Experimental implementation of adaptive-critic based infinite time optimal neurocontrol for a heat diffusion system

Author(s):  
P. Prabhat ◽  
S.N. Balakrishnan ◽  
D.C. Look
1982 ◽  
Vol 104 (2) ◽  
pp. 151-157 ◽  
Author(s):  
M. J. Grimble ◽  
J. Fotakis

The deterministic discrete-time optimal control problem for a finite optimization interval is considered. A solution is obtained in the z-domain by embedding the problem within a equivalent infinite time problem. The optimal controller is time-invariant and may be easily implemented. The controller is related to the solution of the usual infinite time optimal control problem due to Wiener. This new controller should be of value in self-tuning control laws where a finite interval controller is particularly important.


2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Xin Wang

Abstract A discrete-time-coupled state-dependent Riccati equation (CSDRE) control strategy is structured in this paper for synthesizing state feedback controllers satisfying the combined nonlinear quadratic regulator (NLQR) and H∞ robust control performance objectives. Under smoothness assumptions, the nonlinear plant dynamics can be formulated into state-dependent coefficient form through direct parameterization. By solving a pair of coupled state-dependent Riccati equations, the optimal stabilizing solutions can achieve inherent stability, nonlinear quadratic optimality, and H∞ disturbance attenuation performance. The established two-player Nash's game theory is utilized for developing both of the finite and infinite time optimal control laws. Furuta swing-up pendulum, a representative nonholonomic underactuated nonlinear system, is stabilized in real-time for validating the robustness and potential of proposed approach in mechatronics applications.


Author(s):  
Isabel S. Jesus ◽  
J. Tenreiro Machado

In this paper we study the control of a heat diffusion system based on the application of the fractional calculus concepts. In this perspective, several algorithms are investigated and compared, when integrated within a Smith predictor structure. Simulations are presented assessing the performance of the proposed fractional algorithms.


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