Robust control for a wheeled mobile robot to track a predefined trajectory in the presence of unknown wheel slips

In this paper, a robust controller for a nonholonomic wheeled mobile robot (WMR) is proposed for tracking a predefined trajectory in the presence of unknown wheel slips, bounded external disturbances, and model uncertainties. The whole control system consists of two closed loops. Specifically, the outer one is employed to control the kinematics, and the inner one is used to control the dynamics. The output of kinematic controller is adopted as the input of the inner (dynamic) closed loop. Furthermore, two robust techniques were utilized to assure the robustness. In particular, one is used in the kinematic controller to compensate the harmful effects of the unknown wheel slips, and the other is used in the dynamic controller to overcome the model uncertainties and bounded external disturbances. Thanks to this proposed controller, a desired tracking performance in which tracking errors converge asymptotically to zero is obtained. According to Lyapunov theory and LaSalle extension, the desired tracking performance is guaranteed to be achieved. The results of computer simulation have shown the validity and efficiency of the proposed controller

Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems

A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach

Membership-set estimation using random scanning and principal component analysis

A set-theoretic approach to parameter estimation based on the bounded-error concept is an appropriate choice when incomplete knowledge of observation error statistics and unavoidable structural model error invalidate the presuppositions of stochastic methods. Within this class the estimation of non-linear-in-the-parameters models is examined. This situation frequently occurs in modelling natural systems. The output error method proposed is based on overall random scanning with iterative reduction of the size of the scanned region. In order to overcome the problem of computational inefficiency, which is particularly serious when there is interaction between the parameter estimates, two modifications to the basic method are introduced. The first involves the use of principal component transformations to provide a rotated parameter space in the random scanning because large areas of the initial parameter space are thus excluded from further examination. The second improvement involves the standardization of the parameters so as to obtain an initial space with equal size extension in all directions. This proves to largely increase the computational robustness of the method. The modified algorithm is demonstrated by application to a simple three-parameter model of diurnal dissolved oxygen patterns in a lake