Iterative Learning Tracking Control of Nonlinear Multiagent Systems with Input Saturation

A tracking control algorithm of nonlinear multiple agents with undirected communication is studied for each multiagent system affected by external interference and input saturation. A control design scheme combining iterative learning and adaptive control is proposed to perform parameter adaptive time-varying adjustment and prove the effectiveness of the control protocol by designing Lyapunov functions. Simulation results show that the high-precision tracking control problem of the nonlinear multiagent system based on adaptive iterative learning control can be well realized even when the input is saturated. Finally, the validity of the proposed algorithm is verified by numerical analysis.

High-Precision Tracking Control of Machine Tool Feed Drives Based on ADRC

In this paper, the active-disturbance-rejection control (ADRC) is applied to realize the high-precision tracking control of CNC machine tool feed drives. First, according to the number of the feedback channel, the feed systems are divided into two types: signal-feedback system, e.g., linear motor and rotary table, and double-feedback system, e.g., ball screw feed drive with a load/table position feedback. Then, the appropriate controller is designed to ensure the closed-loop control performance of each type of system based on the idea of ADRC. In these control frameworks, the extended state observers (ESO) estimate and compensate for unmodeled dynamics, parameter perturbations, variable cutting load, and other uncertainties. For the signal-feedback system, the modified ADRC with an acceleration feedforward term is used directly to regulate the load/table position response. However, for the double-feedback system, the ADRC is applied only to the motor position control, and a simple PI controller is used to achieve the accurate position control of the load. In addition, based on ADRC feedback linearization, a novel equivalent-error-model based feedforward controller is designed to further improve the command following performance of the double-feedback system. The experimental results demonstrate that the proposed controllers of both systems have better tracking performance and robustness against the external disturbance compared with the conventional P-PI controller.