Model Reference Adaptive Control Method Research Articles

Efficient speed control of induction motor using RBF based model reference adaptive control method

This paper proposes a model reference adaptive speed controller based on artificial neural network for induction motor drives. The performance of traditional feedback controllers has been insufficient in speed control of induction motors due to nonlinear structure of the system, changing environmental conditions, and disturbance input effects. A successful speed control of induction motor requires a nonlinear control system. On the other hand, in recent years, it has been demonstrated that artificial intelligence based control methods were much more successful in the nonlinear system control applications. In this work, it has been developed an intelligent controller for induction motor speed control with combination of radial basis function type neural network (RBF) and model reference adaptive control (MRAC) strategy. RBF is utilized to adaptively compensate the unknown nonlinearity in the control system. The indirect field-oriented control (IFOC) technique and space vector pulse width modulation (SVPWM) methods which are widespread used in high performance induction motor drives has been preferred for drive method. In order to demonstrate the reliability of the control technique, the proposed adaptive controller has been tested under different operating conditions and compared performance of conventional PI controller. The results show that the proposed controller has got a clear superiority to the conventional linear controllers.

Speed-Regulating System for Induction Motor and Invert-er based on Hammerstein Model and Neural Network Control

A novel control strategy based on Hammerstein model and neural network for the speedregulating system of the induction motor and inverter is proposed in this paper. First, Hammerstein model was used to model the speed-regulation system of the induction motor and inverter. Auto-regressive and moving average (ARMA) model was used to identify the dynamic linear module of Hammerstein model of the speed-regulating system. Second, the ARMA model was used as a reference model for identification of the inverse model of static nonlinear neural network (NN) module of Hammerstein model in the framework of the model reference adaptive control method. For the load disturbance issue, two control strategies, online learning neural network direct inverse control and the traditional PI close-loop control strategy were studied. Simulations show that the inverse control based on Hammerstein model and NN is effective and the online learning neural network direct inverse control strategy for the speed-regulating system with load disturbance has higher performance.

An improved nonlinear model for a helicopter and its self-repairing control with multiple faults via quantum information technique

In this paper, an improved nonlinear model for a 3-DOF helicopter and its self-repairing control scheme are investigated via quantum information technique. Firstly, adopting the mechanism analysis, a modified dynamic model is developed, in which the couplings among axes are considered. Such a modeling scheme is useful for applications when the accuracy of original model can not be satisfied. Then, a reconfigurable control scheme is designed for the twin rotor helicopter with multiple faults and parametric uncertainties, which combines active disturbance rejection control method with model reference adaptive control method. In addition, quantum information technique is used to increase the accuracy of self-repairing control of helicopter. Finally, simulation verification is presented in both aspects of modeling and control. The effectiveness and feasibility of the proposed scheme are verified by comparative simulations.

Bayesian nonparametric adaptive control using Gaussian processes.

Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.

Road Vehicle Following Control Strategy Using Model Reference Adaptive Control Method Stability Approach

In convoy system, the control system on each vehicle requires information about proceeding vehicle motion, in order to maintain stability and satisfy operating constraints. A two-vehicle look-ahead control strategy is proposed and investigated for the operation of a convoy. The mathematical modeling for this control strategy has been formulated and simulated in this paper. This paper demonstrates the design process of an adaptive controller gain for a road vehicle following system. This process is done by simulation. The appropriate constants are used as the reference model for the adaptive controller implementation to find the effectiveness of the control. As a result, a road vehicle following system with an adaptive gain controller is produced.

