In this paper we propose a new concise mathematical model of interconnection, so that sectionwise defined functions may be easily united into a single function defined in the whole region. The basis function of this model generates an approximation of a partition of unity and is represented in terms of an analytic function whose limit becomes a block-pulse function. The nonlinear control system is linearized sectionwise so as to be able to design the linear controllers. These controllers are united into a single controller by using the basis function mentioned above. The resulting controller has a compact (i. e. nonsectionwise defined analytic) form which enables easy implementation. Digital computer simulations show that the new controller improves the performances of nonlinear systems with high nonlinearity.
A new learning servo system with a multirate sampling learning compensator is proposed. It reduces memory size remarkably, compared with the conventional learning servo system, and is especially suitable for a commercial application. The developed learning compensator includes two samplers and a digital filter in the input part of it. The first sampler and the digital filter operate at short sampling intervals. The second sampler samples the output of the digital filter at long sampling intervals. The proposed multirate sampling learning servo system achieves excellent robustness to disturbances by using small memory size, and is verified by applying it to a speed and phase control system of a DC motor.
It is known that a servo system for step functions can be designed without using an integral compensator if the plant parameters are known accurately. Therefore, it would be desirable that an integral compensator is introduced only to obtain robust tracking ability while keeping the response for the nominal plant unchanged. From this viewpoint, this paper proposes a new method for designing a robust servo system which is optimal with respect to a quadratic performance index. The method uses the solution of a Riccati equation of order n, while many existing methods use the solution of a Riccati equation of order n + m, where n and m denote the order of the plant and the number of the plant inputs, respectively. It is also shown that, in our method, the circle condition can be always achieved at the plant input by suitably utilizing the design freedom, without changing the nominal response of the closed-loop system. Based on this fact on the circle condition, the connection with one of the existing design methods is also discussed.
This paper proposes a new simulation approach to detect the bottleneck of a production system automatically and improve the productivity to the target level after a few iterations. This new method is applied to a large production system which produces various kinds of pipes. The best capacity plan with minimum investment is obtained. It is concluded that this new method is more practical and effective to redesign the facility capacity compared with the theoretical and/or analytical methods.
In this paper, we propose the concept of distributed representations of fuzzy if-then rules and apply it to classification problems. The distributed representations are implemented by superimposing many fuzzy rules corresponding to different fuzzy partitions of an input space. First we compare the distributed representations with the conventional fuzzy rules which can be viewed as local representations. Next we propose an algorithm to construct fuzzy rules automatically from given data for two-group discriminant problems. The proposed method requires neither adjustment nor learning of membership functions. We also propose a fuzzy reasoning method using the distributed fuzzy rules. The proposed method are illustrated by numerical examples. Last we demonstrate the classification power of the distributed fuzzy rules using the classification problem of iris in Fisher.