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Research On Constrained Control Of Nonlinear Systems Subject To Input Saturation

Posted on:2018-01-06Degree:MasterType:Thesis
Country:ChinaCandidate:N JiFull Text:PDF
GTID:2348330518986564Subject:Control Science and Engineering
Abstract/Summary:PDF Full Text Request
In practical industrial production, input saturation extensively exists in actual sys-tems of a lot of fields due to the inability of delivering unlimited signals for systems.However people often ignore the saturation of inputs during the design process of con-trollers for the object systems, the performance of the plant systems can be induced when the systems are under the condition of input saturation. People have paid close attention to the problem of saturation after the several major disasters. People in the worldwide have done so many researches on the saturation problem in the last several years, the technology of the constrained control has the tremendous development. But the most of the achievements are based on linear systems, the results for nonlinear system are defi-cient. Moreover, few people can deal with the problems of amplitude and rate saturations for actuators or inputs. For these obvious reasons, this thesis presents some novel anti-windup control methods for several typical nonlinear systems with input saturation, such as solid oxide fuel cells (SOFC), aeroelastic systems and variable speed wind turbines(VSWT).The major research results and innovative points can be concluded as the next several parts:1. The novel dynamic anti-windup strategy for SOFC which is the typical discrete nonlinear system is proposed. We simply introduce the fundamental and structure for SOFC. In the design process of the controller for SOFC, we adopt the radial basis func-tion (RBF) neural network to identify the model of SOFC for achieving the sensitivity of plant output to controlled input, then the adaptive controller is designed based on the back propagation (BP) neural network and PID control method, and a dynamic anti-windup compensator on the basis of the Jacobian information is proposed for accommodating the reference to deal with the problem of the input saturation. Moreover, this paper theoret-ically proves the stability of the proposed method based on Lyapunov stability analysis.Finally, the simulation results for SOFC are provided to demonstrate the effectiveness of the proposed constrained control approach.2. The innovative dynamic anti-windup method for aeroelastic systems which is the typical multi-input multi-output (MIMO) discrete nonlinear system is presented. We give the simple description of the structure for the classical aeroelastic system (2-DOF pitch-plunge wing). Compact form dynamic linearization (CFDL) is designed to linearize the model of the 2-D wing-flap system with input constraints. At same time, the adaptive optimal controller is designed based on the multi-observer. Then, we convert the control problem to the optimization problem which is a solution for linear matrix inequality(LMI) constrained minimization problem. Finally, simulation results for the 2-D wing-flap system with input constraints can demonstrate the availability and potential of the presented approach. This proposed model free control approach of aeroelastic systems is particularly effective while the explicit analytical model of aeroelastic systems is difficult to develop.3. The creative dynamic anti-windup method for VSWT which is the typical con-tinuous nonlinear system is presented. We have the simple introduction of the structure and theory for VSWT. A model transformation is proposed for VSWT that we convert the VSWT system into a strict-feedback form through state transformation. Then, an extend state observer (ESO) is given to estimate the unknown dynamic model, and the command-filtered backstepping controller is proposed for the VSWT system. Finally,simulation results are given to demonstrate the effectiveness of the proposed constrained control scheme.
Keywords/Search Tags:nonlinear system, input saturation, constrained control, model free adaptive control, neural network
PDF Full Text Request
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