Model Reference Adaptive Control Controller Research Articles

Performance enhancement of grid‐connected photovoltaic systems using Ant Lion optimisation and Genetic Algorithm‐based optimisation techniques

One of the most common issues in renewable energy applications is modelling and regulating grid-connected PV systems. The model reference adaptive control (MRAC) technique has been analysed in this paper. It's difficult to develop an MRAC that performs well in both transient and steady-state conditions; hence, the primary goal of this paper is to create a modified MRAC that has excellent steady-state and transient responses. A modified MRAC scheme has been proposed, where an Ant lion optimisation technique and a well-known algorithm that is Genetic Algorithm (GA) is used to optimise the controller gain and compared it with each other and also with the conventional MRAC technique. The optimisation techniques are used to tune the controller parameters so that errors should be minimised. The time response analysis of MRAC, MRAC-GA, and MRAC-ALO controllers are shown and evaluated using common control criteria such as rising time, settling time, peak overshoot, steady-state, and error parameters. The simulation findings reveal that the proposed modified MRAC controller, both MRAC-ALO and MRAC-GA, outperforms in relation to transient performance and steady-state performance compared to conventional MRAC controllers.

Improving MRAC Transient Response Using Generalized Dynamic Inversion and Nullspace Parametrization

We demonstrate how the transient response of Model Reference Adaptive Control (MRAC) is improved by utilizing Generalized Dynamic Inversion (GDI) and the corresponding nullspace parametrization in the powerful Greville formula. The proposed approach neither alters the reference model structure, nor it modifies it through feedback. Rather, the proposed approach utilizes the control redundancy in matching the reference model for the purpose of designing an MRAC control law that yields good transient closed loop adaptive control response. The adaptive GDI-MRAC control law is composed of two parts: a particular part that enforces the reference model dynamics, and an auxiliary part that is affine in a null control vector. The particular and the auxiliary parts of the adaptive control law are confined in the range space of the transposed control matrix and its orthogonal complement nullspace, respectively. The null control vector is projected onto the nullspace of the control matrix via a nullprojection matrix. Hence, the null control vector design does not interfere with the reference model following, yet it can be designed via state feedback to improve closed loop state transient response. An improved Lyapunov control function is used to design the adaptation gains such that asymptotic vanishing of the state error dynamics is guaranteed. Extensive simulations are conducted to demonstrate the efficacy of the proposed method.

Modelling, simulation and implementation of a hybrid model reference adaptive controller applied to a manipulator driven by pneumatic artificial muscles

AbstractThe present research aims to model, simulate and implement a new hybrid control approach based on a combination of proportional integral derivative (PID) Controller and Model Reference Adaptive Controller (MRAC), in which Lyapunov’s theory is used to ensure asymptotic stability to control a two degrees of freedom (DoF) manipulator driven by McKibben’s artificial pneumatic muscles. The MRAC controller works as a nonlinearity compensator and PID controller works during the transient period, as the MRAC performs poorly in this regime. This new approach is entitled Hybrid Model Reference Adaptive Controller (H-MRAC) and it has an unprecedented topological structure based on three terms. The feedforward term acts in disturbances rejection, the derivative term in oscillations damping and the feedback term acts in error convergence to zero. In this article, a control system dedicated to pneumatic manipulators was developed. As a result, proof of asymptotic convergence was performed for the proposed topological approach, which was validated on a two DoF manipulator. The proposed mechanism satisfactorily met the ISO/TS 15066 standard, and the position tracking obtained a global error of 37.69% and 51.01% smaller than found in the literature examples, entitled MRAC and A-PID, respectively, for simulations and 37.46% and 30.25% for experiments.

Novel model reference adaptive control with application to wing rock example

In this study, we propose new adaptive laws for adjusting the controller parameters of the model reference adaptive control (MRAC) and MRAC with integral feedback schemes. The innovation presented in this study is considering a new form for the Lyapunov function candidate to prove the stability of the closed-loop system. In general, a Lyapunov function candidate contains two sets of quadratic expressions. The first set contains the state tracking error variable, while the second one consists of the controller parameter estimation errors. We prove that by choosing the tracking error quadratic expressions in the form of the exponential function, new adaptive laws that contain the tracking error quadratic expressions are obtained. The difference between the standard MRAC adaptive laws and the proposed new adaptive laws is the state tracking error exponential quadratic expression appears in the adaptive laws. It is shown that the adaptive laws obtained by the exponential quadratic Lyapunov function are similar to those obtained by the quadratic Lyapunov function except that the adaptive gains are variable with time. The advantage of using these new adaptive laws is improving the tracking performance of the closed-loop system, which has been proven analytically and verified by numerical simulations. Also, the robustness analysis of the proposed MRAC controller in the presence of the exogenous disturbance is studied. We consider the single degree of freedom of the wing rock example to evaluate the performance of the designed controllers.

