Structure Analysis Of Mamdani Fuzzy Control Systems And Applications In HVAC Systems

With the increasing development of technologies, the control processes become more and more complex in control engineering. The control processes generally have the characteristics of nonlinear, multi-loops, big time-delay etc and the parameters are time-variable, then they can't follow specific physical or chemical rules, thus, it is difficult to act classical quantitative analysis and accurate mathematical models of complex process situations are not easy to get so that the classical control strategies and model control theories can't control them availably. Consequently the rapid development of modern industrial processes urgently needs to find more effective control strategies to finish nonlinear control of practical process systems.Since English engineer named Mamdani applied the fuzzy control technique on the control of steam engine in 1974, fuzzy control has become the important part of intelligent control with the development of computer and the relational technologies. Furthermore fuzzy control comes to play more and more important actions in complex process control systems in recent years. As one of the nonlinear control strategies, fuzzy control has the most remarkable characteristics of no needing precise systematic mathematical model. The experiential knowledge of operators is used to construct fuzzy inference rules and then it can control the systems through fuzzy reasoning courses. Fuzzy control avoids the calculation complexity while takes good use of successful experience of operators on spot. Moreover, fuzzy control takes on strong robustness which can conquer inherent time-variable nonlinear, uncertainty and outside disturbance so that it can improve process control quality effectively and break a new path for control research of complex industrial processes and nonlinear systems. Fuzzy control has been widely applied into industrial control field, automation on home electrical facility and other application scopes to settle control problems which are difficult to finish by classical control technique. And then fuzzy control is going to get more and more interest of researchers and engineers in control theories and application field. However, as a whole of fuzzy control research systems, the research about fuzzy control systems is not a perfectly integrated system and there still exist some problems to resolve not only in theories but also in application. In order to develop applications of fuzzy control theories, this thesis studies structure analysis of fuzzy controllers, proposes two novel fuzzy control algorithms based on research fruits of classical PID controllers and model predictive controllers and then applies the proposed fuzzy control algorithms on the temperature control of Heating, Ventilating Air-conditioning systems. The results of simulation and experiments show good control performance of the proposed novel fuzzy control algorithms. In summary, the main contents of this dissertation are as follows:(1) Firstly the survey of fuzzy control systems is given. The research background, generating, developing status, characteristics, basic styles of fuzzy control systems are reviewed and then it introduces main research aspects, contents and their development, combination of both fuzzy control and other control strategies such as PID control, model predictive control and application situation. At last, some difficult problems in fuzzy control development are pointed out and the main research contents of this thesis are summarized.(2) The fundamental, basic structure and design methods of fuzzy control systems are studied. Then after considering one new fuzzy implication algorithm, structure analysis of max-min Mamdani fuzzy controller is developed on the view of mathematical analysis. It proves that a nonlinear fuzzy controller with linear rules, whose input and output variables are isoceles fuzzy number membership functions, is the sum of a global two-dimensional multilevel relay and a local nonlinear PD controller. Afterwards its limit structure characteristics and stability analysis are given. Comparing with other fuzzy reasoning methods, typical fuzzy controllers with different reasoning such as sum-product , sum-min , max-product have similar structure characteristics. Finally further discuss about fuzzy controllers structure is done.(3) A novel method to design fuzzy controller based on the proportional, integral, derivative parameters of PID controller is proposed. It takes full advantage of mature technology of PID controller to improve the design strategies of fuzzy controller. At first structure of fuzzy controllers is chosen to fit for practical industrial application through comparison and analysis. Then the mathematical analytical expression of parameters between fuzzy controllers and linear gains coefficients K_P,K_I,K_D of conventional PID controllers is got based on the structure analysis of fuzzy controllers and it shows that fuzzy controller is one time-variable nonlinear PID controller in nature. Then the novel fuzzy controller is designed through gains tuning of PID controller based the analytical relations between normalized factors of fuzzy controller and gain coefficients of the conventional PID controller.(4) The improved fuzzy variable discourse method is studied. Because the essence of variable discourse of universe is equal to change normalized factors of fuzzy variables, increasing the values of normalized factors for fuzzy input variables can reduce the discourse of universe if the basic discourses keep constants. Moreover it is easier to operate for changing normalized factors than variable discourse. Then this improved fuzzy variable discourse method is applied to tune and optimize designed parameters of fuzzy controllers so that the dynamical performance is obtained.(5) A new fuzzy linearization technique based on sum-min Mamdani fuzzy model is proposed because of difficult to model and control of nonlinear systems. After constructing the fuzzy Mamdani predictive model of nonlinear systems through resolution expression on base of structure analysis expression of the fuzzy model, this fuzzy model was applied in the nonlinear model predictive control as the predictive model. Then the fuzzy linearization predictive models at k+1 sampling time, P horizon predictive output are got and conventional model predictive controller is designed. The procedure to implement this fuzzy linearization model predictive control algorithm of nonlinear system is introduced. Finally simulation test results show that the proposed fuzzy model based-on predictive control approach is a robust and effective control algorithm. Compared with the conventional DMC control approach, this approach has the advantages of less overshoot and shorter setting time etc.(6) The proposed new fuzzy controller design has been applied into temperature control of HVAC systems. After introducing energy efficiency and control of HVAC systems, the basic structure and main control loops are given. Then on base of the physical models of closed-loop control system for air-handling units, the proposed fuzzy control algorithm is used to HVAC systems, that is, flow rate of chilled water m|._chw is to control off-coil air dry boil temperature T_ao. The outstanding characteristics of this proposed fuzzy controller is that it made good use of mature technique of classical PID controller, strongly robust and simple to design. Furthermore it is very convenient to be accepted by common operators in industrial plants. And then it changes the status of the fuzzy controller that is difficult to be applied in industrial process. Finally, a conclusion is made and the future research directions are proposes in this field.