Journal ArticleDOI
Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone
TLDR
An adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete-time systems that contain unknown functions and nonsymmetric dead-zone and can be proved based on the difference Lyapunov function method.Abstract:
In this paper, an adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete-time systems. The controlled systems are in a strict-feedback frame and contain unknown functions and nonsymmetric dead-zone. For this class of systems, the control objective is to design a controller, which not only guarantees the stability of the systems, but achieves the optimal control performance as well. This immediately brings about the difficulties in the controller design. To this end, the fuzzy logic systems are employed to approximate the unknown functions in the systems. Based on the utility functions and the critic designs, and by applying the backsteppping design technique, a reinforcement learning algorithm is used to develop an optimal control signal. The adaptation auxiliary signal for unknown dead-zone parameters is established to compensate for the effect of nonsymmetric dead-zone on the control performance, and the updating laws are obtained based on the gradient descent rule. The stability of the control systems can be proved based on the difference Lyapunov function method. The feasibility of the proposed control approach is further demonstrated via two simulation examples.read more
Citations
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Journal ArticleDOI
Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems
Yan-Jun Liu,Shaocheng Tong +1 more
TL;DR: Two theorems are provided to show that all the signals in the closed-loop system are bounded, the outputs are driven to follow the reference signals and all the states are ensured to remain in the predefined compact sets.
Journal ArticleDOI
Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
Wei He,Yiting Dong +1 more
TL;DR: With the proposed control, the stability of the closed-loop system is achieved via Lyapunov’s stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty.
Journal ArticleDOI
Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints
TL;DR: In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated and it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output.
Journal ArticleDOI
Neural Control of Bimanual Robots With Guaranteed Global Stability and Motion Precision
TL;DR: In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is integrated into the control design and effectiveness of the proposed control design has been shown through experiments carried out on the Baxter Robot.
Journal ArticleDOI
Event-Triggered Fault Detection of Nonlinear Networked Systems
TL;DR: This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme using a polynomial fuzzy fault detection filter to generate a residual signal and detect faults in the system.
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