Double Layer Network-based Operational Feedback Control Method And Simulation Experiments For A Class Of Industrial Processes

One of the goals of operational optimal control for industrial processes is to keep the economic index of products in the production process performing in the range of destination value. Traditional operational control can only utilize manual method to set the setpoints of the device level loop, in other words, the operational level is in the open-loop state. However, manual control method cannot tune the setpoints accurately and timely, hence the economic index cannot be handled, even some malfunctions are generated. In order to deal with this problem, a model predictive control based double-layer network-based operational feedback control method for a class of industrial processes which can be formulated as linear time variant models is proposed in this paper. The proposed method can automatically and dynamically compensate the setpoints of the device level loop. We consider some unreliable communicational modes, such as dropout packet and network noise, in the controller design and stability analysis. Then numerous simulation experiments in the Matlab and operational optimal control experimental platforms are implemented to test the effectiveness of the proposed method. We also compare the performance of proposed method with traditional operational control method. The main work and contribution in this paper are as follows.Firstly, the double layer network-based operational feedback control problem is described. The mathematical model of network unreliable communication is established. The controller design model which separately considering network dropout packet, network noise, and network delay are derived.Secondly, the LQR controller with stability margin is designed for device level. This controller can not only keep the output of device level tracking the setpoints of loop, but also make the convergent velocity tunable. We utilize real-time optimization method to find the steady optimal setpoints. Considering network dropout packet and network noise, the model predictive control based controller are designed to dynamically compensate the setpoints and to prove the system running in the predestinate economic index. The stochastic stability analysis is given out.Thirdly, we use flotation process as the background to make some simulation experiments in the Matlab and operational optimal control experimental platform to validate the effectiveness of the proposed method by comparing with the traditional operational control method. We also design experiments to test the influence of the serious network dropout packet, network noise, and network delay on the stability of the system.