A dynamic neural network (DNN) observer-based output feedback controller for uncertain nonlinear systems with bounded disturbances is developed. The DNN-based observer works in conjunction with a dynamic filter for state estimation using only output measurements during online operation. A sliding mode term is included in the DNN structure to robustly account for exogenous disturbances and recon...

Though chaotic behaviors are exhibited in many simple nonlinear models, physical chaotic systems are much more complex and contain many types of uncertainties. This paper presents a robust adaptive neural control scheme for a class of uncertain chaotic systems in the disturbed strict-feedback form, with both unknown nonlinearities and uncertain disturbances. To cope with the two types of uncert...

In this paper, a class of uncertain, discrete-time, piecewise linear hybrid systems affected by both parameter variations and exterior disturbances is introduced. The robust tracking and regulation control problem for such uncertain piecewise linear hybrid systems is investigated. The main question is whether there exists a controller such that the closed-loop system exhibits desired behavior u...

An asymptotically stable direct adaptive fuzzy PI sliding modecontroller is proposed for a class of nonlinear uncertain systems. In contrast toother existing approaches of handling disturbances, the proposed approachdoes not require this bound to be known, only requiring that it exists.Moreover, a PI control structure is used to attenuate chattering. The approachis applied to stabilize an open-...

The paper addresses receding-horizon (predictive) control for polytopic discrete-time systems subject to input/state constraints and unknown but bounded disturbances. The objective is to optimize nominal performance while guaranteeing robust stability and constraint satisfaction. The latter goal is achieved by exploiting robust invariant sets under linear and nonlinear control laws. Tradeoffs b...

Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial a nonlinear robotic system to achieve high-performance control. In this paper, we propose novel learning-based scheme that couples high-level model controller (MPFC) with low-level feedback linearization (LB-FBLC) systems under disturbances. Th...

The paper addresses receding-horizon (predictive) control for polytopic discrete-time systems subject to input/state constraints and unknown but bounded disturbances. The objective is to optimize nominal performance while guaranteeing robust stability and constraint satisfaction. The latter goal is achieved by exploiting robust invariant sets under linear and nonlinear control laws. Tradeoffs b...

A novel anti-disturbance strategy for a class of uncertain systems is investigated. Besides the unknown frequency sinusoidal, controlled plant subjected to bounded noise and nonlinear term simultaneously. Through construction auxiliary observer, sinusoidal represented in parametric uncertainty form. Only one scalar needs estimating construct compensation signal, meanwhile unwanted operation bet...