T. Matsuo

Robust stabilization of closed-loop systems by PID + Q controller

The Youla parametrization of all stabilizing compensators consists of a full-order observer-based controller and free parameters in RH8. In this paper, we derive a parametrization of all stabilizing compensators with a static output feedback term in the central part, by state-space methods instead of coprime factorization methods. Using the parametrization obtained here, we give a parametrization of stabilizing compensators based on a PID controller and point out that this parametrization can be interpreted as a PID controller with an internal model and a free parameter. We call this controller the PID+ Q controller. Moreover, we design a robust stabilizer for a closed-loop system implemented as a PID controller by selecting an appropriate free parameter.

Fuzzy adaptive identification method based on Riccati equation and its application to ball-plate control system

We propose a fuzzy adaptive identifier for SISO systems with nonlinearities in their input terms. First, we mention briefly the fuzzy basis function expansion and the fuzzy approximator and derive a fuzzy model of SISO plants with input nonlinearities. Next, an adaptation law is derived that does not require strictly positive realness. Moreover, an adaptive identifier of the fuzzy model is given by using the proposed adaptation law. Finally, we apply the fuzzy identifier to a ball-plate apparatus and investigate the performance of the proposed fuzzy identifier by MATLAB simulation and experiment.

An adaptive control system via a Riccati equation and its application to power systems

The dynamics of power systems contain the nonlinearities such as trigonometric functions and the parameter uncertainties of reactances of transmission lines caused by short circuit faults. Wang et al. proposed an adaptive nonlinear controller for a single generator connected through two parallel transmission lines to an infinite bus. They employed the feedback linearization technique and the linear adaptive control technique. Though they used the power angle, the relative speed of the generator and the active electrical power as feedback signals, the power angle is not usually available. So, we design an adaptive nonlinear controller without the power angle feedback-path for Wangs power systems model. We employ the adaptive control method with a Riccati equation in designing the power system stabilizer.

Robust transient stabilizer for power systems with superconducting magnetic energy storage unit

In this paper, the authors establish a nonlinear model of a single machine-infinite bus power system with SMES, based on the Parks electric and mechanical equations, and derive its linearized model with the structural uncertainties caused by the sinusoidal nonlinearities. Moreover, they design a quadratic transient stabilizer for power system by using descriptor-type H/sub /spl infin// control theory. Simulation results from MATLAB/SIMULINK are shown.

Robust adaptive control based on riccati equation, and its application to DC servomotor

Matsuo and Nakano (1999) proposed a parameter adjustment law that guarantees the stability of the error system that has a positive definite solution for a Riccati equation instead of the strictly positive realness condition, even if the plant has exogenous disturbances. However, the parameter adjustment law has the disadvantage that the transient response of the closed loop system has the large overshoot. In this paper, we try to improve the transient performance by introducing the imaginary axis shifting. Moreover, we apply the adaptive scheme to the position control of DC servomotors.

Neuro-Based Modularized Modeling and Its Application to Deaerator with Level Control Systems

Abstract There is an increase in requests for specifications of simulators for checkout of distributed control systems (DCSs) that leads to a great demand for the hardware-in-the-loop (HIL) system based on physical models. Then, the following problems are pointed out: (1) the more computation time is needed for building higher accurate physical models, (2) high-priced interfaces which can process a huge number of input/output (I/O) signals in combination with DCSs are needed. In addition, there is an unfamiliar problem on the transmission delays between PCs on which physical models are running and actual DCSs. To overcome these problems, it is necessary to develop a more practical and accurate simulator which can be embedded in controllers as DCS components. Furthermore, it is important to append the learning/identification function to the controllers for reducing the tuning task of adjusting physical-model-based output responses with actual ones. In this paper, a two-step procedure for modeling large-scale processes called modularized modeling procedure is presented. First, a low-dimensional modeling of each subsystem in the process is performed by using recurrent neural networks (NNs). Secondly, the NN-based identified subsystems are integrated to complete a whole model. Through an example of modeling a deaerator with level control systems, the validity of our procedure is demonstrated. The obtained model is compact enough to be embedded in each controller and is utilized as the simulator for checkout of DCSs.

Fuzzy adaptive fault detector of power systems based on Riccati equation

The dynamics of power systems contain nonlinearities such as trigonometric functions and parameter uncertainties of reactances of transmission lines caused by short circuit faults. Wang et al. (1993, 1994) employed the feedback linearization technique and proposed the switching control between the power system stabilizer (PSS) and the automatic voltage regulator (AVR) in exciting the generator. The PSS is used in the case of transmission-line faults to attain transient stability enhancement. Therefore, it is important to know when the faults occur in the transmission lines. We design an adaptive fault detector for the transmission lines of Wangs (1994) power system model. We employ the fuzzy basis function expansion and the adaptive parameter update law with a Riccati equation in designing the adaptive fault detector.

Robust stability and robust performance conditions for robot manipulators by PD+Q controller

Recently, we proposed the PID+Q controller which consists of a PID controller. The purpose of this paper is to show that we can improve the performance and stability of robot manipulators with plant uncertainties such as dynamic coupling, gravity forces, and first-order lag filters using the PID+Q controller. We discuss the nominal performance, robust stability and robust performance of the PID+Q controller and apply the PD+Q controller to a parallel link robot manipulator.