Model Reference Adaptive Scheme Research Articles

Experimental implementation and validation of a novel minimal control synthesis adaptive controller for continuous bimodal piecewise affine systems

This paper presents the first digital implementation of a novel model reference adaptive scheme for the control of continuous bimodal piecewise affine systems. The algorithm is based on the minimal control synthesis algorithm, originally developed as a MRAC for smooth systems. The resulting adaptive algorithm is a switched feedback controller able to cope with uncertain continuous PWA systems. The analogue continuous-time control law is implemented by using a digital low-cost commercial microcontroller. The aim is to control a piecewise-linear electrical circuit. The experimental validation process is made challenging by the presence of measure uncertainties, noise, quantization errors, unmodeled nonlinear dynamics and computational delays. Experiments confirm the effectiveness of the controller to cope with switching in the circuit dynamics, establishing the strategy as a viable control tool. Nevertheless, the experimental analysis provides a first insight into the robustness of the algorithm.

Model Reference Adaptive Scheme for Multi-drug Infusion for Blood Pressure Control

Summary Using multiple interacting drugs to control both the mean arterial pressure (MAP) and cardiac output (CO) of patients with different sensitivity to drugs is a challenging task which this paper attempts to address. A multivariable model reference adaptive control (MRAC) algorithm is developed using a two-input, two-output patient model. The control objective is to maintain the homodynamic variables MAP and CO at the normal values by simultaneously administering two drugs; sodium nitroprusside (SNP) and dopamine (DPM). Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters.

Model reference adaptive scheme for multi-drug infusion for blood pressure control.

Using multiple interacting drugs to control both the mean arterial pressure (MAP) and cardiac output (CO) of patients with different sensitivity to drugs is a challenging task which this paper attempts to address. A multivariable model reference adaptive control (MRAC) algorithm is developed using a two-input, two-output patient model. The control objective is to maintain the homodynamic variables MAP and CO at the normal values by simultaneously administering two drugs; sodium nitroprusside (SNP) and dopamine (DPM). Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters.

MRAS Sensorless Vector Control of an Induction Motor Using New Sliding-Mode and Fuzzy-Logic Adaptation Mechanisms

In this paper, two novel adaptation schemes are proposed to replace the classical PI controller used in model reference adaptive speed-estimation schemes that are based on rotor flux. The first proposed adaptation scheme is based on sliding-mode theory. A new speed-estimation adaptation law is derived using Lyapunov theory to ensure estimation stability, as well as fast error dynamics. The other adaptation mechanism is based on fuzzy-logic strategy. A detailed experimental comparison between the new and conventional schemes is carried out in both open- and closed-loop sensorless modes of operation when a vector control drive is working at very low speed. Superior performance has been obtained using the new sliding-mode and fuzzy-logic adaptation mechanisms in both modes of operations.

A real-time neuro-adaptive controller with guaranteed stability

This paper presents a new model reference adaptive neuro-control scheme using feedforward neural networks with momentum back-propagation (MBP) learning algorithm. Training is done on-line to tune the parameters of the neuro-controller that provides the control signal. Noting that pre-learning is not required and the structure of overall system is very simple and straightforward. No additional controller or robustifying signal is required. Tracking performance is guaranteed via Lyapunov stability analysis. Both tracking error and neural network weights remain bounded. An interesting fact about the proposed approach is that it does not require a NN being capable of reconstructing globally model non-linearities.

DIRECT FIELD-ORIENTED CONTROL USING BACKSTEPPING STRATEGY WITH FUZZY ROTOR RESISTANCE ESTIMATOR FOR INDUCTION MOTOR SPEED CONTROL

In this paper, the speed control of an induction motor using backstepping design with fuzzy rotor resistance estimation is proposed. First, the direct field oriented control IM is derived. Then, a backstepping for direct field oriented control is proposed to compensate the uncertainties, which occur in the control. Finally, a method of estimation of the rotor resistance in the backstepping control f induction motor drive. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed using fuzzy logic. The effectiveness of the proposed control scheme is verified by numerical simulation. The numerical validation results of the proposed scheme have presented good performances compared to the conventional direct-field oriented control.

Multilayer control of an induction motor drive:A strategic step for automotive applications

Fault tolerance is a critical attribute in automotive electrical and propulsion systems. In this paper, a control scheme is presented that allows an induction motor drive system to operate in the event of multiple sensor failures. Automatic diagnosis of sensor fault and recovery is performed and used to reconfigure the drive system controls to achieve the best performance in lieu of component degradation. This approach couples a new digital delta-hysteresis regulation scheme with a model reference adaptive system scheme in order to provide fault tolerance for both phase-current and rotor position (speed) sensors. Simulation and experimental results are provided to show the effectiveness of the proposed scheme

