Multivariable Self-tuning Controller Research Articles

Adaptive modal vibration control for smart flexible beam with two piezoelectric actuators by multivariable self-tuning control

It has been popular for decades that the vibrations of space structures are suppressed with smart actuators. However, the higher mode vibrations are often motivated when a control strategy is applied to attenuate the vibration for the smart structures. Moreover, if the multi-mode vibration of a smart structure is suppressed with multi-actuators, a proper multivariable control law will be adopted to solve the coupling problem caused by the multi-actuators of the smart structure. Therefore, in the paper, a decoupling technique for two modal vibrations of a smart flexible beam with two piezoelectric patches is adopted by adaptive control. The proposed control law is designed with a multivariable minimum variance self-tuning control. Considering the first two orders of modal vibrations, two piezoelectric patches are configured on the flexible beam according to the strain of the first two orders of modal vibrations along the longitudinal direction of the beam. A dynamical model for the flexible beam with two piezoelectric actuators is constructed by the mode superposition method. With the dynamical model, simulations are implemented to suppress the free vibration of the flexible beam. Moreover, experiments are carried out to verify the effectiveness of the multivariable minimum variance self-tuning control for vibration suppression of the flexible structure. The control results clearly show that the free vibration amplitude of the cantilevered beam with two control voltages applied to the two piezoelectric patches is less than that with one control voltage applied to the first piezoelectric actuator. Thus, multivariable minimum variance self-tuning control is a more efficient approach for suppressing multimodal vibration for a smart flexible beam with two piezoelectric actuators compared with the conventional velocity feedback control.

Multivariable Self-Tuning Feedback Linearization Controller for Power Oscillation Damping

The objective of this brief is to design a measurement-based self-tuning controller, which does not rely on accurate models and deals with nonlinearities in system response. A special form of neural network (NN) model called feedback linearizable NN (FLNN) compatible with feedback linearization technique is proposed for representation of nonlinear power systems behavior. Levenberg-Marquardt (LM) is applied in batch mode to improve the model estimation. A time-varying feedback linearization controller (FBLC) is employed in conjunction with the FLNN-LM estimator to generate the control signal. Validation of the performance of proposed algorithm is done through the modeling and simulating both normal and heavy loading of transmission lines, when the nonlinearities are pronounced. Case studies on a large-scale 16-machine five-area power system are reported for different power flow scenarios, to prove the superiority of proposed scheme against a conventional model-based controller. A coefficient vector Λ for FBLC is derived and used online at each time instant, to enhance the damping performance of controller.

Self-tuning Control based on Discrete-time Sliding Mode with Applications

AbstractA multi-variable self-tuning controller is presented in this paper using the idea of discrete-time sliding mode. It was successfully applied to the engine start control on an automotive V6 engine model provided from SICE in 2006 of which the order was over 50. The requirements of the control were to suppress the engine speed overshoot just after the ignition and to regulate the engine speed 650 ± 50(rpm) within 1.5(s). The control results show the effectiveness of the proposed control.

Multivariable Self-Tuning Control of a Turbine Generator System

Results from a collaborative research and development program devoted to turbine generator (TG) control are described. Digital self-tuning excitation controllers were designed for a generator during the initial phase of the project, with the design subsequently extended to cover multivariable control of the TG, which is the topic of this paper. Simulations and tests on a laboratory-scale machine have been accomplished successfully and a prototype multivariable self-tuning controller has been built. A set of typical results is given, covering responses to fault conditions of the power system. The multivariable self-tuning controller is shown to have good potential for commercial use for the TG.

Multivariable Adaptive Control of Synchronous Machines in a Multimachine Power System

A multivariable self-tuning adaptive control scheme used to enhance power system stability in a multimachine environment is presented. The controller is implemented locally for individual generators with supplementary stabilizing signals through the AVR and governor. A discrete multivariable state space model is developed to represent the generator. The recursive subspace identification method based on the projection approximation subspace tracking approach is employed to update generator model parameters online. A generalized predictive control strategy with constraints on the control signals is used. Simulations studies carried out on a five-machine power system without infinite bus show that the proposed multivariable adaptive controller is effective in damping both local and interarea mode oscillations under small as well as large disturbances. The self-coordinating ability of the adaptive controller with the existing conventional controllers is also demonstrated.

