A framework for multi-variable, semi-adaptive predictive control system

Abstract

In view of the degradation of predictive control performance caused by model mismatch, a multi-variable, semi-adaptive zone predictive control system framework is presented. The proposed framework changes the traditional control mode to testing mode and turns set-point to zone control, thereby realizing the constraint satisfaction of the output variables of the test process. For the constraint zone models predictive control in the integrated testing mode, the amplitude strength of testing input signals is introduced to realize the constraint guarantee function and signal-to-noise ratio maximization. The framework implements the open-loop test to improve test efficiency under the premise of production on the rails. The signal-to-noise ratio of the testing process is ensured by maximizing the test signal amplitude. Furthermore, the framework is extended from constrained to two-layer model predictive control, and the benefit balance coefficient is introduced to realize the balance between economic benefit and testing. The method proposed in the paper is a type of on-line open-loop identification, which solves the problem of the correlation between input signals and noises in closed-loop identification. The simulation results verify the effectiveness of the method.