State-space adaptive control for nonlinear systems

2011 ◽  
pp. 175-192 ◽  
Author(s):  
K. Janiszowski ◽  
M. Olszewski
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
State Space

State Space Adaptive Control for Nonlinear Systems

1994 ◽  
Vol 27 (9) ◽  
pp. 145-148
Author(s):  
K. Janiszowski ◽  
M. Olszewski
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
State Space

Adaptive-state feedback control for Lipschitz nonlinear systems in reciprocal-state space: Design and experimental results

2018 ◽  
Vol 233 (2) ◽  
pp. 144-152
Author(s):  
Assem Thabet
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
State Space ◽  
Digital Signal ◽  
State Feedback Control ◽  
Control Approach ◽  
Space Design ◽  
Lipschitz Nonlinear Systems ◽  
Control Designs ◽  
Processing Device

This article proposes a design of an adaptive control for nonlinear systems which satisfy the Lipschitz condition. The objective is the use of the new reformulation of reciprocal-state space form in adaptive control designs. The presented controller is composed of a state-derivative feedback approach with adaptive gains based on the Lyapunov stability theorem. The first control approach deals with the case of system stabilization. The second is an extension to the tracking problem. High performances are shown through real-time implementation with digital signal processing device (ARDUINO UNO R3 and MEGA 2560).

Adaptive control based on Barrier Lyapunov function for a class of full-state constrained stochastic nonlinear systems with dead-zone and unmodeled dynamics

2021 ◽  
pp. 014233122098563
Author(s):  
Fei Shen ◽  
Xinjun Wang ◽  
Xinghui Yin
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Lyapunov Function ◽  
Dead Zone ◽  
Small Neighborhood ◽  
Unmodeled Dynamics ◽  
Stochastic Nonlinear Systems ◽  
Dynamic Surface ◽  
Barrier Lyapunov Function ◽  
Full State

This paper investigates the problem of adaptive control based on Barrier Lyapunov function for a class of full-state constrained stochastic nonlinear systems with dead-zone and unmodeled dynamics. To stabilize such a system, a dynamic signal is introduced to dominate unmodeled dynamics and an assistant signal is constructed to compensate for the effect of the dead zone. Dynamic surface control is used to solve the “complexity explosion” problem in traditional backstepping design. Two cases of symmetric and asymmetric Barrier Lyapunov functions are discussed respectively in this paper. The proposed Barrier Lyapunov function based on backstepping method can ensure that the output tracking error converges in the small neighborhood of the origin. This control scheme can ensure that semi-globally uniformly ultimately boundedness of all signals in the closed-loop system. Two simulation cases are proposed to verify the effectiveness of the theoretical method.

Real-Time Modular Deep Neural Network-Based Adaptive Control of Nonlinear Systems

2021 ◽  
pp. 1-1
Author(s):  
Duc M. Le ◽  
Max L. Greene ◽  
Wanjiku A. Makumi ◽  
Warren E. Dixon
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Real Time ◽  
Deep Neural Network
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