In this paper, a design method of MRACS (Model Reference Adaptive Control System) is propose& for continuous time plants in the presence of slowly-varying disturbance, by which it is possible to make the output error arbitrarily small. Considered that the control law of MRACS is easy to be realized by digital controller, the structure of this paper is composed as follows : First, the continuous time system is transformed to discrete time system by using sampler and zero-order holder, where the effect of disturbance on the plant output is described by a convolution integral. Next, in order to reduce the effect of disturbance, an integral precompensator is introduced to the sampled system, and an adaptive adjustment law with dead-zone is utilized. as analyzed theoretically, for any reference signals this reaches the results that if a small sampling period is chosen, the influence of disturbance on output can be controlled small. Finally, that the control system is stable and that the signals of the system are bounded are proved. The effectiveness of the proposed method is illustrated by computer simulations.
A hierarchical structure for motion planning and learning control of a biped locomotive robot is introduced. At the upper level of the system, the motion of the center of gravity of the robot is simulated by that of an inverted pendulum. This enables us to compute and predict the position of the center of gravity and also the landing position of supporting toe for next steps. In the second level, in order to determine the positions of other joints from the position of the center of gravity, and of the two toes, we have used an approach known as Hopfield model. However, the inverted pendulum model proposed does not exactly reflect on the actual movement of the robot and there may be some errors in the scheme of neurocomputing. To cope with such situations, the reference input for the position of the center of gravity of the robot is compensated by a learning function of a multi-layered neural network. The system provides an autonomous motion planning scheme for the biped locomotive robots, which is able to cope with, to some extent, the change of walking pattern. Simulation results showed the effectiveness of the proposed method.
To apply knowledge-based methods to industrial process control systems, we developed a real-time expert shell : ERIC (Extended Rule-based system for Intelligent Control). Its knowledge-base consists of working memory and rule-base. To present a process structure in working memory, we support a link description in frame-type data. The rule-base has a module structure of rule sets. Each rule set is a group of boolian or fuzzy logic rules. Therefore, the rule-base can express plant operation in various situations totally. In this paper, we present the structure of internal data for knowledge-base in ERIC. And we explain an integrated inference procedure for boolian logic rules and fuzzy logic rules based on the structure of internal data.
This paper considers the use of an adaptive feedforward controller for both a speed control system and a positioning system with a DC servo motor. The parameters of the feedforward controller are adjusted by an adaptation algorithm, the objective of which is to make the feedforward controller a dynamic inverse of the plant. Moreover, an adaptive disturbance compensator is used, and its parameter is adjusted by the algorithm. The error between the desired output and actual output is used as the adaptation error signal as well as the input to the nonadaptive feedback controller. After the adaptive process is completed, the input to the motor is no longer supplied by the feedback controller but by the feedforward controller. The stability of the proposed adaptive control systems is analyzed and experimentally verified.
This paper describes a multivariable algorithm for controlling the flatness of a multi-high rolling mill with actuators of crown control, lateral shift, and tilting rolling. First, the flatness control problem is formulated as a Linear-Quadratic control problem and the control law is derived. Second, to ensure the control constraints are satisfied, a practical flatness control algorithm is proposed. A non-interactive control algorithm is also proposed to compensate for the thickness disturbance caused by the flatness control. Finally, field test results are presented showing the performance of the proposed control algorithms.