Design and experimental evaluation of a multivariable self-tuning PID controller

In the paper, a new multivariable self-tuning PID controller design scheme is proposed which has a static matrix precompensator to reduce the interaction terms of the process transfer function matrix adjusted by an online estimator. The p×p precompensated multivariate system is then controlled via ‘p’ univariate self-tuning PID controllers whose parameters are adjusted by a second identifier placed around the precompensated plant. The PID parameters are calculated online based on the relationship between the PID and generalised minimum variance control laws. The scheme is experimentally evaluated on a 2 × 2 level plus temperature control system. Experimental results illustrate the effectiveness of this scheme.

Design weighting parameter tuning of multivariable self-tuning controllers

In this paper, we present the method for on-line tuning of a design weighting polynomial parameters of a multivariable self-tuning controller which adapts to changes in the higher-order nonminimum phase system parameters with time delays and noises. The algorithm effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins–Monro algorithm at the stage of optimizing the design weighting polynomial parameters of the controller. The proposed method is simple and effective compared with the pole restriction method. The computer simulation results are presented to adapt the higher-order multivariable system with nonminimum phase and with changeable system parameters.

Multivariable adaptive decoupling temperature control of a thermo-analyser

This paper deals with multivariable self-tuning temperature control of a thermo-analyser. The decoupling method is used and the parameters of two independent controllers are derived using the deadbeat approach with reference signal tracking. The recursive least-squares method with LD decomposition and directional forgetting is used to estimate the parameters of the process model. This multivariable controller has been designed specially for technological processes where process output variables without overshoot are desired and reference signals are changed with steps and linear increasing.

SELF-TUNING ADAPTIVE CONTROL OF FORCED VIBRATION IN ROTOR SYSTEMS USING AN ACTIVE JOURNAL BEARING

A multivariable self-tuning adaptive controller was developed to control forced vibration of rotor systems incorporating a new type of active journal bearing. The adaptive control algorithm enables the controller to cope with non-linearity, parameter variation with time and parameter uncertainty in rotor-bearing systems. The control algorithm requires no pre-knowledge of system parameters and imbalance distribution. This is especially significant in applications with complex rotor systems supported on fluid-film bearings. The controller enables the system to retain a desired equilibrium position and at the same time reduces vibration about the equilibrium position. A pre-identification procedure was introduced before the self-tuning loop to determine the order of the controller, the time delay of the system, and to accelerate the controller tuning process. The control algorithm is presented quite generally. It may be used as a general procedure in the applications of active vibration control in rotating machinery.

Multivariable self-tuning temperature control for plastic injection molding process

This paper develops a multivariable self-tuning predictive control for improving set-point tracking performance, disturbance rejection, and robustness of a temperature control system for an extruder barrel in a plastic injection molding process. The stochastic discrete-time multivariable mathematical model is built and its unknown system parameters are identified by using the recursive least-squares estimation method. The multivariable predictive control is derived based on the minimization of a generalized predictive performance criterion. A real-time self-tuning control algorithm is proposed and then implemented by using a digital signal processor (DSP) TMS320C31 from Texas Instruments. Experimental results are used to show the feasibility and effectiveness of the proposed method.

A comparison between robust and adaptive hybrid position/force control schemes for hydraulic underwater manipulators

Tele-operated hydrauilic underwater manipulators are commonly used to perform remote underwater intervention tasks such as weld inspection or mating of connectors Automation of these tasks to use tele-assitance requires a suitable hybrid position/force control scheme, to specify simultaneously the robot motion and contact forces. Classical linear control does not allow for the highly non-linear and time varying robot dynamics in this situation. Adequate control performance requires more advanced controllers. This paper presents and compares two different advanced hybrid control algorithms. The first is based on a modified Variable Structure Control (VSC-HF) with a virtual environment, and the second uses a multivariable self-tuning adaptive controller. A direct comparison of the two proposed control schemes is performed in simulation, using a model of the dynamics of a hydraulic underwater manipulator (a Slingsby TA9) in contact with a surface. These comparisons look at the performance of the controllers under a wide variety of operating conditions, including different environment stiffnesses, positions of the robot and dynamic parameters. Conclusions are drawn based on the relative performance of each controller and on the practicalities of the proposed schemes.

