Control of a Hydraulic Generator Regulating System Using Chebyshev-Neural-Network-Based Non-Singular Fast Terminal Sliding Mode Method

Abstract

A hydraulic generator regulating system with electrical, mechanical, and hydraulic constitution is a complex nonlinear system, which is analyzed in this research. In the present study, the dynamical behavior of this system is investigated. Afterward, the input/output feedback linearization theory is exerted to derive the controllable model of the system. Then, the chaotic behavior of the system is controlled using a robust controller that uses a Chebyshev neural network as a disturbance observer in combination with a non-singular robust terminal sliding mode control method. Moreover, the convergence of the system response to the desired output in the presence of uncertainty and unexpected disturbances is demonstrated through the Lyapunov stability theorem. Finally, the effectiveness and appropriate performance of the proposed control scheme in terms of robustness against uncertainty and unexpected disturbances are demonstrated through numerical simulations.

A New Fuzzy Reinforcement Learning Method for Effective Chemotherapy

“…The use of state-of-the-art automatic control methods can help to improve the effectiveness and efficiency of various processes and systems, leading to better outcomes and more sustainable solutions [58][59][60][61][62][63][64][65][66][67]. Among the stated control strategies, intelligent controllers have significant advantages [68][69][70][71][72][73].…”