This paper presents mean-based fuzzy controllers for trajectory tracking for a class of multiple-input multiple-output robotic systems with nonaffine-like form and parametric uncertainties, in which direct adaptive controllers with state estimators are developed via a mean-based fuzzy identifier without prior knowledge of the membership functions. By using the proposed adaptive technique, unfavorable influence from the initial design of membership functions can be effectively diminished. Moreover, the computation burden of the adaptive laws can be successfully alleviated because the derivative of the fuzzy systems is not required. A Lyapunov-based stability analysis is utilized to guarantee successful system control and desired tracking performance of the closed-loop system. Finally, two examples are provided to demonstrate the feasibility of the proposed control method.

The paper proposes a fuzzy control method with a real-time genetic algorithm for an uncertain DC server motor with a Buck converter. The parameters of the fuzzy system are online adjusted by the real-time genetic algorithm in order to generate appropriate control input. For the purpose of on-line evaluating the stability of the closed-loop system, an energy fitness function derived from backstepping technique is involved in the genetic algorithm. According to the experimental results, the genetic fuzzy control scheme performs on-line tracking successfully.

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

In this paper, a nonlinear parameter fuzzy control scheme is proposed for a class of uncertain systems without all states measurement. In the control scheme, a fuzzy identifier without prior knowledge on membership functions is merged into direct adaptive control by means of a linear state estimator. Since the structure of the fuzzy identifier is nonlinear in the adjusted parameters, the fuzzy identifier uses a mean method to develop adaptive laws. Finally, an example is provided to demonstrate the effectiveness of the proposed control scheme.

This paper proposes a radial basis function neural network adaptive backstepping controller (RBFNN_ABC) for multiple-input multiple-output (MIMO) nonlinear systems in block-triangular form. The control scheme incorporates the adaptive neural backstepping design technique with a first-order filter at each step of the backstepping design to avoid the higher-order derivative problem, which is generated by the backstepping design. This problem may create an unpredictable and unfavorable influence on control performance because higher-order derivative term errors are introduced into the neural approximation model. Finally, simulation results demonstrate that the output tracking error between the plant output and the desired reference can be made arbitrarily small.

In the paper, an adaptive learning approach of integrating evolution fuzzy-neural networks and Q-learning is developed so that a mobile robot can adapt itself to a real and complex environment. Specifically, based on Q-value and an evolution method that adjusts their parameter values of the fuzzy-neural networks, the mobile robot evolves better strategies to adapt to the environment. However, in most studies of evolution learning, the learning of mobile robots often requires a simulator and an enormous amount of evolution time so as to perform a task. Therefore, we are to integrate Q-learning into the evolution fuzzy-neural networks to avoid the requirement of the simulator. Experiment results of a mobile robot illustrate the performance of the proposed approach.

A direct adaptive fuzzy cerebellar model articulation controller (CMAC) is developed for a class of uncertain nonlinear systems. Only the system output measurement is available for feedback. The adjustable weighting factors of the direct adaptive fuzzy CMAC are updated online by an adaptive law. Strictly positive real Lyapunov theory is used to verify the stability of the closed-loop system, and the proposed control scheme guarantees that all signals involved are bounded and the system output asymptotically tracks the desired output. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.

In this paper, an observer-based direct adaptive fuzzy-neural control scheme is presented for nonaffine nonlinear systems in the presence of unknown structure of nonlinearities. A direct adaptive fuzzy-neural controller and a class of generalized nonlinear systems, which are called nonaffine nonlinear systems, are instead of the indirect one and affine nonlinear systems given by Leu et al. By using implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the fuzzy-neural controller are derived for the nonaffine nonlinear systems. Based on strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. Moreover, the overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed-loop system will asymptotically track the desired output trajectory. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.

In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF controller are demonstrated to illustrate the effectiveness of the proposed method.

In this paper, a robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance, and modeling errors. A generalized projection update law, which generalizes the projection algorithm modification and the switching-sigma adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system to the specified regions. Moreover, a variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances, and modeling errors. To demonstrate the effectiveness of the proposed method, several examples are illustrated in this paper.

Based on the H/sub /spl infin// control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach, an adaptive fuzzy-neural observer tuned online for a class of uncertain (unknown) nonlinear systems is developed. Unlike the results of Marino et al. (1992, 1995), the assumption that the uncertain system nonlinearities only are restricted to the system output is not required. Moreover, the adaptive fuzzy-neural observer provides the modeling error (and the external bounded disturbance) attenuation with H/sub /spl infin// performance, obtained by a Riccati-like equation. Finally, simulation results demonstrate that the proposed observer yields satisfactory performance.

A robust adaptive fuzzy-neural controller for a class of unknown nonlinear dynamic systems with external disturbances is proposed. The fuzzy-neural approximator is established to approximate an unknown nonlinear dynamic system in a linearized way. The fuzzy B-spline membership function (BMF) which possesses a fixed number of control points is developed for online tuning. The concept of tuning the adjustable vectors, which include membership functions and weighting factors, is described to derive the update laws of the robust adaptive fuzzy-neural controller. Furthermore, the effect of all the unmodeled dynamics, BMF modeling errors and external disturbances on the tracking error is attenuated by the error compensator which is also constructed by fuzzy-neural inference. We prove that the closed-loop system which is controlled by the robust adaptive fuzzy-neural controller is stable and the tracking error will converge to zero under mild assumptions. Several examples are simulated in order to confirm the effectiveness and applicability of the proposed methods.

In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance.

The adaptive fuzzy-neural controllers tuned online for a class of unknown nonlinear dynamical systems are proposed. To approximate the unknown nonlinear dynamical systems, the fuzzy-neural approximator is established. Furthermore, the control law and update law to tune on-line both the B-spline membership functions and the weighting factors of the adaptive fuzzy-neural controller are derived. Therefore, the control performance of the controller is improved. Several examples are simulated in order to confirm the effectiveness and applicability of the proposed methods in this paper.