Theory Of Iterative Learning Control And Its Applica-tion On Networked Control Systems

Iterative learning control(ILC) is a technique which is applicable to systems or pro-cesses operating repetitively over a finite interval time. The present control input used the information such as the previous control input signals and tracking error signals after every trial until the desired trajectory is followed to a high precision. In this dissertation, First-ly, A high-order internal model (HOIM) ILC is presented for tracking problem of varying reference trajectories in iteration domain. Then there are three different ILC algorithms for discrete time networked control systems with various data dropouts and transmission delays and proved asymptotical convergence in iteration domain. The main contents are outlines as follows.1. A HOIM-based ILC was proposed to deal with iteratively varying reference trajec-tories problems. Firstly, the varying reference trajectories in the iteration domain are described by a HOIM that can be formulated as a polynomials between two con-secutive iterations. Then, by incorporating HOIM into the ILC law, and designing appropriate learning control gains. Through both rigorous theoretical analysis and numerical simulations, the learning convergence of tracking error in the iteration do-main can be guaranteed for continuous-time linear time-varying system when the HOIM-based ILC is used. Finally, the HOIM-based ILC is applied to nonlinear cas-es.2. An ILC is presented for a class discrete time networked control system which a net-work exists between the sensor and the controller. The packet dropouts and one-step delay subject to the Bernoulli distribution. Owing to the essence of feedforward-based control ILC can perform trajectory tracking tasks while both the packet dropout-s and the one-step delay phenomena are taken into consideration.3. The discrete-time networked control systems are investigated where the sensor-to- controller channels experience both random packet dropouts and transmission de-lays. Firstly, random data dropouts and transmission delays of network communica-tion are modeled as a Markov chain process. Then, an ILC with moving weighted average is proposed for discrete time-varying linear system with network commu-nication channels. Finally, theoretical analysis validate the effectiveness of the ILC with moving weighted average for networked control system with both data dropouts and transmission delays.4. The problem of ILC is considered for a class of discrete time-varying networked control systems with random packet dropouts. The packet dropout occurs during the packet transmission between the ILC controller and the actuator of plant. Firstly, the packet dropout is viewed as a binary switching sequence which subjects to the Bernoulli distribution. Then, the hold-input scheme with average ILC is proposed. The average ILC is consisted of averaging previous control signals and tracking er-ror signals, and the hold-input scheme is adopted to compensate the packet dropout at the actuator. Finally, theoretical analysis and numerical simulations validate the effectiveness of the hold-input scheme with average ILC for discrete time-varying linear system.The conclusions and perspectives are presented in the end of the dissertation.