유도전동기의 고성능 제어를 위한 적응 퍼지-뉴로 제어기

본 논문은 유도전동기 드라이브의 고성능 제어를 위한 적응 퍼지-뉴로 제어기를 제시한다. 이 알고리즘의 설계는 퍼지제어와 신경회로망을 사용하는 퍼지-신경회로망 제어기에 기초한다. 적응 퍼지-뉴로 제어기는 신경회로망의 학습패턴과 같은 퍼지 룰을 사용하고 또한 지령값과 실제값 사이의 오차를 최소화하기 위하여 신경회로망의 뉴런사이의 하중을 역전파 알고리즘 방법을 사용하여 조절한다.BR 적응 기준 모델 설계는 기준모델의 출력과 전동기 속도 사이의 오차와 오차 변화분을 기초로 한 퍼지 로직에 의하여 실행되는 적응 메카니즘을 제시한다. 적응 퍼지-뉴로 제어기의 제어 성능은 다양한 동작 상태에 대한 분석으로 평가한다. 제안한 제어시스템의 실험 결과는 고성능과 파리미터 변동과 정상상태 정확성, 순시응답의 강인성을 가진다.

Adaptive Control Strategy for the Dynamic Positioning of a Shuttle Tanker During Offloading Operations

In deep water oil production, Dynamic positioning systems (DPS) strategy has shown to be an effective alternative to tugboats, in order to control the position of the shuttle tanker during offloading operations from a FPSO (floating production, storage, and offloading system). DPS reduces time, cost, and risks. Commercial DPS systems are usually based on control algorithms which associate Kalman filtering techniques with proportional-derivative (PD) or optimal linear quadratic (LQ) controllers. Since those algorithms are, in general, based on constant gain controllers, performance degradation may be encountered in some situations, as those related to mass variation during the loading operation of the shuttle tanker. The positioning performance of the shuttle changes significantly, as the displacement of the vessel increases by a factor of three. The control parameters are adjusted for one specific draught, making the controller performance to vary. In order to avoid such variability, a human-based periodic adjustment procedure might be cogitated. Instead and much safer, the present work addresses the problem of designing an invariant-performance control algorithm through the use of a robust model-reference adaptive scheme, cascaded with a Kalman filter. Such a strategy has the advantage of preserving the simple structure of the usual PD and LQ controllers, the adaptive algorithm itself being responsible for the on-line correction of the controller gains, thus insuring a steady performance during the whole operation. As the standard formulation of adaptive controllers does not guarantee robustness regarding modeling errors, an extra term was included in the controller to cope with strong environmental disturbances that could affect the overall performance. The controller was developed and tested in a complete mathematical simulator, considering a shuttle tanker operating in Brazilian waters subjected to waves, wind and current. The proposed strategy is shown to be rather practical and effective, compared with the performance of constant gain controllers.

Generating optimal adaptive fuzzy-neural models of dynamical systems with applications to control

The paper describes an approach to generating optimal adaptive fuzzy neural models from I/O data. This approach combines structure and parameter identification of Takagi-Sugeno-Kang (TSK) fuzzy models. We propose to achieve structure determination via a combination of modified mountain clustering (MMC) algorithm, recursive least squares estimation (RLSE), and group method of data handling (GMDH). Parameter adjustment is achieved by training the initial TSK model using the algorithm of an adaptive network based fuzzy inference system (ANFIS), which employs backpropagation (BP) and RLSE. Further, a procedure for generating locally optimal model structures is suggested. The structure optimization procedure is composed of two phases: 1) locally optimal rule premise variables subsets (LOPVS) are identified using MMC, GMDH, and a search tree (ST); and 2) locally optimal numbers of model rules (LONOR) are determined using MMC/RLSE along with parallel simulation mean square error (PSMSE) as a performance index. The effectiveness of the proposed approach is verified by a variety of simulation examples. The examples include modeling of a nonlinear dynamical process from I/O data and modeling nonlinear components of dynamical plants, followed by tracking control based on a model reference adaptive scheme (MRAC). Simulation results show that this approach is fast and accurate and leads to several optimal models.

Indirect model reference adaptive control with dynamic adjustment of parameters

SUMMARY The paper discusses in detail a new method for indirect model reference adaptive control (MRAC) of linear time-invariant continuous-time plants with unknown parameters. The method involves not only dynamic adjustment of plant parameter estimates but also those of the controller parameters. Hence the overall system can be described by a set of non-linear differential equations as in the case of direct control. Many of the difficulties encountered in the conventional indirect approach, where an algebraic equation is solved to determine the control parameters, are consequently bypassed in this method. The proof of stability of the equilibrium state of the overall system is found to be different from that used in direct control. Using Lyapunov’s theory, it is first shown that the parameter errors between the parameter estimates of the identifier and the true parameters of the plant, as well as those between the actual parameters of the controller and their desired values, are bounded. Following this, using growth rates of signals in the adaptive loop as well as order arguments, it is shown that the error equations are globally uniformly stable and that the tracking (control) error tends to zero asymptotically. This in turn establishes the fact that both direct and indirect model reference adaptive schemes require the same amount of prior information to achieve stable adaptive control.

