Self-tuning Control Scheme Research Articles

Fuzzy self-tuning of PID controllers

Abstract This paper presents a novel fuzzy self-tuning PID control scheme for regulating industrial processes. The essential idea of the scheme is to parameterize a Ziegler-Nichols-like tuning formula by a single parameter α, then to use an on-line fuzzy inference mechanism to self-tune the parameter. The fuzzy tuning mechanism, with process output error and error rate as its inputs, adjusts α in such a way that it speeds up the convergence of the process output to a set-point y r , and slows down the divergence trend of the output from y r . A comparative simulation study on various processes, including a second-order process, processes with long dead-time and non-minimum phase processes, shows that the performance of the new scheme improves considerably, in terms of set-point and load disturbance responses, over the PID controllers well-tuned using both the classical Ziegler-Nichols formula and the more recent Refined Ziegler-Nichols formula.

Adaptive compliant control for an inspection robot system

A robotic sub-sea inspection system is described. Emphasis is placed on how to use self-tuning adaptive control strategies to control the robot compliant motion. Experiments on a PUMA robotic manipulator have shown that adaptive control can greatly improve contact quality and provide much better tracking ability in an unknown environment. Also adaptive controllers can make the approach time shorter than ordinary fixed gain controllers. A coupled dynamics concept for surface contact, which is typical in an inspection task, is introduced. Implementation of an adaptive controller in VAL-2 is detailed. A supervisory control system, capable of incorporating real-time compliant motion control and written in MODULA-2, is also described.

On Self-Tuning Control of Tokamak Thermokinetics

Adaptive control techniques can be applied to online gain tuning of tokamak thermokinetics. Here, a self-tuning control scheme is explored for both the plasma profile and power control. The distributed parameter system of the flux-surface-averaged one-dimensional transport equations is discretized by a nonlinear variational procedure. A finite-dimensional multiple-input/multiple-output control algorithm is derived using the linearized equations. A particular class of nonlinear three-parameter profiles is used for plasma density, temperature, and deuterium fraction profiles. Feedback gains are determined using a simplified minimum variance control law of self-tuning control. In the examples, normal multiple-output specifications for the plasma profile parameters for the density and power control are shown to be controllable by multiple-particle inputs alone.

Application of a self-tuning control scheme to a power system with multi-mode oscillations

A self-tuning control scheme is proposed and its effectiveness for the purpose of enhancing the damping of multi-mode oscillations in a power system is examined. Simulation results show that the proposed self-tuning stabilizer is suitable for and can cooperate with conventional stabilizers in providing enhanced performance to a power system with multi-mode oscillations.

An Implicit Quadratic H∞ Self-Tuning Controller

An implicit H∞ self-tuning control scheme is presented. Costing of the system error and control signals is achieved using a dynamic cost function. The H∞ optimal solution to this problem is obtained using a recursive least square identification algorithm. The simple procedure for calculating the controller, without solving any diophantine equations, make this method particularly suitable for self-tuning control applications.

A self-tuning control scheme for disk file servos

The authors present a self-tuning control scheme for disk file track-following servos. The scheme augments an existing fixed controller to recover a specified nominal performance in the presence of unmodeled dynamics. The compensator used in each disk file is individually tuned by inserting an add-on adjustable compensator to the nominal compensator, utilizing a linear fractional transformation (LFT) known as the Youla-Kucera parametrization. Similarly, the difference between the actual disk drive dynamics and the nominal model is characterized by an unknown transfer function, using the Youla-Kucera parametrization applied to the plant rather than the compensator. The algorithm consists of two steps: identification of the unmodeled dynamics transfer function, followed by the synthesis of the add-on compensator, utilizing the linear quadratic Gaussian (LQG) compensator methodology.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Design of Self-Tuning Controllers for Industrial Plant

Self-tuning control is now rapidly maturing to provide a realistic option for the control of a wide range of industrial systems. This is particularly evident in the field of proccss control where system lime constants arc relatively slow and assumptions on slowly varying plant dynamics permits the use of such adaptive schemes. However, the application of self-tuning techniques to systems exhibiting fast dynamic behaviour and/or severe non-linear characteristics appears to be somewhat limited and it is this, coupled with recent advances in computer technology, that has provided the impetus for much interest and on-going research. By adopting a ease study approach, the paper gives detailed consideration to the application of self-tuning control to an engine test cell which exhibits fast dynamic behaviour and to a high temperature gas fired heat treatment proccss which exhibits severe plant non-linearities. Drawing from these two industrial applications, some of the problems encountered when implementing standard self-tuning control schemes arc highlighted and the need for the tailoring and refining of the algorithms for specific applications is emphasised.