Partial-state feedback multivariable MRAC and reduced-order designs

This paper develops a new model reference adaptive control (MRAC) framework using partial-state feedback for solving a multivariable adaptive output tracking problem. The developed MRAC scheme has full capability to deal with plant uncertainties for output tracking and has desired flexibility to combine the advantages of full-state feedback MRAC and output feedback MRAC. With such a new control scheme, the plant-model matching condition is achievable as with an output or state feedback MRAC design. A stable adaptive control scheme is developed based on LDS decomposition of the plant high-frequency gain matrix, which guarantees closed-loop stability and asymptotic output tracking. The proposed partial-state feedback MRAC scheme not only expands the existing family of MRAC, but also provides new features to the adaptive control system, including additional design flexibility and feedback capacity. Based on its additional design flexibility, a minimal-order MRAC scheme is also presented, which reduces the control adaptation complexity and relaxes the feedback information requirement, compared to the existing MRAC schemes. New results are presented for plant-model matching, error model, adaptive law and stability analysis. A simulation study of a linearized aircraft model is conducted to demonstrate the effectiveness and new features of the proposed MRAC control scheme.

Fractional order adaptive MRAC controller for an active suspension system

This paper investigates the use of fractional order operators in conventional model reference adaptive control (MRAC). A fractional adaptive controller is designed based on the use of a fractional-order parameter adjustment rule. Applied in numerical simulations for an active suspension system and compared with the conventional MRAC, it is shown that the performances of FOMRAC are superior to classical control schemes.

Stabilizing the dynamic behavior and Position control of a Remotely Operated Underwater Vehicle

Remotely operated underwater vehicle (ROUV) provides an interesting and extensible platform to search the wanted objects and for the inspection of deep sea. The basic goal of this research is to control the position and stabilize the dynamic behavior of ROUV. In this study, we design the dual controller approach design for controlling the overall responses of ROUV. The design dual controller consists of model reference adaptive control (MRAC) along with proportional integral derivative (PID) controller and an integral use for the feedback of the design scheme. The dynamic moments and disturbances in the system is dealt by MRAC controller and PID controller is responsible for tuning the adaptive gains of the system. However, Lyapunov stability criterion is responsible for the stability of the system. The inclusion of integrator as a feedback in the system increases the order of the system model, but it helps to eliminate the steady state error and also improves the convergence rate of the system. The designed control algorithm is tested and confirmed its validity using experiment and simulations by tracking the reference path of the ROUV. It is evidence that the designed control system shows quick convergence, improved steady state error and better robustness in the presence of disturbances.

Model Reference Adaptive Control for linear motor

Purpose. Presentation the results of mathematical modelling based on known numerical analysis methods. By writing a model behind the magnetic equivalent circuits method in the MATLAB software environment, the electro-mechanical characteristics of a linear motor and the causes of the occurrence of braking forces with increasing speed were obtained. The Model Reference Adaptive Control (MRAC) for the linear motor was developed in the MATLAB software environment. The model takes into account the influence of the uncertainties parameters of the linear motor due to inertial load, friction, ripple force and electrical parameters. Based on the structure of the motor, the model of MRAC control is utilized to reduce the effect of parametric uncertainties on the motor movement. The developed model achieves high accuracy and speed of adaptive control of a linear motor.

Model Reference Adaptive Iterative Learning Speed Control for Ultrasonic Motor

In the design process of the controller, the adaptive gain of model reference adaptive control (MRAC) often requires a tradeoff between the adaptive ability, robustness and stability of the control system. The tradeoff of adaptive gain leads to poor control performance and increase design difficulty. Aiming at this problem, the iterative learning idea is introduced into the model reference adaptive control strategy. The control parameter adaptive law based on the parameters of the previous control process is designed. For scalar systems, a new control strategy is constructed, which is the combination of MRAC and iterative learning control (ILC). The adaptive ability of the model reference adaptive controller is improved by using learning ability of ILC. An appropriate composite energy function is designed to prove the uniform convergence of the proposed control strategy and the boundedness of the control quantity. The proposed control strategy is applied to the ultrasonic motor. The effectiveness of the proposed control strategy is verified by experiments and simulations. The controller is designed by using the first-order model that is large different from the actual object. It verifies that the control strategy has strong robustness to model deviation and online time-varying characteristics.