Adaptive Flight-Control Design Using Neural-Network-Aided Optimal Nonlinear Dynamic Inversion

A neural-network-aided nonlinear dynamic inversion-based hybrid technique of model reference adaptive control flight-control system design is presented in this paper. Here, the gains of the nonlinear dynamic inversion-based flight-control system are dynamically selected in such a manner that the resulting controller mimics a single network, adaptive control, optimal nonlinear controller for state regulation. Traditional model reference adaptive control methods use a linearized reference model, and the presented control design method employs a nonlinear reference model to compute the nonlinear dynamic inversion gains. This innovation of designing the gain elements after synthesizing the single network adaptive controller maintains the advantages that an optimal controller offers, yet it retains a simple closed-form control expression in state feedback form, which can easily be modified for tracking problems without demanding any a priori knowledge of the reference signals. The strength of the technique is demonstrated by considering the longitudinal motion of a nonlinear aircraft system. An extended single network adaptive control/nonlinear dynamic inversion adaptive control design architecture is also presented, which adapts online to three failure conditions, namely, a thrust failure, an elevator failure, and an inaccuracy in the estimation of . Simulation results demonstrate that the presented adaptive flight controller generates a near-optimal response when compared to a traditional nonlinear dynamic inversion controller.

Bi-objective optimal control modification adaptive control for systems with input uncertainty

This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and the predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and the predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.

Reconfiguration for Sensor Failure of Aero-Engine Electronic Control System Based on the MRAC

In the paper, the fault-tolerant control strategy for the aero-engine electronic control system is researched based on the Model Reference Adaptive Control method. In the design process, a new reference model design method is proposed with the Raccatti equation, which can guarantee the reconfiguration system have satisfactory performance. According to the sensor failure models, the failure system mathematic model is given. Lyapunov stability theory was applied to obtain the adaptive control law through design a Lyapunov function, which can ensure the strictly positive realness and global asymptotic stability for the reconfiguration control system. For the typical sensor failures, the reconfiguration effect with the control strategy is simulated and verified in the Matlab, which revealed that the aero-engine control system could operate well under the failure with the fault-tolerant control.

Adaptive Controller Design for SISO Switched Nonlinear Systems with Linear Uncertain Parameters

This paper presents that single input single output (SISO) switched nonlinear system tracks the variation of the state error to approach the excepted values by using model reference adaptive control (MRAC) method. In order to improve the adaptive control for nonlinear systems by Using Narendra method and dividing the system into two parts: linear and nonlinear parts. The controllers are designed to guarantee that the systems are closed to the model reference system with the arbitrary switching signal. Switching systems can ensure choose the best controller so that can enhance the performance. The adaptive laws are given that are based on a class of feedback single input single output nonlinear uncertain systems which can switch feedback linear standard models. The adaptive laws are different from the classic adaptive laws, because they vary with different switching signals until the best matching one comes. Simulation results show that the proposed method is effective.

Experimental Validation of Bayesian Nonparametric Adaptive Control Using Gaussian Processes

Many current model reference adaptive control methods employ parametric adaptive elements in which the number of parameters are fixed a priori and the hyperparameters, such as the bandwidth, are predefined, often through expert judgment. As an alternative to these methods, a nonparametric model using Gaussian processes was recently proposed. Using Gaussian processes, it is possible to maintain constant coverage over the operating domain by adaptively selecting new kernel locations as well as adapt hyperparameters in an online setting to improve model prediction. In this work, the first extensive experimental flight results are presented using Gaussian process/model reference adaptive control. Experimental results show that Gaussian process/model reference adaptive control outperforms traditional model reference adaptive control methods that use radial basis function neural networks in terms of tracking error as well as transient behavior on trajectory following using a quadrotor. Results show an improvement of a factor of two to three over preexisting state-of-the-art methods. Additionally, many model reference adaptive control frameworks treat the adaptive element as being known exactly, and they do not incorporate certainty of the prior model into the control policy. In this paper, the notion of a Bayesian scaling factor is introduced that scales the adaptive element in order to incorporate the uncertainty of the prior model and current model confidence. The stability and convergence of using the Bayesian scaling factor in a closed loop is proven.

Decentralized adaptive command following and disturbance rejection for subsystems with local full‐state feedback

SummaryWe present a decentralized model reference adaptive control method, where each local controller uses full‐state feedback from the local subsystem. The controller is strictly decentralized, meaning that no information (including reference‐model trajectories) is shared between local controllers. This decentralized controller achieves stabilization, command following, and disturbance rejection provided that the reference‐model commands and the disturbances are sinusoidal with known spectrum. The controller is effective for multi‐input subsystems with arbitrarily large subsystem interconnections. Copyright © 2014 John Wiley & Sons, Ltd.