A controller for regulation of mean arterial blood pressure using optimum nitroprusside infusion rate

Automation of drug delivery for control of mean arterial blood pressure is highly desirable in a number of clinical applications. An integrating self-tuning control strategy for control of mean arterial blood pressure using sodium nitroprusside is presented. Next to robust performance, the most attractive feature of the controller is its capability to optimize the quantity of infused medication without introducing a bias in the blood pressure level; a problem that existed in some of the other adaptive control strategies that have been proposed previously. Further, the controller design requires only the knowledge of the pure delay and the order of the transfer function describing the patient's response to the medication; it does not require that the entire transfer function be specified. The derivation of the controller is not based on the patient response to sodium nitroprusside; indeed, it is a general adaptive control strategy for control of systems with transport delay. The controller performs robustly in the presence of variations in the patient response and successfully controls the pressure at the desired level. The ability of this strategy to reduce the amount of infused medication makes it potentially attractive for use in clinical applications, as large doses or long term use of sodium nitroprusside can adversely affect central nervous system and hematopoietic tissues.

Hybrid state-space self-tuning control using dual-rate sampling

This paper presents a hybrid state-space self-tuning control scheme using dual-rate sampling for suboptimal digital adaptive control of linear time-invariant continuous-time multivariable stochastic systems with unknown parameters. An equivalent fast-rate discrete-time state-space innovation model (with estimated states) of the continuous-time system is constructed by using the estimated system parameters and Kalman gain. To utilize the existing optimal regional-pole assignment method developed in the continuous-time domain, the constructed fast-rate discrete-time model is converted into an equivalent continuous-time model for the development of a state-feedback optimal control law with pole placement in a specific region. The developed analog optimal control law is then converted into an equivalent pseudo-slow-rate digital control law via the proposed digital redesign technique, which can be realized via slow-rate digital electronics. The proposed method enables the development of a digitally implementable advanced control algorithm for digital adaptive control of continuous-time multivariable stochastic systems which may be unstable and/or have nonminimum phase.

Logical covariance matrix reset in self-tuning control

The paper investigates the application of a state-space self-tuning pole-placement strategy to a non-linear hydraulic test rig designed specifically for evaluating the effectiveness of self-tuning control schemes. The test rig is representative of a wide range of industrial systems exhibiting both non-linear characteristics and fast dynamic behaviour, such factors, until recently, prohibiting the effective use of linear self-tuning schemes. In recognition of this shortfall, a novel technique for enhancing the covariance matrix update stage within the parameter estimation algorithm of a self-tuning controller is proposed. The technique, termed logical covariance matrix reset, requires only a minimum of computational overhead and is shown to give rise to a significantly improved parameter tracking ability by making more effective use of potential information available to the estimation algorithm at set-point change. Results of simulation studies involving the application of the logical resetting technique to the hydraulic test rig are presented when the rig is subjected to slow variation in feed flow and sudden variation in load.

Experimental evaluation of a self-tuning controller

A self-tuning control (STC) scheme is examined in real time by comparing its performance with that of two other control schemes. Experimental results show that the controller is superior to a well-known conventional self-tuning controller and a proportional-plus-integral (PI) self-tuning controller whose parameters are selected on the basis of a pole assignment method. The control scheme can be extended to multi-input multi-output systems. >

Development and implementation of self-tuning tracking controller for DC motors

A self-tuning control strategy for tracking applications of DC drives is proposed. By this technique, the rotor speed and/or position of a DC motor can follow any arbitrarily selected track (or trajectory). The developed control strategy is adaptive and robust, i.e. insensitive to system variations, such as load changes, noise, and disturbances. This results in what is known as a high-performance drive system, which is required in applications such as robotics, actuation, and manipulation. The developed drive system was implemented and tested in the laboratory. The system was examined under various uncertainties such as load variations, noisy measurements, and field disturbances. All lab tests confirm the accuracy and the robustness of the proposed tracking controller. It is shown how such a powerful controller can be implemented by off-the-shelf hardware and software instead of by specially designed items that are often time consuming and costly. >

Self-tuning control scheme for stability enhancement of multimachine power systems

A decentralised self-tuning control scheme for the stability enhancement of multimachine power systems is presented. The dynamic performance of each machine in the study multimachine sytem is described by a discrete-time model for the purpose of stabiliser design. The parameters of this model are estimated once every sample, depending on the measured input-output data of the local machine. Each stabiliser is designed using this discrete-time model and a performance index which enhances the damping characteristic of the overall multimachine system is defined. Nonlinear simulation tests show that the proposed self-tuning control scheme is suitable for the stability enhancement of two multimachine power systems, both for small and large disturbances and for different operating conditions. It is also demonstrated that the proposed self- tuning stabiliser can effectively co-operate with the conventional fixed parameter stabiliser, to improve the system stability.

