Model Reference Approach Research Articles

Fuzzy adaptive model reference approach-based power system static VAR stabilizer

Abstract This paper presents a power system Static VAR stabilizer based on the fuzzy adaptive model reference approach. The Static VAR Compensator SVC (thyristor controlled reactor-fixed capacitor (TCRFC) type) is used for voltage regulation and to improve power system stability. The proposed control system consists of a feedback fuzzy controller using the speed state deviation and its derivative as input signals. Moreover, a model reference with a fuzzy adaptive mechanism is built to make the power system speed deviation response follow a certain track. The reference model transfer function of second order is chosen which has desired performance specifications. The input signal to reference model is the speed state deviation response. While the error between model reference output response and the power system speed deviation response is an input signal to fuzzy adaptive mechanism, the other fuzzy adaptive mechanism signal is the derivative of the above mentioned error signal. The control signal to the SVC stabilizer comprises the sum of the feedback fuzzy controller output and the fuzzy adaptive mechanism output signals. The proposed control system is proved by digital simulation power system speed and torque angle states response due to various disturbances. Furthermore, the effect of the additional signal comes from the fuzzy adaptive mechanism for enhancing the power system stability. A comparison between power system response using the proposed SVC stabilizer and without adding the control signal of fuzzy adaptive mechanism is presented. The results show the effectiveness and power of the presented SVC.

Learning periodic signals with recurrent neural networks

AbstractWe study a model for learning periodic signals in continouus time recurrent neural networks where the weight matrix has companion‐like structure. We use a model reference approach to derive a learning rule which solves the following learning problem: A network with unknown weights is running in closed loop mode giving a periodic trajectory. The output of this reference system is used as the input of a second learning network of the same structure with adjustable weights. We derive a learning rule (parameter adaptation law for the weights) which achieves the aims: Under appropriate assumptions the weights converge to the reference weights and the output tracking error goes to zero. If the learning dynamics is switched off after a sufficient period of time the learning system can approximately reproduce the reference signal when running in closed loop mode.

Nonlinear identification and control of a heat exchanger: A neural network approach

In this paper the potentials of neural networks-based control techniques are explored by applying a nonlinear generalized minimum variance control methodology to a simulated application example. In particular, reference is made to the control problem of regulating the output temperature of a liquid-satured steam heat exchanger by acting on the liquid flow-rate. Due to the non-minimum phase characteristic of the dynamics of the process, a simple inverting minimum variance controller is unsuitable. On the other hand, an effective solution is provided by a detuned model reference approach, which introduces a penalization factor in the control variable. A steady-state off-set error problem, caused by the neural network approximations, is tackled by means of an hybrid control structure, which combines a nonlinear integral action block with a neural controller. A comparison analysis is made to show the effectiveness of the proposed neural control schemes with respect to classical linear controllers.

Adaptive control of a submerged vehicle with sliding fuzzy relations

An adaptive fuzzy logic controller scheme is proposed for application to subsea remotely operated vehicles. The system uses a model reference approach to adjust the rulebase. The closed-loop controller rules are fuzzy relations with certain constraints, allowing smooth control and training. The submerged vehicle behaves in a safe manner during the training period and closely follows the desired behaviour of the reference model.

EXPERIMENTS ON THE MODELLING AND VIBRATION CONTROL OF A THREE-DEGREE OF FREEDOM MANIPULATOR WITH FLEXIBLE LINKS

Industrial robots have lumped flexibility due to the drive mechanism and distributed flexibility due to the elasticity of the arm. By developing an accurate model for the robot dynamics, advanced control strategies can be formulated to improve the tracking performance and reduce the residual vibration of the robot end-point. In this paper, a flexible two-link, three-degree of freedom manipulator is treated. Based on experimental modal analysis results, a reduced order model for the robot is derived using Lagrangian dynamics. The links flexibility was included by superimposing the flexible motion over the rigid body motion using the finite-element approach. Based on this model, two controllers are designed and implemented on the robot. The first is a gain-scheduling regulator, while the second is based on the model reference approach. Experimental results show the ability of these controllers to damp the end-point vibration.

Predictive temperature tracking for batch evaporative crystallizers: the partial state model reference approach

Batch operated crystallizers are widely used in the processing of fine chemical and pharmaceutical products. However, the tracking of various temperature setpoint trajectories appears to be a very difficult task if the crystallizer is cooled through vacuum, and advanced closed-loop control strategies have to be considered. This paper describes the application of the so called partial state model reference generalized predictive control approach (PSMR GPC) to the crystallization of organic compounds. After a brief introduction to the processes of crystallization from the solution, the main difficulties related to the use of vacuum for cooling industrial crystallizers are emphasized, followed by the weaknesses of PI control. The PSMR strategy is recalled and this algorithm is applied to calculate the input pressure leading to satisfactory tracking of temperature trajectories. The design of a non-linear adaptive supervisor is then described. In addition to the basic control law, this supervisor has been implemented to ensure improved safety and robustness. Supervisory PSMR GPC performances are reported. With respect to the main industrial constraints and requirements, the results are encouraging.

