.

A fuzzy adaptive control approach for nonlinear systems with unknown control gain sign

In this paper, a new direct adaptive controller for nonlinear SISO systems with unknown control gain sign is investigated. The controller design is based on the approximation capability of fuzzy systems. A Nussbaum function is incorporated in the adaptation laws to deal with the unknown control direction (i.e. with the unknown control gain sign). The stability of the closed-loop system is proven using a Lyapunov approach. Finally, simulation results are given to verify the feasibility and effectiveness of the proposed controller.

Observer-based indirect adaptive fuzzy control for SISO nonlinear systems with unknown gain sign

This paper develops an observer-based indirect adaptive fuzzy control scheme for a class of single-input single-output (SISO) nonlinear systems. The considered systems have unknown nonlinear functions, unknown control gain sign, and unmeasurable state variables. In this study, fuzzy systems are utilized to approximate unknown nonlinear functions, and a fuzzy adaptive state observer is designed to estimate the unmeasured state. A robust controller is used to compensate for the lumped errors, and the Nussbaum gain function is incorporated in the robust controller to estimate the unknown gain sign. Based on the strictly positive real Lyapunov design approach, the parameter adaptive laws are designed by the fuzzy basis functions rather than by its filtering. It is proved that all the signals in the closed-loop systems are bounded and that the tracking error converges asymptotically to zero. A simulation example is used to demonstrate the effectiveness of the proposed scheme.

On the design of observer-based fuzzy adaptive controller for nonlinear systems with unknown control gain sign

In this paper, an observer-based fuzzy adaptive controller for nonlinear systems with unknown control gain sign is investigated. Because the system states are not available for measurement, a tracking-error observer is constructed. In this controller, the adaptive fuzzy system is used to approximate the unknown nonlinearities and the Nussbaum function is incorporated to deal with the unknown control direction (i.e. with the unknown control gain sign). The stability of the closed-loop system is proven using the strictly positive real (SPR) condition and Lyapunov theory. Finally, simulation results are given to verify the feasibility and effectiveness of the proposed controller.

Adaptive tracking H-infinity control for switched nonlinear systems with unknown control gain sign

This paper addresses the adaptive tracking control scheme for switched nonlinear systems with unknown control gain sign. The approach relaxes the hypothesis that the upper bound of function control gain is known constant and the bounds of external disturbance and approximation errors of neural networks are known. RBF neural networks (NNs) are used to approximate unknown functions and an H-infinity controller is introduced to enhance robustness. The adaptive updating laws and the admissible switching signals have been derived from switched multiple Lyapunov function method. It’s proved that the resulting closed loop system is asymptotically Lyapunov stable such that the output tracking error performance and H-infinity disturbance attenuation level are well obtained. Finally, a simulation example of Forced Duffing systems is given to illustrate the effectiveness of the proposed control scheme and improve significantly the transient performance.

Fuzzy adaptive control for a class of nonlinear systems with unknown control gain

This paper deals with the direct adaptive control using fuzzy systems of a class of uncertain SISO nonlinear systems with unknown control gain sign. Within this scheme, a fuzzy system is used to generate directly the control input signal without dynamic system estimation, and the Nussbaum-type function is used to deal with the unknown control gain sign. At first, we present a direct adaptive control scheme for nonlinear systems with the known control gain sign where the parameter adaptation law is driven by tracking and identification errors. Then, this control scheme is generalized for the case of unknown control gain sign. The stability analysis of the closed-loop system is performed using a Lyapunov approach and simulation results are provided to verify the effectiveness of the proposed design.