Geometry and Thermal Regulation of GMA Welding via Conventional and Neural Adaptive Control

This paper investigates the application of conventional and neural adaptive control schemes to Gas Metal Arc (GMA) welding. The goal is to produce welds of high quality and strength. This can be achieved through proper on-line control of the geometrical and thermal characteristics of the process. The welding process is variant in time and strongly nonlinear, and is subject to many defects due to improper regulation of parameters like arc voltage and current, or travel speed of the torch. Adaptive control is thus naturally a very good candidate for the regulation of the geometrical and thermal characteristics of the welding process. Here four adaptive control techniques are reviewed and tested, namely: model reference adaptive control (MRAC), pseudogradient adaptive control (PAC), multivariable self-tuning adaptive control (STC), and neural adaptive control (NAC). Extensive numerical results are provided, together with a discussion of the relative merits and limitations of the above techniques.

A Simple Pole-Assignment Scheme for Designing Multivariable Self-Tuning Controllers

Lots of self-tuning control schemes have been proposed for unknown parameter systems. Among them, pole-assignment schemes have been widely used for unknown time delay systems. They are usually classified into two methods, the implicit and the explicit methods according to how to identify the parameters. The latter has an advantage to design a control scheme by taking account of the stability margin and control performance. However, it involves a considerably computational burden to solve a Diophantine equation. A simple scheme is proposed in this paper, which can construct a self-tuning pole-assignment control system, while taking account of the stability margin and control performance without solving the Diophantine equation.

Self-tuning PID control: A multivariable derivation and application

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 property, in which the controller parameters are tuned automatically on-line, and also the structure of a multivariable PID controller, making it more favourable for use in industry. The algorithm is applied to a microcomputer based multi-input multi-output (MIMO) furnace. Some experiments are 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.

048 Multivariable self-tuning control of a paper machine: Li Qing-Quan, Zhang Wei-Cun, Chen Chang-Xiang, pp 239–242

048 Multivariable self-tuning control of a paper machine: Li Qing-Quan, Zhang Wei-Cun, Chen Chang-Xiang, pp 239–242

GH ∞ self-tuning control for multivariable systems

A H ∞ self-tuning controller which is based on the generalized H ∞ cost-function is described that includes robust design features. The GH ∞ control law is simple to compute and is suitable for multivariable self-tuning control applications. The simplifications achieved in the controller structure which are necessary for use in self-tuning, depend upon the assumptions made on system and cost-weighting models. The cost-function weights are assumed to ensure a system matrix can be factorized into a particular form. The control law is coupled to an extended least squares identification algorithm. The GH ∞ control law may be designed to be insensitive to unmodelled dynamics and the self-tuning action provides adaption to parametric uncertainty.

MULTIRATE MULTIVARIABLE SELF-TUNING CONTROL OF DISTILLATION COLUMNS USING KALMAN PREDICTOR

Abstract A novel method for designing Kalman Predictor (KP) based multivariable self-tuning controllers for Multi Input Multi Output (MIMO) systems has been proposed, and also applied to the control of two distillation columns. The objective is to maintain constant terminal compositions despite disturbances entering the system even when the controlled variables are not measured at the same sampling rate. The KP generates minimum variance estimates of the output variables. Simulation results show that KP based multirate multivariable self-tuning controller exhibits better performance than the earlier reported multirate controller for set point tracking, even in the presence of nonstationary disturbances. KP based self-tuning controller is, therefore, suitable for industrial applications.

039 Algorithm and robustness for a multivariable implicit self-tuning controller

039 Algorithm and robustness for a multivariable implicit self-tuning controller

Self-Tuning Control of a Heating, Ventilating and Air-Conditioning System

This paper describes the minimum-variance parameter-adaptive (self-tuning) control algorithms for SISO and MIMO systems. The algorithms are designed on the basis of linear input-output system models by the combination of recursive-parameter estimation and control algorithms: a single-variable minimum-variance self-tuning controller and a multi-variable minimum-variance self-tuning controller. These controllers are applied to a three-input three-output single environmental space, which consists of an air heater, air humidifier and ventilator, and whose output variables are temperature, relative humidity and air velocity. The results of simulation indicate that it is possible to use the self-tuning controllers to stabilize the controlled system after a short adaption phase and to achieve at least a satisfactory control performance for time-varying set-points of the output variables.

Self-Tuning PID Control: A Multivariable Derivation and Application

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.