Stiffness enhancement of flight control actuator

A modification of the model reference approach, using numerical optimization is proposed for the estimation of external forces applied to control surfaces of an aircraft. This estimation provides the basis for the feedforward control of the surface position. Incorporated into the control system of a flight control actuator, this technique allows a significant enhancement of the actuator stiffness.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A model reference approach for implementing active structural acoustic control

A model reference active structural acoustic control (ASAC) approach is investigated as a method for replacing microphone error sensors located in the acoustic field with accelerometers located on the structure. The suggested controller is the multi-input/multi-output adaptive LMS algorithm. As opposed to driving the response of the sensor located on the structure to zero with the control inputs, the response is driven to some pre-determined ‘‘reference’’ value corresponding to an a priori training cost function, which is originally chosen to minimize either the far-field pressure at a number of acoustic field points, or the supersonic region of the wave-number transform. In effect, the uncontrolled structure is adaptively modified to behave like the reference structure. Results indicate that the identical optimal control solution can be obtained when implementing the structural sensors, and the required number of sensors must simply equal the number of control actuators. In addition, for the example studied here, significant reduction in far-field sound radiation can be achieved even when errors of ±5% are present in the reference model. A considerable level of attenuation in sound can also be achieved for a band of frequencies within ±5% of the reference frequency used to obtain the models for creating the disturbance. Thus it appears that the model reference approach is reasonably robust.

A second generation expert system for fault diagnosis

We propose a second generation expert system for fault diagnosis in a power system. This expert system combines shallow reasoning with deep reasoning that is based on a model reference approach. The model used for qualitative simulation of the power system in the deep reasoning part is based on a timed Petri net approach. The timed Petri net approach allows for accurate qualitative simulation of the protection system and power system components. The Petri net model permits one to model the function and behaviour of the power system components. It therefore allows for both static and dynamic simulation of the protection system and the associated power system. An application showing the modelling of part of substation configuration is given.

A model reference approach for implementing active structural acoustic control.

A model reference active structural acoustic control (ASAC) approach is investigated as a method for replacing microphone error sensors located in the acoustic field with accelerometers located on the structure. The suggested controller is the multi-input/multi-output adaptive LMS algorithm. As opposed to driving the response of the sensor located on the structure to zero with the control inputs, the response is driven to some predetermined ‘‘reference’’ value corresponding to a cost function which originally implemented microphones or the supersonic region of the wave-number transform as error information. In effect, the uncontrolled structure is adaptively modified to behave like the reference structure. Results indicate that the identical optimal control solution can be obtained when implementing the structural sensors, and the required number of sensors must simply equal the number of control actuators. In addition, for the example studied here, significant reduction in far-field sound radiation can be achieved even when errors of ±5% are present in the reference model. A considerable level of attenuation in sound can also be achieved for a band of frequencies within ±5% of the reference frequency used to obtain the models for creating the disturbance. Thus it appears that the model reference approach is reasonably robust. [Work supported by ONR/DARPA.]

On Reduced Order Model Reference Adaptive Control

This work deals with the relaxation of one |of the basic assumptions in the model reference approach to the adaptive control of unknown linear SISO systems, namely, the assumption concerning the a-priori knowledge of the relative degree. The elimination of such an assumption allows a unified treatment of both the unmodeled dynamic case and the standard case in which the plant order is known. This problem has been faced by designing a scheme which performs an adaptive pole assignment. The main feature of this scheme consists of the fact that the invariant reference model is of reduced order, while the number of the poles to be assigned results equal to the unknown order of the plant. The resultant adaptive control structure turns out to be globally stable.

ADAPTIVE CONTROL OF AN AIR MOTOR POWERED INDUSTRIAL SERVODRIVE

The work is a current extension of work on the conception, design and performance of an air motor powered servodrive for a 20 kg payload industrial situation. The earlier work involved dynamic modelling (using bond graphs) and simulation to help design the system, and response measurements on a prototype system. Fixed parameter feedback control was used. In parallel with that brushless d.c. motor, hydraulic motor, and hydraulic cylinder servodrives designed and similarly analysed, and comparisons were made with the airmotor servodrive's performance.The airmotor servodrive's performance was sufficiently encouraging to warrant further development. The system is nonlinear, and the load impedance it would experience in real life could change continually during its working cycle. It was considered highly probable that an appropriate adaptive control strategy will significantly improve the controlled performance of the system. Such a strategy, using the model reference approach, has been devised and implemented on digital simulation. A hardware system using software control will later be assembled to seek validation of the findings of the simulation.The paper presents the servodrive system and the adaptive control strategy developed, and the simulated performance of the loaded system under adaptive control for several conditions.

