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Closed-loop Adaptive Inverse Control And Its Application Research On Thermal System

Posted on:2012-07-31Degree:DoctorType:Dissertation
Country:ChinaCandidate:Z J WangFull Text:PDF
GTID:1118330362454398Subject:Power Engineering and Engineering Thermophysics
Abstract/Summary:PDF Full Text Request
The control strategies based on system's inverse model mainly include two control method, those are adaptive inverse control and inverse system control. In recent years, the control methods based on the system's inverse model and its application researches have aroused widely attention. To research deeply the problems such as the method of modeling the process of system's inverse dynamic, the relationships and the effective integrations between the control strategies based on system's inverse model and the classical feedback control strategies, which are expected to provide new control idea and technological means.Fuzzy model is a set of fuzzy inference rules based on fuzzy linguistic variables, fuzzy sets and fuzzy logic, which has been used widely to modeling and controlling complex nonlinear systems. In this paper, we have studied the problems of closed-loop adaptive inverse control methods based on T-S fuzzy model and its application in thermal systems, the main work includes the following four aspects:①For the existing problems of T-S fuzzy identification methods, we started from fuzzy clustering schemes and proposed an identification method of T-S fuzzy model and a sub-step identification method of T-S fuzzy based on decomposing cluster of the worst subset, and applied them in modeling typical thermal processes and their inverse systems. In the identification process, new clustering and identifying objective functions were introduced, and new fuzzy rules can be automatically generated according to the required modeling accuracy, which avoided the disadvantages of the number of fuzzy rules should be given in advance. When the identification accuracy does not meet the predetermined target, only the structure and parameters of new sub-model should be identified. Comparing with the traditional identification methods of T-S fuzzy model, the proposed method has the advantages of high precision, less the number of fuzzy rules and less calculation cost and good real-time tracking ability, which provides an effective support for the development of adaptive inverse control method for thermal process. In this paper, the effectivenesses of the proposed methods were verified by simulation experiment of identifying nonlinear systems.②For the features of complex thermal processes with the characteristics of large delay and thermal inertia, an incremental control algorithm was established based on corrected datumquantity; The correspondent relationship between the parameters of the inverse model of controlled object and the characteristic parameters of controller were demonstrated, and an closed-loop adaptive inverse control system based on increment form was proposed, which realized the organic combination of adaptive inverse control with classical feedback control scheme. The characteristic parameters of controller can be adjusted directly according to the online identification results of the inverse model of the controlled object. In this paper, for several typical thermal systems (including SISO and MIMO thermal systems), the incremental closed-loop adaptive inverse control method was utilized to control them by simulation experiments. The results showed that, comparing with conventional adaptive inverse control method, the incremental closed-loop adaptive inverse control method can effectively reduced the influence of the precision of the inverse dynamic model of the controlled object on the control performance, and has good adaptive ability and robustness.③An adaptive PID controller design method based on the inverse model of the object (PID-IMO) was proposed. In the established PID-IMO control system, by choosing the appropriate structure of the inverse model, the accordance of structures of the inverse model and PID controller was realized; The inner and equal relationships between the characteristic parameters of adaptive PID controller and the parameters of the inverse model of controlled object were demonstrated. The parameters of PID controller were obtained directly according to the on-line identification result of the inverse model, and the PID controller which matches with the controlled object's properties was formed, and this method was extended cascade control system. The simulation result of controlling two typical thermal systems showed that the proposed adaptive PID-IMO control system has good adaptive ability, anti-interference ability and robustness.④Inverse system control is a nonlinear control strategy with widely applicability. In this paper, fuzzy model was introduced to this control scheme. The proposed sub-step identification method of T-S fuzzy based on decomposing cluster of the worst subset was adopted to establish the inverse model of the controlled object, which avoided the difficulty of solving the analytical inverse model of the controlled object, and the linearizing and decoupling effect of the inverse system based on fuzzy model was investigated; According to the different delay characteristic of the controlled object, PID controller and Smith predictor were adopted as the additional controller, and two fuzzy inverse control system were designed. In this paper, the performances of the designed control systems were verified by simulation experiment.
Keywords/Search Tags:Fuzzy identification, Inverse model, Adaptive control, Closed-loop inverse control, Thermal system
PDF Full Text Request
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