S. Omatu

Optimal sensor location problem for a linear distributed parameter system

This paper studies an optimal sensor location problem for a linear distributed parameter system. It is assumed that a criterion for the optimal sensor location is to minimize the trace of the optimal filtering error covariance function. The existence and uniqueness theorem concerning a solution of the optimal filtering error covariance function for the pointwise observation case is considered. Then by using the existence and uniqueness theorem, the comparison theorem for the partial differential equations of Riccati type is proved. By using the theorems obtained here, the existence theorem concerning a solution of the optimal sensor location problem is proved and the necessary and sufficient conditions for optimality are derived. Finally, some numerical examples for the optimal sensor location problem are illustrated.

An application of the information theory to filtering problems

Abstract The purpose of this paper is to study the filtering problems from the viewpoint of the information theory. For a linear system it is proved that the necessary and sufficient condition for maximizing the mutual information between a state and the estimate is to minimize the entropy of the estimation error. Then we derive the Kalman-Bucy filter for both the discrete-time and the continuous-time systems by an application of the information theory. Furthermore, the approach is extended to the nonlinear dynamical systems with noisy observations and then the information structures of the optimal filter for a continuous-time nonlinear system are made clear, which has been presented as the interesting open problems by Bucy.

Linear fixed-point smoothing by using functional analysis

A new approach to the fixed-point smoothing problem for linear stochastic distributed parameter systems is proposed by using functional analysis. The number of sensor locations is assumed to be finite and the error criterion is based on the unbiased and least-squares estimations. The algorithm for an optimal fixed-point smoothing estimate is derived by using Itos stochastic calculus in Hilbert spaces. By applying the kernel theorem to these results, a family of partial differential equations for the optimal fixed-point smoothing estimate is derived. The existence and uniqueness theorems concerning the solutions for both the smoothing gain and the smoothing estimator equations are proved. Finally, usefulness of the algorithm is illustrated with a numerical example.

Towards effective neuromorphic controllers

A feedforward controller based on a multilayer neural network and several control configurations is proposed. The basic weak points of the controller are discussed and several approaches to attain better performance are investigated. These include, among other aspects, faster updating algorithms, efficient use of the available knowledge about the plant, better training procedures, hybrid configurations using nonneural control techniques, efficient definition of the structure, and initialization and prelearning of the neuromorphic controller.<<ETX>>

Self-Tuning PID Control: A Multivariable Derivation and Application

Abstract In this paper, a multivariable self-tuning controller with a proportional plus integral plus derivative (PID) structure is derived. The algorithm features a combination of the self-tuning properly in which the controller parameters are tuned automatically on-line and also the structure of a multivariable PID controller, making it more favourable to be used in the industries. The algorithm is applied to a microcomputer based multi-input multi-output (MIMO) furnace. Some experiments were conducted to observe the ability of the controller in the temperature control of MIMO furnace under set-point changes and its relative robustness as compared with a fixed tuned multivariable PID (FTMPID) controller. The experimental results prove that the controller is capable of giving a good control result for the process.

Application of generalised predictive control to a temperature control process

The authors describe the application of the generalized predictive control (GPC) algorithm to control the temperature of a microcomputer-controlled water bath system. They highlight the ability of the algorithm to cope with the variation of the process dead time through some simulations and experimental results. They also investigate the effect of changing the design parameters of GPC for this system. Verification of the effectiveness of the algorithm is concluded through comparisons made with a self-tuning controller proposed by D.W. Clarke and P.J. Gawthrop (1975, 1979). The present investigation of the capability of the GPC to control the temperature of the water bath proves that the GPC algorithm can produce a good closed-loop response for this process and is also robust enough for processes with variable time delay. It is also shown that the design parameters of GPC are very flexible and easy to choose because they are integers.<<ETX>>

Control of real-time processes using back-propagation neural networks

The authors discuss the use of appropriately trained back-propagation neural networks as physical controllers similar to conventional feedforward controllers in real-time control systems. Experiments were concluded on two process models; one was a single-input single-output water bath process, and the other a multi-input multi-output nonlinear furnace. By obtaining a set of a plants input-output patterns, the neural networks were trained to learn their inverse dynamics and then were configured as feedforward controllers to the plants. The results show that the neural network controllers perform well. The applicability of other types of neural network control schemes is discussed.<<ETX>>

Adaptive control of furnace temperature by using a microcomputer

The adaptive temperature control of a furnace is considered based on the multivariable self-tuning control (STC) theory. The main reason for adopting the multivariable STC algorithm is that the measurement data look like a random signal and the control system can be regarded as minimum-phase. The control input has been determined by using the multi-variable STC method on a microcomputer. According to the control input, solid-state relays are controlled such that the desired power can be supplied. Experimental results are presented to show the effectiveness of the present algorithm compared with the results obtained using PID (proportional plus integral plus derivative) control methods by Ziegler-Nichols and the conventional PID controller.<<ETX>>

Multivariable self-tuning controller with I-PD structure

The authors propose a multivariable self-tuning controller based on I-PD type control. First, they construct a model matching control system with I-PD structure. In a discrete-time I-PD type control system one cannot construct the model matching control system due to the time-delay element. Then, the authors introduce a precompensator and construct an I-PD type control system for the extended system. Next, they consider a self-tuning algorithm of PID (proportional plus integral plus derivative) gain matrices included in this control system. Furthermore, they propose a discrete-time reference model. Finally, they present a numerical simulation result in order to show the effectiveness of the proposed control algorithm.<<ETX>>

Improvement of the Tracking Property for the Linear Quadratic Adaptive Controller

Abstract In this paper, we propose a new algorithm to the generalized minimum variance self-tuning controller to improve the tracking property of the output to the reference signal. This algorithm includes a feedback loop of the input signal and a feedforward loop of steady state error between the output and reference signals. It is shown that the present algorithm has powerful tracking property by both some numerical simulation results and pressure control results for a real plant.