Study on application of model reference adaptive control in fast steering mirror system

The control accuracy of traditional controllers for fast steering mirror system depends largely on the accuracy of the system model, which limits the performance of the controllers. Model reference adaptive controller (MRAC) is suggested for further improving the control accuracy of the fast steering mirror. Before designing the controller, we first introduced rate feedback to reduce the resonance peak of the fast steering mirror. Then a reference model which has good dynamic performance is selected. The MRAC controller is designed base on Lyapunov theory, which guarantees the asymptotic stability of the whole system and makes the output of the FSM system evolves asymptotically to the selected model. Experimental results show that FSM system with MRAC controller shows excellent dynamic performance.

Model Reference Adaptive Control Scheme for Retuning Method-Based Fractional-Order PID Control with Disturbance Rejection Applied to Closed-Loop Control of a Magnetic Levitation System

This study demonstrates the utilization of model reference adaptive control (MRAC) for closed-loop fractional-order PID (FOPID) control of a magnetic levitation (ML) system. Design specifications of ML transportation systems require robust performance in the presence of environmental disturbances. Numerical and experimental results demonstrate that incorporation of MRAC and FOPID control can improve the disturbance rejection control performance of ML systems. The proposed multiloop MRAC–FOPID control structure is composed of two hierarchical loops which are working in conjunction to improve robust control performance of the system in case of disturbances and faults. In this multiloop approach, an inner loop performs a regular closed-loop FOPID control, and the outer loop performs MRAC based on Massachusetts Institute of Technology (MIT) rule. These loops are integrated by means of the input-shaping technique and therefore no modification of any parameter of the existing closed-loop control system is necessary. This property provides a straightforward design solution that allows for independent design of each loop. To implement FOPID control of the ML system, a retuning technique is used which allows transforming an existing PID control loop into an FOPID control loop. This paper presents the simulation and experimental results and discusses possible contributions of multiloop MRAC–FOPID structure to disturbance rejection control of the ML system.

Failure diagnosis and tolerant control method for hydrothermally aged SCR system by utilizing EKF observer and MRAC controller

For ensuring emission performances of a selective catalytic reduction (SCR) system, it shall be critically robust and adaptive against any hydrothermal aging failure throughout its whole service life. Simulation was carried out here to investigate its hydrothermal aging effect by using such hydrothermal aging model and the corresponding results showed that its performances were significantly influenced while the catalyst (V2O5/WO3-TiO2) was hydrothermally aged. On this basis, an extended-Kalman-filter-based (EKF-based) observer was designed to identify its hydrothermal aging states and the corresponding results indicated that the actual hydrothermal aging degree could be estimated quickly and accurately. Moreover, a Lyapunov-based model reference adaptive controller (MRAC) was designed to improve its control performances based on the diagnosis information from the EKF-based observer while V2O5/WO3-TiO2 was hydrothermally aged. Thus, its hydrothermally aged failure-tolerant control performances could be remarkable improved by means of Lyapunov-based MRAC.

Virtual-command-based model reference adaptive control for abrupt structurally damaged aircraft

Although a high-gain learning rate can offer ideal tracking performance in adaptive control in theory, it can also lead to high-frequency oscillations in practice due to the unmodeled dynamics of the system. In aircraft structural damage scenarios, the strong uncertainty and the safety-critical nature of the problem make this conflict critical. In this paper, a novel virtual-command-based model reference adaptive control (MRAC) scheme for flight control is proposed. In the new framework, the direct relationship between the learning law and the actual tracking error is broken; instead, a virtual command is introduced as the input to the standard MRAC controller. The key feature is that even when the virtual tracking error is large, the actual tracking error can be maintained within a small range; thus, the MRAC learning rate does not necessarily need to be large to suppress the virtual transient tracking error, which is greatly beneficial for the robustness of the MRAC controller. The proposed method is illustrated by the attitude control of the 6-DOF nonlinear Generic Transport Model in a scenario with a broken left wing tip.

Dissolved Oxygen Control of Batch Bioreactor using Model Reference Adaptive Control scheme

Bioreactor imparts a significant role in the manufacture of pharmaceuticals, enzymes, food products, etc. as these processes depend on the biotransformation catalyzed by microorganisms. Dissolved Oxygen(DO) is one of the significant parameter in an aerobic fermentation process. DO control is difficult to achieve due to the variations in process dynamics during batch/fed-batch processes and the complex nonlinear behavior of the Bioreactor. In this paper, design and implementation of Model Reference Adaptive Control(MRAC) scheme based on MIT rule is applied to DO control of the bioreactor using the stirrer speed as control signal. A PC-supported, fully automated, multi-task control system has been designed and built by the authors using LabVIEW. A comparative study is carried out for the experimental bioreactor with conventional PI controller and proposed MRAC scheme for DO control. Results show that MRAC controller provides good tracking performance in comparison to PI controller.

