Identification Modelling and fault-tolerant predictive control for industrial input nonlinear actuator system

Abstract

Industrial control system plays an important role in process industry, chemical production and other manufacturing industries. It has important theoretical research significance and engineering application value to establish accurate models for industrial actuator system with dead-zone input nonlinearity. The structure and order of the typical system model are determined by analyzing the mechanism relationship of the system. Based on sampled data, a data-driven identification algorithm is proposed for industrial actuator system with dead-zone input nonlinearity to describe the main dynamic characteristics of the system output. The convergence property of the proposed algorithm is also analyzed. Process faults will reduce the tracking control accuracy of industrial actuator systems. An intermediate observer is used to estimate the faults. A fault-tolerant synchronous control feedback rate based on fault estimation is designed to compensate faults. The input dead zone block will weaken the feedback control performance of the input signal and reduce the control precision of the intermediate observer. According to the dead zone model, a compensator is introduced to transform the dead zone function into a linear function passing through the origin of coordinates. The transformed linear segment and the dynamic linear segment of the system constitute the generalized linear system. The model predictive control (MPC) strategy is designed to achieve robust and precise control by eliminating the effect of measurement noise. Finally, numerical simulation example and experimental test results verify the superiority and merit of the modeling method and fault-tolerant control strategy.