Much work has been done, concerning the adaptive algorithms to adjust filter's coefficients. Among them, Block Orthogonal Projection Algorithm (BOPA) already proposed by authors is effective one, from viewpoints of convergence speed and computational requirements. Unfortunately, the convegence characteristics of BOPA lower down, when the BOPA is applied to the important system of use in which the input signal is colored one and observed noises are added at output of unknown sysycem.
First, this paper makes it clear that the convergence characteristic of BOPA can be strongly influenced by the dispersion of singuar value distribution of input state matrix at any block, and secondly presents a block adaptive algorithm based on singular value decomposition, which can realize effective parameter estimation under the condition mentioned above. The proposed algorithm can obtain stable convegence characteristics by putting some smaller singular values of state matrix into zero. Furthermore, it is shown that the proposed algorithm is capable for application to practical systems by computer simulations.

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Fundamental structures of MRACS have not made remarkable progresses since a control system design method was advocated by V. V. Monopoli in 1974. Although various approaches have been developed and achieved successful results for parameter adjustment algorithm, the application of almost all these systems is limited to time-invariant systems. The authors have been making research on MRACS which can be applied to the systems with unpredictable parameter variations.
In this paper, a design method of the Exact Model Matching Control System (EMM) which can be applied to time-varying plants is introduced. The usefulness of the method exemired by a simulation study which is carried out on a 13th-order model of the steam temperature control systems of a thermal power plant.

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Recently the hybrid adaptive control system is actively studied. The system has a continuous-time control law and a discrete-time like adaptive law. It has some merits as follows; it is suitable to the digital control, easy to analyze the stability, and robust relative to the adaptive control having a continuous-time adaptive law. However, the usual hybrid adaptive laws need a lot of computational time because they have numerical integrations in the adaptive calculations. In order to realize a more practical hybrid adaptive system, it is important to reduce the computational time.
To overcome the problem, this paper proposes a hybrid adaptive law without any numerical integration. The law is derived from the minimization of the maximum estimation error over an interval. We show briefly the global stability of the model reference adaptive control system (MRACS) with the proposed hybrid adaptive law. The viability of the analysis is verified by simple numerical simulations. Furthermore, it is shown the superiority to a usual hybrid adaptive control system, with respect to the computational time and the convergency of both the tracking error and the parameter estimation error.

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PID control strategy has been used for several process control systems up to now. However, it is difficult to determine appropriate gains in this control algorithm. For example, excessive overshoot and long settling-time may occur if we select unsuitable gains. Therefore, it is important to determine and adjust PID control gains since control performance depends on these gains.
On the other hand, we have found that PID control system is a special case of generalized minimum variance control systems. In this paper, we construct a design method of model following control system with PID control structure based on this relation. First, we show that the usual PID control system has a pre-compensator with PID control gains. Next, in order to construct a model following adaptive control system, we insert a compensator into the PID control system and hence, we regard the pre-compensator as a reference model. For identification of system parameters we adopt the least squares algorithm. Next, we illustrate some numerical simulations to show the effectiveness of the proposed control algorithm. Finally, we apply this method to a pressure control system.

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We propose a useful adaptive control method on fuzzy control using a fuzzy associative memory system and LVQ; learning vector quantization. LVQ, which is proposed by T. Kohonen, can automatically cluster the input space on neural networks. We use LVQ to automatically create membership functions of fuzzy rules at on-line computing without teaching signals. We realize a new fuzzy adaptive control which uses three elements; (1) membership functions created by LVQ, (2) fuzzy model-type fuzzy rules, and (3) a fuzzy associative memory system. The fuzzy associative memory system is useful for fuzzy inference on this fuzzy adaptive control. The fuzzy model-type fuzzy rules are useful for this fuzzy adaptive control because of high representation faculty. On an application, we show this fuzzy adaptive control is useful for a non-linear system with variable parameters.

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A contact motion of a robotic manipulator to a workpiece is a fundamental operation which occurs frequently. In recent years, as assembly robots have been widely used, the contact force analysis and control have become more important. The contact motion is a very short time phenomenon, and if we use only a feedback control scheme, it is difficult to realize a desirable contact motion. In this paper, a new control method of the contact force by learning control is proposed. The learning control is based on an iterative operation, and is one of the feedforward controls which do not need precise values of the system parameters. Therefore it is useful for the contact force control. The feature of the proposed learning algorithm is to learn an initial state of the system, that is, an approaching velocity of the manipulator and initial values of the integral controller. Simulation shows the effectiveness of the proposed method.

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