Valve-controlled asymmetric cylinder is widely used in servo loading system. As a kind of typical electro-hydraulic servo system (EHSS), it inherently has the characteristics such as high order nonlinear, strong coupling, and uncertain, therefore, conventional control strategy is difficult to satisfy the requirements of high-performance control. In this paper, a novel linear active disturbance rejection control (LADRC) method was proposed, in which the internal and external disturbances were actively estimated by the third-order linear extended state observer (LESO) in real-time, and rejected by the control law of proportional integral control (PID) with acceleration feed-forward. The stability of the proposed method was proved, and the influence rules of the LADRC parameters on the control performance were revealed by simulation. Finally, comparative experiments between LADRC and PID control were carried out, results showed that the disturbances can be effectively compensated and the control goals can be successfully achieved with the proposed method.
At present, the theory and application of fractional-order neural networks remain in the exploratory stage. We study the asymptotic stability of fractional-order neural networks with Riemann-Liouville (R-L) derivatives. For non-delayed and delayed systems, we propose an asymptotic stability criterion based on the combination of the Lyapunov method and linear matrix inequality (LMI) method. The highlights include the following: (1) for fractional-order neural networks with time delay, the existence and uniqueness of solutions are proven by using matrix analysis theory and contraction mapping theorem, and (2) based on the unique solution, a suitable Lyapunov functional is constructed. Based on the inequality theorem and LMI method, two sets of asymptotic stability criteria for fractional-order neural networks are proven, which avoids the difficulty of solving the fractional derivative by the Leibniz law. Finally, the results are verified using numerical simulations.INDEX TERMS fractional-order neural networks, time delay, asymptotic stability, Lyapunov, LMI
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