Asymmetric Actuator Backlash Compensation in Quantized Adaptive Control of Uncertain Networked Nonlinear Systems

A Compound Scheme Based on Improved ADRC and Nonlinear Compensation for Electromechanical Actuator

A compound scheme, based on an improved active disturbance rejection controller (ADRC) and nonlinear compensation, is developed for the electromechanical actuator (EMA) system in this paper. First, considering the influences of backlash, friction on the EMA system, a model for the EMA system is presented. The LuGre model and Hysteresis inverse model are used to compensate for the friction and backlash phenomenon. Then, the method of improved ADRC, based on the Fal function filter and a Linear extended state observer (LESO), is investigated. Simultaneously, since the controller parameters of the improved ADRC are complicated, the non-dominated sorting genetic algorithm II (NSGA-II) is presented to optimize the controller parameters, to achieve the best dynamic response. Finally, simulation and experiment are presented to validate the effectiveness of the proposed method. Under the nonlinear compensation, the performance of the proposed compound scheme is compared with the conventional proportional integral (PI) controller, in terms of step response analysis and sine wave response analysis. Simulation and experiments show that the proposed controller provides high-performance dynamic characteristics.

Direct Adaptive Preassigned Finite-Time Control With Time-Delay and Quantized Input Using Neural Network

This paper investigates an adaptive finite-time control (FTC) problem for a class of strict-feedback nonlinear systems with both time-delays and quantized input from a new point of view. First, a new concept, called preassigned finite-time performance function (PFTF), is defined. Then, another novel notion, called practically preassigned finite-time stability (PPFTS), is introduced. With PFTF and PPFTS in hand, a novel sufficient condition of the FTC is given by using the neural network (NN) control and direct adaptive backstepping technique, which is different from the existing results. In addition, a modified barrier function is first introduced in this work. Moreover, this work is first to focus on the FTC for the situation that the time-delay and quantized input simultaneously exist in the nonlinear systems. Finally, simulation results are carried out to illustrate the effectiveness of the proposed scheme.

Adaptive Fuzzy Quantized Control for Nonlinear Systems With Hysteretic Actuator Using a New Filter-Connected Quantizer

This paper aims at the issue of adaptive fuzzy quantized control for a class of uncertain nonlinear systems preceded by unknown actuator hysteresis. One challenging problem that obstructs the development of the control scheme is that the direct application of the quantized signal containing high-frequency components to the hysteretic actuator will lead to system performance deterioration. To resolve this challenge, we propose a filter-connected quantizer in which a hysteretic quantizer is employed to reduce the communication rate and an adaptive high-cut filter is designed to smooth the hysteresis input. Furthermore, based on fuzzy logic systems' online approximation capability, a novel adaptive fuzzy control scheme involves a new control strategy and an adaptive strategy is constructed via a backstepping technique. It is proved that the proposed control scheme guarantees the tracking error is asymptotically convergent to an adjustable neighborhood of zero and all of the closed-loop signals are uniform ultimate bounded. Lastly, simulations are conducted to further verify our theoretical results.

Distributed Adaptive Containment Control for a Class of Nonlinear Multiagent Systems With Input Quantization

This paper is devoted to distributed adaptive containment control for a class of nonlinear multiagent systems with input quantization. By employing a matrix factorization and a novel matrix normalization technique, some assumptions involving control gain matrices in existing results are relaxed. By fusing the techniques of sliding mode control and backstepping control, a two-step design method is proposed to construct controllers and, with the aid of neural networks, all system nonlinearities are allowed to be unknown. Moreover, a linear time-varying model and a similarity transformation are introduced to circumvent the obstacle brought by quantization, and the controllers need no information about the quantizer parameters. The proposed scheme is able to ensure the boundedness of all closed-loop signals and steer the containment errors into an arbitrarily small residual set. The simulation results illustrate the effectiveness of the scheme.