A novel hybrid petri net model for urban intersection and its application in signal control strategy

A signal control strategy based on model reference adaptive control is investigated with a new hybrid petri net model for urban intersection in artery. The hybrid petri net intersection model is simplified via the token transition. The new model can be more in touch with facts through the waiting zones and addition phases. The hybrid petri net is innovatively used as the reference model in the model reference adaptive control method. The predicted traffic flow is precisely computed by the support vector machine. Finally, a series of simulation results with the practical data verify the feasibility of the novel arterial petri net model and the effectiveness of the control strategy.

Exponential parameter and tracking error convergence guarantees for adaptive controllers without persistency of excitation

In model reference adaptive control (MRAC) the modelling uncertainty is often assumed to be parameterised with time-invariant unknown ideal parameters. The convergence of parameters of the adaptive element to these ideal parameters is beneficial, as it guarantees exponential stability, and makes an online learned model of the system available. Most MRAC methods, however, require persistent excitation of the states to guarantee that the adaptive parameters converge to the ideal values. Enforcing PE may be resource intensive and often infeasible in practice. This paper presents theoretical analysis and illustrative examples of an adaptive control method that leverages the increasing ability to record and process data online by using specifically selected and online recorded data concurrently with instantaneous data for adaptation. It is shown that when the system uncertainty can be modelled as a combination of known nonlinear bases, simultaneous exponential tracking and parameter error convergence can be guaranteed if the system states are exciting over finite intervals such that rich data can be recorded online; PE is not required. Furthermore, the rate of convergence is directly proportional to the minimum singular value of the matrix containing online recorded data. Consequently, an online algorithm to record and forget data is presented and its effects on the resulting switched closed-loop dynamics are analysed. It is also shown that when radial basis function neural networks (NNs) are used as adaptive elements, the method guarantees exponential convergence of the NN parameters to a compact neighbourhood of their ideal values without requiring PE. Flight test results on a fixed-wing unmanned aerial vehicle demonstrate the effectiveness of the method.

Robust Discrete-Time MRAC With Minimal Controller Synthesis of an Electronic Throttle Body

The electronic throttle body (ETB) is a fundamental actuator for regulating the air mass coming into an internal combustion engine; hence, it is used to control the engine torque in any modern drive-by-wire configuration. To cope with the nonlinear and discontinuous dynamics of this automotive device, in this paper a novel discrete-time model reference adaptive control (MRAC) method is designed and experimentally tested on an ETB installed on a 2-L engine. The control strategy extends the class of the minimal control synthesis (MCS) algorithms for discrete-time systems by adding an explicit discrete-time adaptive integral action and an adaptive robust term. An in-depth experimental investigation shows that the proposed control method is a viable solution as it is robust with respect to nonlinear torques acting on the plant, and it guarantees better performance than those provided by other MRAC strategies especially for small reference signals around the limp-home position where plant nonlinearities strongly affect the ETB dynamics.

An Improved MRAC Scheme for Non-linear Design of Adaption Gain γ using Heuristic Algorithms

This paper presents an approach to Optimal Model Reference Adaptive Control (OMRAC) and comparative performance evaluation are analyzed for a conical tank process which exhibits behavior of a class of nonlinear dynamical system. The conventional Model Reference Adaptive Control (MRAC) method based on the MIT rule reported in the works (P. Swarnkar et al., 2010), has its difficulty in choosing the reference model and adaption gain γ. Inspired by the above work, the selection of reference models in MRAC scheme are based on the multiple models (MM) depending on the operating regime of the process. In this proposed work, the reference model considered is a second order system having fixed roots in denominator polynomial representing the most dominant poles in the process determined using Dominant Pole Algorithm (DPA). The numerator polynomial of the reference model is designated as static sensitivity from each linearized region of the process. The optimal adaption gain γ of MRAC is obtained by Bacterial Foraging based Particle Swarm Optimization (BFPSO) algorithm and the system performance is compared with traditional algorithms such as Bacterial Foraging Optimization (BFO) and Particle Swarm Optimization (PSO). The simulated result gives consistently better setpoint tracking mechanism and error minimization.

