【正文】 em shown in Fig. 3, the plant model is a secondorder and type system with the following transfer function: (2)Where K = 16, = 1, and= . In our simulation experiments, we use the discrete simulation method, the results would be slightly different from that of a continuous system, the sampling time of the system is set to be s. For the fuzzy controller, the fuzzy subsets of e and d are defined as shown in Fig. 4. Their cores The fuzzy control rules are represented as Table 1. Fig. 5 demonstrates the simulation result of step response of the fuzzy control system with a Pl fc. We can see that the steadystate error of the control system bees zero, but when the integration factor fl is small, the system39。 Adaptive control 1. Introduction Among various inference methods used in the fuzzy controller found in literatures , the most widely used ones in practice are the Mamdani method proposed by Mamdani and his associates who adopted the Minmax positional rule of inference based on an interpretation of a control rule as a conjunction of the antecedent and consequent, and the productsum method proposed by Mizumoto who suggested to introduce the product and arithmetic mean aggregation operators to replace the logical AND (minimum) and OR (maximum) calculations in the Minmax positional rule of inference. In the algorithm of a fuzzy controller, the fuzzy function calculation is also a plicated and time consuming task. Tagagi and Sugeno proposed a crisp type model in which the consequent parts of the fuzzy control rules are crisp functional representation or crisp real numbers in the simplified case instead of fuzzy sets . With this model of crisp real number output, the fuzzy set of the inference consequence will be a discrete fuzzy set with a finite number of points, this can greatly simplify the fuzzy function algorithm. Both the Minmax method and the productsum method are often applied with the crisp output model in a mixed manner. Especially the mixed productsum crisp model has a fine performance and the simplest algorithm that is very easy to be implemented in hardware system and converted into a fuzzy neural network model. In this paper, we will take account of the productsum crisp type fuzzy controller. 2. PID type fuzzy controller structure As illustrated in previous sections, the PD function approximately behaves like a parameter timevarying PD controller. Since the mathematical models of most industrial process systems are of type, obviously there would exist an steadystate error if they are controlled by this kind of fuzzy controller. This characteristic has been stated in the brief review of the PID controller in the previous section. If we want to eliminate the steadystate error of the control system, we can imagine to substitute the input (the change rate of error or the derivative of error) of the fuzzy controller with the integration of error. This will result the fuzzy controller behaving like a parameter timevarying PI controller, thus the steadystate error is expelled by the integration action. However, a PI type fuzzy controller will have a slow rise time if the P parameters are chosen small, and have a large overshoot if the P or I parameters are chosen large. So there may be the time when one wants to introduce not only the integration control but the derivative control to the fuzzy control system, because the derivative control can reduce the overshoot of the system39。 目 錄Part 1 PID type fuzzy controller and parameters adaptive method 1Part 2 Application of self adaptation fuzzyPID control for main steam temperature control system in power station 7Part 3 Neurofuzzy generalized predictive control of boiler steam temperature ………13Part 4 為Part3譯文：鍋爐蒸汽溫度模糊神經(jīng)網(wǎng)絡的廣義預測控制2126 / 27Part 1 PID type fuzzy controller and Parameters adaptive method Wu zhi QIAO, Masaharu Mizumoto Abstract: The authors of this paper try to analyze the dynamic behavior of the productsum crisp type fuzzy controller, revealing that this type of fuzzy controller behaves approximately like a PD controller that may yield steadystate error for the control system. By relating to the conventional PID control theory, we propose a new fuzzy controller structure, namely PID type fuzzy controller which retains the characteristics similar to the conventional PID controller. In order to improve further the performance of the fuzzy controller, we work out a method to tune the parameters of the PID type fuzzy controller on line, producing a parameter adaptive fuzzy controller. Simulation experiments are made to demonstrate the fine performance of these novel fuzzy controller structures. Keywords: Fuzzy controller。 PID control。s response so as to improve the control performance. Of course this can be realized by designing a fuzzy controller with three inputs, error, the change rate of error and the integration of error. However, these methods will be hard to implement in practice because of the difficulty in constructing fuzzy control rules. Usually fuzzy control rules are constructed by summarizing the manual control experience of an operator who has been controlling the industrial process skillfully and successfully. The operator intuitively regulates the executor to control the process by watching the error and the change rate of the error between the system39。s response is slow, and when it is too large, there is a high overshoot and serious oscillation. Therefore, we may want to introduce the derivative control law into the fuzzy controller to overe the overshoot and instability. We propose a controller structure that simply connects the PD type and the PI type fuzzy controller together in parallel. We have the equivalent structure of that by connecting a PI device with the basic fuzzy controller serially as shown in . Where ~ is the weight on PD type fuzzy controller and fi i