A Hybrid Model Reference Adaptive Control/Computed Torque Control Scheme for Robotic Manipulators

A direct model reference adaptive controller scheme for a robotic manipulator based on the Lyapunov stability criterion is developed. The algorithm presented is based on the formulation of a plant state model that includes all the non-linear characteristics of the robot manipulator. This adaptive controller takes advantage of the structure and any known dynamics of the system in order to increase the speed of adaptation and relax the conditions required for convergence. It uses a modified Lyapunov function to derive the adaptation laws that are not dependent on the usual assumption that the time-varying plant is close to the desired time-invariant plant. The Lyapunov design guarantees asymptotic stability.

Design of decentralized observation schemes for large-scale interconnected systems: Some new results

A new algorithm for the design of decentralized observation schemes for large-scale interconnected systems is presented. This algorithm offers a constructive procedure based on a stability result that employs the notion of block diagonal dominance in matrices, and is shown to improve considerably upon the existing results for the decentralized observer design problem. A principal contribution of this paper is the demonstration of how the observer gains can be tailored systematically to the existing interconnection pattern within the overall system. Although the present results are developed in the context of decentralized observation, they can be extended to the design of decentralized stabilization algorithms and to the design of decentralized model reference adaptive identification schemes.

An Adaptively Controlled Electrohydraulic Servo-Mechanism: Part 1: Adaptive Controller Design

A systematic model reference adaptive control design scheme is presented. The control scheme is developed and analysed within the framework of a sampled data system with a parameter adaptive algorithm designed on the basis of hyper stability theory. A number of supervisory functions are used to supplement the basic adaptive control system in order to enhance robust controller action.

A method of adaptive control and identification for discrete polynomic non-linear systems containing parameters linearly

An adaptive law for multi-input multi-output discrete polynomic non-linear systems, which can be used for both control and identification is investigated by using the Lyapunov method. It has a simple structure and a convenient form for computation. For control problems, serial-type and parallel-type model-reference adaptive schemes are proposed and a criterion for the selection of the types is given. For identification problems, two conditions for identifiability with respect to input and output data and known constant matrices of the system are derived. To show the effectiveness of this method, a model-reference adaptive scheme for Volterra's prey-predator system is constructed. Digital simulation of the scheme confirms that this method is useful for both control and identification of discrete and continuous systems.

A model reference adaptive scheme for identification of a multicompartmental model for cell proliferation kinetics†

A stable model reference adaptive approach is used to develop an algorithm for identifying the parameters of a multicompartmental model which portrays the progression of cells through the different phases of the cell cycle. The identification procedure is tailored to utilize a popular form of data that can be easily collected from cytokinetic experimentation viz., multiple DNA distribution curves via microfluorometry studies. Details of some necessary modifications of the procedure to overcome certain practical limitations of data collection arc described. Validation of the procedure is demonstrated by application to two data series (one collected on a CHO cell line and the other on a human lymphoma cell line) for identification of model parameters.

On parameter convergence in adaptive control

It is well known that the parameter error as well as the model-plant mismatch error in a model reference adaptive scheme tends exponentially to zero iff a certain sufficient richness condition holds for signals inside the time-varying plant control loop. In this paper we give conditions on the reference signal (the exogenous input to the adaptive loop) — namely, that it have as many spectral lines as there are unknown parameters, in order to guarantee parameter convergence.

Robustness properties of model-reference adaptive control systems

Model-reference adaptive schemes are usually designed on the assumption that the structure of the plant transfer function is known, only the parameter values being uncertain. In the paper, the stability properties of such systems are studied when the plant can only be approximately represented by a model of the type assumed, i.e. when its transfer function does not belong to the class for which exact matching with the reference model can be achieved.

A lemma for the problem of exponentially convergent adaptive identification and state observation schemes

A lemma is presented and proved that concerns the exponential convergence property of a special class of probing signals employed in model reference adaptive identification and state observation schemes. The lemma, an interesting result in itself, is useful in finding exponential bounds for the parameter errors in adaptive identification schemes.

Multivariable adaptive parameter and state estimators with convergence analysis

AbstractThe convergence properties of a very general class of adaptive recursive algorithms for the identification of discrete-time linear signal models are studied for the stochastic case using martingale convergence theorems. The class of algorithms specializes to a number of known output error algorithms (also called model reference adaptive schemes) and equation error schemes including extended (and standard) least squares schemes. They also specialize to novel adaptive Kalman filters, adaptive predictors and adaptive regulator algorithms. An algorithm is derived for identification of uniquely parameterized multivariable linear systems.A passivity condition (positive real condition in the time invariant model case) emerges as the crucial condition ensuring convergence in the noise-free cases. The passivity condition and persistently exciting conditions on the noise and state estimates are then shown to guarantee almost sure convergence results for the more general adaptive schemes.Of significance is that, apart from the stability assumptions inherent in the passivity condition, there are no stability assumptions required as in an alternative theory using convergence of ordinary differential equations.