Power system stability improvement with multivariable self-tuning control

A multivariable self-tuning adaptive control scheme is presented. This scheme is of a decentralized nature and is implemented locally for individual generating units. A discrete multivariable autoregressive-moving-average (ARMA) model is developed to represent a generating unit. The recursive-least-squares (RLS) estimation algorithm with variable-forgetting factor and the generalized-minimum-variance control technique are utilized to synthesize the local controllers. A dynamic goal-point-generating model is introduced to provide varying goal point for the local controller which leads the subsystem output to its equilibrium gradually. Extensive simulations are performed on the IEEE ten-machine test system. The results show that the proposed multivariable adaptive control scheme is effective in damping the severe oscillations after large disturbances as well as improving the system dynamics under small oscillations and is better than the conventional power system stabilizer (PSS) method. The controller demonstrates robustness and is compatible with the existing conventional controllers in multimachine systems. >

Adaptive Control Schemes for DC Servomotors

The DC motors used in robotics and other industrial applications are submitted to frequent environment changes. As a consequence the motor model has time-varying parameters resulting in a poor performance when nonadaptive linear controllers are used. In this paper, two adaptive schemes for the position control of DC servomotors have been studied. The first scheme is based on self-tuning technique which uses pole-assignment by linear state feedback as the control strategy The second scheme uses adaptive feedforward in addition to pole-assigning linear state feedback. Experimental results show that a simple self-tuning control scheme for DC servomotors is a better solution to low cost automation problems in robotics and other industrial applications.The results obtained show that the adaptive scheme proposed by Kelly [1] seems to offer no great benefit, rather it seems to increase the amount of overshoot. Standard schemes such as selftuning control provide cost-effective solution to the control problem.The paper is organized as follows: a DC motor model and linear state feedback controller are discussed in Section II. Section III deals with selftuning regulator, whereas adaptive feedforward with linear state feedback is dealt with in Section IV. Experimental results on the DC motor control are given in Section V and some conclusions are drawn in Section VI.

Intelligent controls for building services

The paper reviews the 'state of the art' in the design and application of adaptive and self-tuning controls. Their role in the hierarchy of control loops needed to operate modern building services is described, and an introduction to adaptive and self-tuning control techniques is given. The performance of a self-adaptive optimum-start controller, a rule-based selforganising regulator for three-port valves and a self-tuning control scheme for cooling coils are discussed in detail. The case studies are used to demonstrate the potential advantages and practical problems associated with the implementation of intelligent controls. The authors conclude that future control strategies are most likely to be based on a combination of 'rule-based' and analytical techniques.

A Robust Self-tuning Power System Stabilizer

A robust self-tuning control scheme for stability enhancement of power systems is presented in this paper. An integrator driven by the tracking error is included into the control loop such that the closed-loop system exhibits consistent performance with fast transient recovery under large load changes and over a wide range of operation conditions. Results from nonlinear simulation studies confirm the effectiveness of the proposed control scheme for single-machine and multimachine power systems.

Explicit self-tuning control for a class of non-linear systems

This paper is concerned with adaptive control of discrete-time linear systems having a polynomial input non-linearity. A new self-tuning control scheme is presented to implement adaptively the suboptimal control law derived in the previous work. By the on-line adaptation of the compensators P( q −1) and R( q −1), the difficulty of cautiously choosing P( q −1) and R( q −1) a priori to ensure the stability of the closed-loop system is thus circumvented. A simple locally convergent self-tuning algorithm and a globally convergent improved self-tuning algorithm are given which provide an efficient way to the control of this class of non-linear systems. Simulation results verify the effectiveness of the proposed algorithm.

Application of intelligent control of time-delay processes to the mix moisture control system in sinterplants

This paper presents a intelligent controller based on the polezero placement self-tuning control strategy, which aims at controlling the mix moisture in sinterplants.

Self-tuning Control for a Class of Nonlinear Systems with On-line Compensator Adaptation

This paper is concerned with adaptive control of discrete-time linear systems having a polynomial input nonlinearity. A new self-tuning control scheme is presented to adaptively implement the suboptimal control law derived in the previous work. By the on-line adaptation of the compensators P(q-1) and R(q-1) the difficulty of cautiously choosing P(q-1) and R(q-1) a priori to ensure the stability of the closed loop system is thus circumvented. A simple locally convergent self-tuning algorithm and a globally convergent improved self-tuning algorithm are given which provide an efficient way to the control of this class of nonlinear systems. Simulation results verify the effectiveness of the proposed algorithm.