【正文】 tter than each method alone. The parative method above is perhaps a little subjective, but it does reflect some objective properties and relationships among those three methods. In the real world, one may use other ways to evaluate these methods. For example, if robustness is stressed, then it should be highly weighted when the total scores are calculated. For further descriptions of parative studies between FLC and PID, readers may refer to Boverie et al. (1991), Chao and Teng (1997), Misir et al. (1996), Mizumoto (1995), Moon (1995), and Wu and Mizumoto (1996). FIGURE 7 FLC is more robust than PID.6 CONCLUSION This paper gives parisons between fuzzy control, PID control, and advanced fuzzy control based on the experimental results of a demo model which simulates the control principle of the BR1 reactor. Fuzzy control is more robust than PID control, but with a wellcharacterized system, such as a reactor, it should be better to use a hybrid method which inherits the advantages of both methods. Furthermore, the adaptive fuzzy control is able to aid the designer in finding the fuzzy control rules, especially for systems possessing much of dynamical uncertainty. References Batur, C. and Kasparian, V. (1991), 39。should be stronger。 P(t+l)S. FIGURE 5 Any trajectory has up to four feature sections and four feature points. The related norm to guide how to change rules is the following: (i) if D(I + 1) 5 0 and DD(t + I) 0, that is, P(t) P(t + 1) 5 S, then r[i] = r[i] (ii) if D(i f 1) 0 and DD(t + 1) 0, that is, P(t +1) S and P(t + 1) P(t), then r[i] = r[i] +a, (iii) if D(t + 1) 0, that is, P(t + 1) S, then r[i] = r[i] a, where a is a step size and a = 1,2,3,4,5,6. In case (i), the fuzzy con troller makes the water level P(t f 1) closer to the set value S, therefore the behaviour of the fuzzy controller is good, no rules should be changed。s position in the rule table (or the rule data file). User[i] to represent the fuzzy control magnitude (conclusion fuzzy set) of the ith rule, and let simply where 1=NL,2=NM,3=NS,4=ZE,5=PS,6=PMY7=PL. In general, a control locus may be expressed with Fig. 5, and it can be regarded as having up to four feature sections and four feature points. For each feature part, we offer a norm to guide the regulation of the fuzzy control rules. For example, the current water level P(t) is in the feature part (I), then after the fuzzy controlling using the current control rules, we measure the water level P(t + l) at the next time which has three possibilities: P(l) P(t + 1) S。s last behaviour is judged and then the rule base is changed accordingly. In this cycle, the controller will use the new rule base and output the result to the controlled object. The behaviour of the new rule base will be judged and changed again in the next cycle. The Principle of the Adaptive Function Let D and DD represent error (the difference between the actual value and the desired value) and change in error, respectively. Let D(t) and DD(t) represent error and change in error at time t, respectively. They are two input variables. Let U be an output variable, and assume thetotal number of the rules is n, then every rule has the following form: if D is A, DD is Bi, then U is C。 Tanscheit and Scharf, 1988。 Lin et al., 1997, Procyk and Mamdani, 1979。 Qi and Chin, 1997) (mostly also generating membership functions and scale factors) and selforganizing controllers (SOC) (He er al., 1993。 Wang and Mendel, 1992). One problem of the sourceable method is that it depends strictly on the source which will be transformed into rules. In the case that the source is noisy, then the rules might be biased. Another problem of the sourceable method is that it is usually nonadaptive, ., all the rules are fixed, therefore it cannot perform well under a dynamic environment. The nonsource able methods are sourcefree and they produce and choose rules according to a performance measurement of the controller, such as genetic algorithms (GA) (Karr, 1991。 Lin ez al., 1995。 Kosko, 1992。 Halgamuge and Glesner, 1994。 Tonshoff and Walter, 1994。 Kd: the differential parameter and Kd = Td where Td is the differential time. In practice, a discrete form of the above formula is used where T, is the sample period. Figure 3 shows a result of the PID control,where PB= l5%, Ti=30s, Td= 10s. In view of this figure, the PID control is very stable (very smooth in steady states), and has quick responses too, but with visible overshoots. 4 ADVANCED FUZZY CONTROL The kernel part of the fuzzy logic control is the fuzzy rule base with linguistic terms, though the membership functions and scale factors also have an important effect on the fuzzy logic controller. There are some papers which discuss how to adjust membership functions and/or scale factors (Batur and Kasparian, 1991。 Kp: the proportional parameter and Kp = (1IPB) x loo%, where PB is the proportional band。 1996a,b), and singletons are for output variables (Ornron, 1992). Each fuzzy controller has one rule base which contains 49 fuzzy control rules. The its rule can be represented as the following form: if D is Ai and DD is Bi, then VL (or VS) is Ci where A, Bi, and Ci are fuzzy linguistical values, such as NL, PS, and so on. The above rule is sometimes abbreviated as (Ai, Bi : Ci). Figure 2 shows a control effect of a synthetic control process. It first goes up from 0 to 20cm then keeps on at 20 an, next drops down from 20 to 10 cm and finally keeps on at 10 cm. In view of this figure, we know that the fuzzy control has quick responses (quickly approaching the set v