Font Size: a A A

A Study On Tracking Control Baesd On Affine Fuzzy Model Of Complex Thermal Processes

Posted on:2016-03-01Degree:DoctorType:Dissertation
Country:ChinaCandidate:S J LiuFull Text:PDF
GTID:1108330503477348Subject:Power Engineering and Engineering Thermophysics
Abstract/Summary:PDF Full Text Request
Most controlled thermal processes have exhibited complex dynamic characteristics, i.e. high nonlinearity, strong-coupling, large time-delay and parameter variations. Therefore, the traditional PID control method shows inability to obtain an excellent response when the unit demands change in a wide range. On the other hand, for many advanced control strategies, an accurate model of the process is the prerequisite for successful application. The Takagi-Sugeno (T-S) fuzzy model is an effective methodology to approximate the complex nonlinear systems, and fuzzy logical control has been proved to be a successful control approach for certain nonlinear systems.In general, the T-S fuzzy system can be divided into affine fuzzy system and homogeneous fuzzy system according to whether the affine term is contained. The affine term can describe the dynamic of the process more precisely. Nevertheless, in convenience of stability analysis and controller design come along with it. It was not until recently that some researchers have attempted to develop methods for the stability analysis and controller design of the affine fuzzy system.1. Respectively, for the approximate linearization and system identification are two ways to explore the significance of the study of affine fuzzy models. It is concluded that when the mathematical model of the nonlinear mechanism object is approximate linearized to get T-S fuzzy model, only the first of all the local sub-models are placed in the same coordinate substrate, can be smoothed via membership functions, but in non-coordinate origin, even balance, fuzzy model is generally contain affine item. In addition, data identification of a superheated steam temperature system shows that, when using the same identification method, the accuracy of affine fuzzy model is higher than the homogeneous fuzzy model.For the present continuous/discrete affine fuzzy model for stability analysis, there are two common methods. Whether the affine item processing model after pieces were part of the coefficient matrix, it is based on the stability analysis of the fuzzy Lyapunov function of autonomous systems. To reduce the number of linear matrix inequalities(LMIs), it puts forward the concept of "affine group of maximum overlap rule "and "affine effective set of rules", and use examples to compare the conservation of the criteria. Among them, the method of entering the affine criterion into the coefficient matrix approach can get a strong conservative, and it is not conducive for the design of maintaining stability controller.2. For discrete nonlinear affine fuzzy object model, instead of using the method of entering the affine term into the coefficient matrix, we use the parallel distributed compensation (PDC) technique to design the stability controller. The stability analysis of the closed-loop control system based on fuzzy Lyapunov function, and introduce the S-procedure to decreases the conservative of criterions. Stability criterion that we get is a series of bilinear matrix inequalities (BMIs), and we use the idea of drawing iterative solution to propose the algorithm BMI equation.On this basis, for large power generators in tracking Automatic Generation Control (AGC) instruction with plant conditions need to achieve fast response, but due to the interference of factors external to the unit itself has more control and influence the effect of the measurement signal to noise problems, a sets H∞ static error tracking control system is designed. The H∞ tracking control problem is to solving an optimization problem to ensure the stability of the closed-loop system and reduce the effects of the bounded external disturbances, and put forward the corresponding algorithm of BMI. Fuzzy tracking controller design is used for the target machine furnace coordinated tracking system. The simulation results show that:the closed-loop control system can approach the target very closely. But if the system has the limits of lift rate, the tracking process adjustment time will become longer and the overshoot will become large, and also the control valves and frequency range of motion will become large, and difficult to be changed by adjusting the controller to control the effect of the design parameters. It indicates the conservation of stability criterion we get is relatively large.3. During the operation progress of thermal power units, changes of the system parameters arise due to the variety of coal, equipment aging and so on. In these situations, this paper presents a guaranteed cost stability controllers based on affine fuzzy model with uncertain parameters. A descriptor representation of the fuzzy system is taken to derive the sufficient stability conditions in terms of linear matrix inequalities (LMIs), after stability analysis of the closed-loop system combined with S-procedure. The system is derived in terms of LMIs, instead of BMIs in most of pioneer works. Thus, the conditions can be easily solved via LMI toolbox.This approach can design controllers with guaranteed cost stability and performance. The stability criterion in the formulation of the controllers has a weaker conservativeness. With the approving of the quadratic performance index and fuzzy Lyapunov functions, the stability criterion of the closed-loop can be derived which can meet the cost functions. To determine the effectiveness of the proposed fuzzy controller design method, a numerical example is considered in the AGC tracing progress of the Bell-Astrom system and the simulation results show that the closed-loop system is asymptotically stable.4. The constrained problem of the operators in the progress can be solved with model predictive control algorithm. Based on fuzzy Lyapunov functions and common Lyapunov functions respectively, the stability conditions for robust controller design of uncertain continuous-time affine fuzzy system are derived. Meanwhile, to obtain relaxed conclusions, the S-procedure is employed to decrease the conservatism of the stability results. After that, with the stability conditions of infinite horizon based on the theory of stability predictive control, the problem of operator constrains can be converted into the solving of LMIs functions with constrains. The simulation of (continuous stirred tank reactor, CSTR) shows the design target can be traced rapidly and stably in closed-loop system. The controlled variables are in the designed range, which avoids the frequent and hard action of the controllers.
Keywords/Search Tags:Affine fuzzy model, tracking control, thermal process, boiler-turbine coordinated system, linear matrix inequality, guaranteed cost, stable model, predictive control
PDF Full Text Request
Related items