Long-range predictive control using weighting-sequence models

Long-range predictive control appears to be a better foundation for self-tuning compared with k-step ahead or model-reference approaches. Various methods have been proposed in the literature based on weighting-sequence models, and the paper unifies their development. By assuming a noise structure which involves Brownian motion, natural integrating action is achieved as opposed to the ad hoc approaches previously used. Simulation studies using truncated models show that large numbers of parameters are necessary using weighting sequences, although a parallel method using a CARIMA model is entirely satisfactory. When used with nonminimum-phase plant, the dynamic matrix control method works best.

Computational experience with adaptive model-reference identification schemes

This paper deals with the design implementation of adaptive identification schemes using a model-reference approach. An on-line, general-purpose simulation program is developed to operate in an interactive mode. The simulation program provides the user with multiple design options and aids in the assessment of the adaptive identification process. An illustrative example is presented to demonstrate the potential features of the program.

Estimation and direct adaptive control of delay-differential systems

The problem of adaptive prediction and direct adaptive control for systems described by delay-differential systems is considered. A model reference approach to direct adaptive control is shown to be feasible for a class of 2-D systems. A reparametrization of the 2-D system is given and the identification problem is converted into a linear estimation problem. Some necessary conditions for existence of solutions to the adaptive prediction and direct adaptive control problem are also given.

Microprocessor-based control of industrial sewing machines

Microcomputer-based control of the x − y motion of a sewing head of an industrial sewing machine is considered. A digital controller, replacing a previously used analogue one, is designed. It is realized using a Motorola 6801 single-chip microcomputer. Adaptive control of the system is also discussed. In particular the model reference approach, using the Liapunov function, is adopted. A solution using this approach is proposed, along with a realization using the 16-bit Mostek 68200 microprocessor.

Model Reference Adaptive Control for Manipulators–A Review

Based on the model reference approach and Popov's hyperstability theory, several adaptive control laws are derived in a simple and unified manner. The concepts of hyperstabi1ity theory and model reference adaptive control systems are introduced by means of a first order constant linear system. The theory is then applied to the design of adaptive controllers for manipulators having complex and nonlinear dynamics.

Applications of Adaptive Control to Ship Steering

Abstract This paper describes the application of various adaptive control techniques to the automatic steering of ships. At the Control Laboratory an adaptive autopilot has been developed which is now commercially available. The design is mainly based on the model-reference approach. The adaptive properties of this autopilot have reduced the number of manual settings, leaving only a few settings which can be easily understood by the operator. The autopilot is able to change heading in a predictable way and to keep a ship on her course, either with maximum accuracy or with minimum fuel consumption. Similar principles have been applied to the design of a new type of autopilot, which not only controls the heading of the ship but also the roll. For both control loops only one single actuator is used: the rudder. In order to realize such a system additional adaptive features have to be added to those already present in the course controller. Finally the application of adaptive control to the hydraulic rudder servo system is described. These control systems are mostly based on a relay as actuator. The switching of the relay is governed by a table which contains the time intervals necessary for a certain rudder displacement. An adaptive scheme has been implemented to update the table in case its values appear to be wrong.

Estimation and Direct Adaptive Control of Delay-Differential Systems

The problem of adaptive prediction and direct adaptive control for systems described by delay-differential systems is considered. A model reference approach to direct adaptive control is shown to be feasible for a class of 2-D systems. A reparametrization of the 2-D system description is given and the identification problem is converted into a linear estimation problem. Some necessary conditions for existence of solutions to the adaptive prediction and direct adaptive control problem are also given

Adaptive steering of ships—A model reference approach

This paper describes the application of model reference adaptive control (MRAS) to automatic steering of ships. The main advantages in this case are the simplified controller adjustment which yields safer operation and the decreased fuel cost. After discussion of the mathematical models of process and disturbances, criteria for optimal steering are defined. Algorithms are given for direct adaptation of the controller gains, applicable after setpoint changes, as well as for identification and adaptive state estimation, to be used when the input is constant. Solutions for applying MRAS to a certain class of nonlinear systems are dealt with. Full-scale trials at sea and tests with a scale model in a towing tank are described. It is shown that the autopilot designed indeed has the desired properties. Fuel savings up to 5% in comparison to conventional PID control are demonstrated. These savings are mainly possible because of the adaptive state estimator.