Decentralized Adaptive Iterative Learning Control For A Class Of Interconnected Systems And Applications

With the rapid development of modern science and technology, many practical control systems can be seen as nonlinear interconnected large-scale systems, so the decentralized control theory of interconnected large-scale systems has attracted great attentions in both academic research and industrial applications. Owing to the unrealized information exchanges between subsystems physically and the lack of computing power corresponding to the centralized controllers, the decentralized control technique usually has priority in usage under these complicated circumstances. The dissertation focuses on the certain kinds of nonlinear interconnected systems and analyses the systems characteristics. Then the decentralized adaptive learning control algorithms are designed correspondingly. The convergence analysis is provided for the proposed algorithms in this paper. Simulation results demonstrate that, utilizing the proposed controllers, the tracking error for each subsystem converges along the iteration axis. By systematically combining adaptive iterative learning control and decentralized control techniques, an attempt was made in this dissertation not only to forward deep development of interconnected system control science but also to provide better approaches to practical engineering problems to a certain degree.The main contents are composed of the following four parts.(1) An decentralized model reference adaptive iterative learning controller is designed for a class of nonlinear interconnected system with uncertainties. There are two parts in the controller, one is the feed-back controller which is used to stabilize the closed subsystem, the other is the adaptive iterative learning controller which eliminates the effects of the interconnections among subsystems. The convergence of the proposed algorithm is proved based on a Lyapunov-like method, guaranteeing the tracking error of each subsystem approaches to zero along with control iterations. A simulation example is given to show the effectiveness of the proposed method.(2) Base on the nonlinear interconnected systems with state delay and model uncertainties, a decentralized model reference adaptive iterative learning control is proposed in this paper. The proposed controller of each subsystem is delay-independent which utilize only the current closed-loop local state variables. In order to eliminate the effects of interconnections and state delay, the extra adaptive parameters are updated along iteration axis. The algorithm convergence is proved carefully and the effectiveness of the proposed scheme is shown through computer simulation. Time delays, due to the information transmission between subsystems, naturally exist in interconnected systems and hence the control problem becomes more important than those interconnected systems without time delays.(3) A decentralized backstepping adaptive iterative learning control schemes is proposed for a class of interconnected nonlinear systems of strict feedback form. The interconnections among subsystems are assumed to be bounded by an unknown1st-order or Pth-order polynomial in sub-outputs. A time-vary adaptive iterative learning control gain ζik is introduced to counteract the effect of the interconnections which are linear bounded in sub-output. As for the Pth-order polynomial sub-outputs bounded interconnections, two adaptive updated parameters ζik and βik are utilized. All the adaptive parameters are updated along both iteration axis and time one to counter the effects of the interconnections. It is shown that by using the proposed decentralized controller, the outputs of the subsystems can track the desired reference outputs iteratively. The simulation results show that the output tracking error of each subsystem converges along the iterative axis.(4) A decentralized robust adaptive iterative learning control scheme for trajectory tracking of interconnected manipulators is developed. The proof of convergences based on the Lyapunov method. Through the iterative update of subsystem unknown parameters and interconnected parameters, the Lyapunov function composed of tracking errors and estimated parameter errors is monotonically decreased in iteration domain. The simulation results show that this method can guarantee the tracking error convergence of each subsystem.