State Dependent Riccati Equation Based Model Reference Adaptive Stabilization of Nonlinear Systems with Application to Cancer Treatment

The paper suggests a new approach to Model Reference Adaptive Control (MRAC) design for stabilization of a class of uncertain nonlinear systems. The proposed MRAC design methodology is based upon a stable nonlinear reference model which is produced by a state feedback controller using the so-called State Dependent Riccati Equation (SDRE) techniques. Based on states of the reference model, the designed stabilizer for the nonlinear reference model is then adapted for the nonlinear plant dynamics with a suitable adaptation mechanism, again by using the SDRE methodology. The proposed technique is illustrated to develop an optimal chemotherapy drug administration for cancer treatment using a tumor growth mathematical model. Simulation results show the effectiveness of the proposed SDRE-based MRAC method for the stabilization of nonlinear systems.

An Adaptive Fuzzy Control Method for Spacecrafts Based on T-S Model

A model reference adaptive control method is proposed for uncertain nonlinear characteristics of spacecraft attitude control system. This method combines fuzzy control methodology and nonlinear feedback linearization methodology, which made the closed-loop system stable and the state of fuzzy system track the state of reference model according to the parallel distributed compensation theory and the rational design of the fuzzy state feedback control law. The nonlinear closed-loop system was linearized by selecting fuzzy state feedback parameters and fuzzy membership function. Then an adaptive control law was designed by Lyapunov function. As a result the system can be adaptive to all kinds of parameter uncertainties and robust to modeling inaccuracy and external disturbance. Meanwhile, the simulation results indicate that the control law can quickly guarantee the stability of the spacecraft attitude and be robust to model perturbations and external disturbances. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3549

Recursive Adaptive Control for Solar Panel Servo System

Two typical methods for model reference adaptive control are introduced. By integrating the Narendra adaptive control method and the variable structure model reference adaptive control method, a new variable model reference adaptive recursive control method is presented. The results of simulation computations show that the new method has the merits of the above two methods and is efficient and effective.

Optimization of a Temperature Control Loop using Self Tuning Regulator

Stirred Tank Reactor (CSTR) plays an important role in the process industries. It helps for maintaining the temperature of the liquid in the reactors. This paper deals with the comparison of adaptive control and conventional PID control in CSTR process. In the adaptive control, Model Reference Adaptive Control (MRAC) and Self-Tuning Regulator (STR) methods are used. The Recursive Least-Square algorithm (RLS) gives the process parameters and Minimum Degree Pole Placement (MDPP) gives the controller parameters and is used to obtain the Control law. This paper illustrates how well the MDDP and RLS algorithms work. The S-function simulation is made using MATLAB codes and the results were analysed. Simulation results shows that the closed loop response of adaptive control has a better performance, compared with the conventional PID controller. This adaptive control method is applied to the Continuous Stirred Tank Reactor for maintaining the liquid temperature inside the reactor.

Adaptive Fault-Tolerant Control for Flight Systems with Input Saturation and Model Mismatch

A novel scheme for fault-tolerant control is proposed in this paper, in which model reference adaptive control method is incorporated with control allocation to cope with simultaneous actuator failures, input saturation, and model mismatch in the flight system. In order to reduce performance degradation caused by actuator failures, the proposed scheme redistributes the control signal to healthy actuators and updates the weighting matrix based on actuator effectiveness. Because of saturation errors resulting from actuator constraints and model mismatch caused by abnormal changes in the system, the original reference model may not be appropriate. Under this circumstance, an adaptive reference model which can also provide satisfactory performance is designed. Simulations of a flight control example are given to illustrate the effectiveness of the proposed scheme.