Research On Networked Learning Control System For Uncertain Nonlinear Object

Networked control system is a kind of feedback control systems, wherein the control loop is closed via the communication network, and the network-induced delay is inevitable. Network-induced delay is a class of uncertain and random delay, which results in the difficulty for the control system to be designed. In practice, moveover, a lot of uncertain and nonlinear factors come into existence. So the robust analysis and design of uncertain nonlinear networked control system need to be emphasizd. Obviously, the first issue needs to be solved is the robust stability analysis. In the thesis, a class of uncertain nonloinear networked control system is discussed and the sufficient condition for robust asymptotic stability is presented. And it is also given the maximum network-induced delay that is allowable for the stability to be remained. The Lyapunov theorem is employed to solve the problems.In networked control systems, control loop is closed via the communication network entirely. The network-induced delay as well as the possible data loss will always deteriorate the control performance. And in addition, the possibility of network paralysis makes the system security be hard to guarantee. On the other hand, for the expanding of the production scope, continuously developing complication and time varying of the controlled plant, more and more controller with some learning ability, such as the ability of online system identification, optimazation and system diagnosis, are applied to adapt itself to the time varying environment to improve the control performance and to keep its optimal operation condition. The learning algorithm complexity, however, is also continuously increased as the demand on control performance going higher and higher. Existing field control equipments' computing and memory resource will be exhausted by so complicated learning algorithm. If the high capability field control equipments would be produced, they will be very expensive. In the thesis, we design a systerm that the local control unit is simple and ease of realization, while the complicated identification and learning algorithm are realized by the remote network unit. Named as networked learning control system, this system could be a control system with low cost, high capability and full use of network resource.As mentioned before, if only apply certain and linear control strategy, it would be very hard to improve the control system performance. Therefore for the uncertain nonlinear plant, the research on networked learning control system is valuable. And the stability and learning algorithm convergence should be main focus. In this thesis, some following aspects have been carefully researched. First, for the control object with unknown mathematical model, the network-induced delay compensation strategy, which is based on the cubic spline one-step predictive algorithm, is proposed. In order to improve the compensation precision of network-induced delay, multi-step predictive delay compensation model of cubic spline rolling optimization is also designed, and the corresponding networked learning stratery is put up. The networked learning control simulation is studied for complex, time-varying control object with unknown mathematical mode, and the simulation results prove the validity of this control strategy.Second, for the uncertain nonlinear object, the composite predictive algorithm in which the nonlinear model predictive is compensated by error prediction based on multi-step predictive algorithm using cubic spline rolling optimization is presented. And the corresponding networked learning algorithm is also presented. The networked learning control simulations are studied for uncertain nonlinear object and the simulation results prove the validity of the control strategy.Third, for a class of uncertain control object with unknown nonlinear function, dynamic recurrent neural network (DRNN) is applied in local controller, and unknown nonlinear function is remotely identified by uniformly partition cubic spline interpolating function. The network-induced delay is compensated by multi-step predictive algorithm of cubic spline rolling optimization in the networked learning unit. Finally, for uncertain bounded delay, the maximum online learning rate of DRNN that insures the system stability is obtained by Lyapunov stability theorem, and then the sufficient condition for the asymptotic stability is presented.At last, the networked learning control strategy is applied in the circulating fluidized bed boiler (CFBB) of the electricity generating burning systems and the H2-O2 fuel cell test system. The validity of the networked learning control strategy is also testified by the simulations and field testing.