Adaptive finite‐time control for output regulation of nonlinear systems with completely unknown control directions

Abstract

This article studies the finite‐time output regulation problem for nonlinear strict‐feedback systems with completely unknown control directions and unknown functions. First, according to the necessary conditions for the solvability of the output regulation problem, the output regulation problem of nonlinear strict‐feedback systems and the external system is transformed into a stabilization problem of nonlinear systems. Second, an internal model with external signals is designed. Third, based on finite time, fuzzy control, output feedback control, and Nussbaum gain functions, the control law is designed so that all signals of the closed‐loop system are the semi‐global practically finite‐time stable (SGPFS), and the tracking error converges to a small neighborhood of the origin in a finite‐time. Finally, the proposed algorithm is applied to the finite‐time tracking problem of Chua's oscillator system.

An Intelligent Model Predictive Control Strategy for Stable Solar-Wind Renewable Power Dispatch Coupled with Hydrogen Electrolyzer and Battery Energy Storage

“…To test the model's accuracy, various experiments were The IMPC is able to significantly reduce the PV irregularities and provide stabilized power to the integrated grid, as demonstrated in Figure 10. Constraint (18) is also observed in Figure 10, where the firmed power is greater than zero. In addition, Figure 11 shows a comparison of battery SoC between the IMPC, FLC, and without any control.…”

“…For the collected data, 70% was supplied for NN training, 15% to test network generality, the rest 15% to test the network performance. To minimize the function J (18), the difference between a MA filter's firmed reference solar-wind power y r and the neural network's output power y m , Quasi-Newton (QN) optimization ( 13) is used. Thus, the control signal v, value of hydrogen electrolyzer time constant T HE , is generated by the coupled action of the QN algorithm, NN model, and the constraints to effectively regulate the HE-FC production for stable wind-solar power supply plus BESS operation enhancement.…”

“…Literature Review. The low pass filter (LPF) is the most commonly used control filtering method to smooth down the fluctuating components of the power supplied into the grid, a first-order low pass filter was used in [18]. The time constant of the filter has a direct impact on its performance; higher values reduce power fluctuation and result in a stable output power [19].…”