Adaptive Fuzzy Control of Stochastic Nonlinear Systems With Fuzzy Dead Zones and Unmodeled Dynamics

Adaptive Prescribed Performance Control of A Flexible-Joint Robotic Manipulator With Dynamic Uncertainties

An adaptive fuzzy control strategy is proposed for a single-link flexible-joint robotic manipulator (SFRM) with prescribed performance, in which the unknown nonlinearity is identified by adopting the fuzzy-logic system. By designing a performance function, the transient performance of the control system is guaranteed. To stabilize the SFRM, a dynamic signal is applied to handle the unmodeled dynamics. To cut down the communication load of the channel, the event-triggered control law is developed based on the switching threshold strategy. The Lyapunov stability theory and backstepping technique are applied coordinately to design the control strategy. The semiglobally ultimately uniformly boundedness can be ensured for all signals in the closed-loop system. The designed control method can also guarantee that the tracking error can converge to a small neighborhood of zero within the prescribed performance boundaries. At the end of the article, two illustrative examples are shown to validate the designed event-triggered controller.

Robust adaptive control for uncertain nonlinear systems with odd rational powers, unmodeled dynamics, and non-triangular structure

Numerous approaches have been reported for different control problems of low-order triangular nonlinear systems. Nevertheless, it shows that the dynamic models of some practical plants are high-order nonlinear systems with odd rational powers. Besides, these systems constantly possess a non-triangular form and suffer from the impact of unmodeled dynamics. The related studies of such systems are very few and more challenging. This work concentrates on adaptive control issue of high-order nonlinear system with odd rational powers, unmodeled dynamics, and non-triangular structure. Based on the small-gain theorem, and by employing the adaptive technique, adding a power integrator method, and neural network method, we successfully construct a new adaptive controller, which greatly decreases the use of parameter estimations in the adaptive control of the considered system. An example highlights that the strategy regulates the studied systems well and performs satisfied system responses.

Asynchronous Sampled-Data Filtering Design for Fuzzy-Affine-Model-Based Stochastic Nonlinear Systems

This article studies the asynchronous sampled-data filtering design problem for Itô stochastic nonlinear systems via Takagi-Sugeno fuzzy-affine models. The sample-and-hold behavior of the measurement output is described by an input delay method. Based on a novel piecewise quadratic Lyapunov-Krasovskii functional, some new results on the asynchronous sampled-data filtering design are proposed through a linearization procedure by using some convexification techniques. Simulation studies are given to illustrate the effectiveness of the proposed method.