Model Reference Adaptive Control Based Estimation of Equivalent Resistance and Reactance in Grid-Connected Inverters

The model of a grid-connected inverter used for the corresponding controller design depends on the equivalent resistance and inductance between the inverter and the grid. These parameters are not fixed and change with operating conditions, temperature, and age. This paper analyses the effectiveness of the application of the model reference adaptive control (MRAC) approach in obtaining an accurate model of a grid-connected inverter system. Active and reactive power based MRAC approaches are used to estimate the equivalent resistance and reactance between the inverter and the grid. Detailed stability analysis of the MRAC approach is presented. The system models for the MRAC controller design are derived and guidelines on the controller parameters selection are proposed. Experimental results validate the feasibility of the proposed scheme.

Optimization-based model reference adaptive control for dynamic positioning of a fully actuated underwater vehicle

This paper presents a predictive optimization-based model reference adaptive control (MRAC) approach for dynamic positioning (DP) of a fully actuated underwater vehicle subject to dynamic uncertainties and actuator saturation. Compared with conventional linear reference model-based approaches, this proposed MRAC controller utilizes an optimized reference model composed of the closed-loop approximate vehicle model under a nonlinear model predictive controller, in which both the state and input constraints are considered. An adaptive dynamic inversion controller is designed to track the reference trajectory in the presence of dynamic uncertainties, and a single hidden layer neural network is incorporated to compensate for the mismatch of the actual and approximate models and ensure the convergence of tracking errors. The effectiveness of the proposed DP approach is validated by comparative simulations performed with a remotely operated vehicle.

Design, analysis and modelling of a hybrid controller for serial robotic manipulators

SUMMARYWhen the end-effector of a robotic arm grasps different payload masses, the output of joint motion will vary. By using a model reference adaptive control approach, the payload variation effect can be solved. This paper describes the design for a hybrid controller for serial robotic manipulators by combining a PID controller and a model reference adaptive controller (MRAC) in order to further improve the accuracy and joint convergence speed performance. The convergence performance of the PID controller, the MRAC and the PID+MRAC hybrid controller for 1-DOF, 2-DOF and subsequently 3-DOF manipulators is compared. The comparison results show that the convergence speed and its performance for the MRAC and the PID+ MRAC controllers is better than that of the PID controller, and the convergence performance for the hybrid control is better than that of the MRAC control.

Convergence performance comparisons of PID, MRAC, and PID + MRAC hybrid controller

This study proposes a hybrid controller by combining a proportional-integral-derivative (PID) control and a model reference adaptive control (MRAC), which named as PID + MRAC controller. The convergence performances of the PID control, MRAC, and hybrid PID + MRAC are also compared. Through the simulation in Matlab, the results show that the convergence speed and performance of the MRAC and the PID + MRAC controller are better than those of the PID controller. In addition, the convergence performance of the hybrid control is better than that of the MRAC control.

Comparison of PI Controller, Model Reference Adaptive Controller and Fuzzy Logic Controller for Coupled Tank System

Objective: The Coupled Tank System is modeled and its level should be controlled. Methods: The conventional Proportional Integral (PI) controller, Model Reference Adaptive Controller (MRAC) and Fuzzy Logic Controller are used, to control the level of the second tank. The MRAC can alter the controller parameters in response to changes in plant and the reference model indicates properties of the desired control system. The Fuzzy Logic Controller uses a set of rules to control the plant. Findings: The simulation is done, which demonstrates that the MRAC controller delivers better response compared to the PI controller and Fuzzy Logic Controller. Applications: The Model Reference Adaptive controller can be implemented in various level control applications such as conical tank system, spherical tank system, etc.

Controlling an unmanned quad-rotor aerial vehicle with model parameter uncertainty and actuator failure

It is challenging to stabilise an unmanned quad-rotor aerial vehicle when a dynamic change in its model parameters or failure of its actuator occurs. In this paper, a quad-rotor unmanned aerial vehicle (UAV) is controlled based on model reference adaptive control (MRAC) and a linear quadratic regulator (LQR). The kinematics and dynamics of the quad-rotor are calculated, and Lyapunov's direct stability method is used to design the MRAC. In order to evaluate the performance of the adaptive control algorithms in the presence of thrust loss that may occur due to component failure or physical damage, a real quad-rotor is built from scratch using commercial components. Both controllers are designed, implemented and tested using AVR microcontrollers. Comparison is made between the controllers under normal and faulty situations and the effectiveness of the proposed control strategy is verified. Simulation and experimental results show that both controllers have satisfactory performance under normal conditions and even in the presence of the partial loss of thrust that may occur due to the loss of control effectiveness in one of the rotors or the damage of one propeller, superior system performance is observed using the proposed MRAC controller.