Research Of Learning And Control Strategies For Networked Control System

Networked control system (NCS) has wide industrial applications, such as in ball maglev system, dual-axis hydraulic positioning system, robot and large-scale transportation vehicles, due to various advantages including low cost of installation, ease of maintenance, easy installation, and flexibility. However, many industrial systems are characterized by time-varying, nonlinear and multivariable coupling. The plants are distributed at different locations, and distributed control strategies are most common where many local controllers are implemented. While the local controllers are mostly embedded real-time controller or program logic controllers which are highly reliable, but they can not fulfill the needs for high quality control due to very low computational capability for implementation of complex control strategies. This imposes a severe challenge on the widely used single-layer NCS. Learning control strategies are capable of effectively improving the control performance by on-line mining of valuable knowledge and hidden relations, accumulating experience and adapting to environment, but it is difficult to design and implement via the local controllers. Therefore, a two-layer networked learning control system (NLCS) is proposed. The focus of this paper is on the design of control strategies of two-layer NLCS. The main work is summarized as follows:Firstly, a two-layer NLCS architecture is presented, and.the differences between two-layer NLCS and NCS are analyzed. Then, a novel fast radial basis function (RBF) neural network based on fast recursive algorithm (FRA) is proposed. The method can not only select RBF neural network centers, but also can estimate the network weights simultaneously using a back substitution approach. Unlike popular orthogonal least squares (OLS) algorithm, FRA requires less computational effort by the computational complexity analysis. Furthermore, a self-learning fuzzy control strategy based on fast RBF neural network for two-layer NLCS is presented. Under this strategy, RBFNN is employed in a learning agent, while fuzzy control strategy is adopted in the local controller. The RBF neural network is used to on-line tune the parameters of fuzzy controller. Simulation results confirm its effectiveness.Secondly, a new locally regularized recursive method for the center selection of RBF neural network is proposed. By associating each candidate center to an individually regularized parameter which is optimized within the Bayesian evidence framework, the associated weights of those nonsignificant candidate centers are effectively forced to zero as their corresponding regularization parameters become sufficiently large. Therefore, the proposed method can easily choose significant centers and hence improve the sparsity. The computational complexity analysis shows that the computational cost is significantly reduced by using a proper regression context and the recursive formulas. Then, a self-learning fuzzy control strategy based on RBF neural networks with regularized parameters for two-layer NLCS is developed, and simulation results demonstrate its effectiveness.Thirdly, a novel locally regularized automatic construction method for RBF neural models is proposed. This is achieved by combing the proposed locally regularized recursive method with the leave-one-out (LOO) cross-validation criterion. It can automatically determine the network size by iteratively minimizing a LOO mean square error (MSE) without the need to specify any additional termination criterion. By defining a proper regression context and the recursive formulas, the whole network construction process can be concisely formulated and easily implemented at significantly reduced computational expense which is not achievable using any existing approaches. Then, self-learning fuzzy control strategy based on automatic constructive RBF neural network with regularized parameters for two-layer NLCS is investigated, and its effectiveness is then confirmed by simulation results.Fourthly, to improve the control performance, a two-layer NLCS scheme without changing the local controller is studied by using reinforcement learning methods. Under this scheme, reinforcement learning methods are employed in a learning agent, while proportion integration (PI) control strategy is adopted in the local controller. Total control signal consists of control signal of learning agent plus control signal of PI controller, where control signal of learning agent dynamically tunes total control signal. Then, two-layer NLCS using Q-learning and Actor-Critic neural network are investigated respectively, and simulation results verify its effectiveness.Fifthly, the characteristics of multi-channels networks for multi-input multi-output (MIMO) NCS are analyzed. The generalized sensor and generalized actuator are defined, and the sate of generalized sensor and generalized actuator can be described using diagonal matrix. The methods of zero-set and zero order holding (ZOH) are used to compensate data loss, MIMO NCS models are then modeled as a switched system with unknown switched sequence and sufficient conditions for asymptotically stable are given. This can be further extended to uncertain MIMO system, and sufficient conditions for asymptotically stable are derived. Numerical example confirms its effectiveness.Finally, SUPMAX distributed control system (DCS) is introduced, and the network architecture and the method for tracking networked nondeterministics are presented. A two-layer NLCS experiment platform is then established by addding a learning agent to SUPMAX DCS. Using the SMCP protocol and address resolution function, and real-time data can be obtained from distributed processing unit (DPU) via Ethernet network. An advanced PID self-tuning software package is then developed using the characteristic area method and a relay tuning algorithm, which can tune the PID parameters in DPU. Simulation experiments demonstrate that the software can effectively tune the PID parameters, and shows the potentials of the software for real-life applications.