Conventional Model Reference Adaptive Control Research Articles

Flexible Quadrotor Unmanned Aerial Vehicles: Spatially Distributed Modeling and Delay-Resistant Control

This paper introduces an analytical framework for the derivation of distributed-parameter equations of motion of a flexible quadrotor. This approach helps obtain rigid and elastic equations of motion simultaneously, in a decoupled form, to facilitate the controller design. A delay-resistant low-frequency adaptive controller is employed, which prevents excessive oscillations due to flexible dynamics, compensates uncertainties, and addresses the inherent time delay. In addition to this, a delay-dependent stability condition for the overall system dynamics is obtained including the human operator with reaction time delay, the adaptive controller, and the flexible quadrotor dynamics with input delay. With comparative simulation studies, it is demonstrated that the flexible arm tip oscillations are significantly reduced when the low-frequency delay-resistant closed-loop reference model adaptive controller is used, compared to a closed-loop reference model adaptive controller and a conventional model reference adaptive controller.

On the hybrid MRAC-PID control: A comparison study

This study performs a comparative analysis of three Model Reference Adaptive Control (MRAC) schemes grounded in Lyapunov theory. Specifically, it compares a pure MRAC controller with two hybrid MRAC-PID schemes, each integrating the PID component in a distinct manner within the control structure. The aim is to address a significant gap in the existing literature related to the lack of comparisons involving the different ways the MRAC and PID control can be hybridized. The developed controllers undergo extensive testing in simulated scenarios emphasizing model reference convergence and disturbance rejection. The results underscore that hybridizing the MRAC mechanism with a PID controller offers notable advantages in both performance and robustness, especially concerning disturbance rejection, a scenario in which the hybrid controllers improved by around 70 and 75% the mean square error obtained with a pure MRAC controller. In general, these findings highlight the potential benefits of combining MRAC and PID techniques, providing insight into the improved control system performance that PID control can bring to conventional MRAC schemes.

Robustification of the state-space MRAC law for under-actuated systems via fuzzy-immunological computations.

This paper formulates an enhanced Model-Reference-Adaptive-Controller (MRAC) that is augmented with a fuzzy-immune adaptive regulator to strengthen the disturbance-attenuation capability of closed-loop under-actuated systems. The proposed scheme employs the conventional state-space MRAC and augments it with a pre-configured fuzzy-immune mechanism that acts as a superior regulator to dynamically modulate the adaptation gains of the Lyapunov gain-adjustment law. The immunological computations increase the controller's adaptability to flexibly manipulate the damping control effort under exogenous disturbances. The efficacy of the proposed Immune-MRAC law is comparatively analyzed under practical disturbance conditions by conducting real-time hardware experiments on the QNET rotary pendulum. The experimental outcomes validate the faster transient-recovery behavior and stronger damping effort of the proposed control law against the exogenous disturbances while preserving the system's asymptotic stability and control energy efficiency.

Tube-based Model Reference Adaptive Control for Vibration Suppression of Active Suspension Systems

With the developments of industrial automation in recent years, vehicle suspension systems have received a great deal of attention in industry and academia due to their critical role in the chassis performance of vehicles [1]. The suspension system is expected to guarantee the vehicle's maneuverability and provide satisfactory ride comfort by absorbing the vibrations arising from the road surface excitations and ensuring road-holding capability and suspension safety. Motivated by the desirable performance of the model reference adaptive control (MRAC) approach, various literature studies have investigated its performance in diverse linear and nonlinear practical systems [2], [3] (for more background review, see Supplementary material-Appendix A). Aiming to improve the conventional MRAC in terms of closed-loop stability, asymptotical tracking, and robustness against uncertainties and external disturbances, authors in [4] developed the tube-based MRAC (T-MRAC) approach. To simultaneously achieve the desired objectives, the developed scheme split the control signal into two main terms; an adaptive part and another part that corrects the control objective. In addition, an optimization problem is formulated to find the newly determined correction control component. However, although the method's performance was verified using some simple single-input single-output unstable systems [5], more extensive investigations on complex practical systems are needed to validate its performance. The main contributions of this paper are to develop a novel tube-based MRAC approach for multi-input multi-output active suspension systems; a realistic design that guarantees ride comfort and suspension safety and achieves better control performance with reduced control effort in the presence of external disturbances compared to conventional methods. Moreover, to validate the method's applicability in practical applications, three different road profiles are conducted to simulate distinct practical road situations, as the bump-dip road, random road, and sinusoidal profiles. Furthermore, the vibration suppression performance of the developed adaptive control scheme is investigated, and superior simultaneous ride comfort, road handling, and suspension safety are guaranteed compared to conventional MRAC [6] and SMC [7] approaches.

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.

Yaw-Rate-Tracking-Based Automated Vehicle Path Following: An MRAC Methodology With a Closed-Loop Reference Model

Abstract Concerning automated vehicles, various path-following controllers have been designed by the model reference adaptive control (MRAC) approach. Through appropriate Lyapunov redesigns, asymptotical stability and signal boundedness are ensured for the path-tracking control loops. However, transient behaviors of the closed-loop responses are seldom considered in the context of MRAC synthesis. To bridge the foregoing gap, a closed-loop-reference-model-based MRAC, which yields an improved transient performance compared with a traditional MRAC, is exploited to synthesize a vehicular path-following control law. Besides, an infinitely differentiable projection operator is complemented to the control parameters’ adaptation schemes for estimation speed-up and robustness enhancement. Hardware-in-the-loop experiments are used to evaluate the proposed method and to demonstrate its improvement over some conventional MRAC designs.

Improved ANFIS based MRAC observer for sensorless control of PMSM

Detection of the rotor position is an important prerequisite for controlling the speed and developed torque in permanent magnet synchronous motor (PMSM). Even though use of incremental encoder and resolver is one of the popular schemes for sensing the rotor position in a PMSM drive, it increases the size and weight of the drive and reduces its reliability. Dynamic modeling of the motor and control algorithms are often used in sensor-less control of PMSM to estimate rotor position and motor speed. Most sensor-less control algorithms use machine parameters like torque constant, stator inductances and stator resistance for estimating the rotor position and speed. However, with accuracy of such estimation and the performance of the motor degrades with variation in motor parameters. Model reference adaptive control (MRAC) provides a simple solution to this issue. An improved Adaptive neuro-fuzzy inference system (ANFIS) based MRAC observer for speed control of PMSM drive is presented in this paper. In the proposed method adaptive model and adaptive mechanism are replaced by an improved ANFIS controller, which neutralize the effect of parametric variation and results in improved performance of the drive. The modeling equations of PMSM are used to estimate the rotor position for speed and torque control of the drive. Simulation studies have been carried out under various operating condition using MATLAB/Simulink. In addition, a comparative analysis of the conventional MRAC based observer and improved ANFIS based MRAC observer is carried out. It is observed that the proposed method results in better performance of the PMSM drive.

Managing driving disturbances in lateral vehicle dynamics via adaptive integrated chassis control

This paper presents a vehicle stability control method based on a multi-input multi-output (MIMO) model reference adaptive control (MRAC) strategy as an advanced driver assistance system (ADAS) to enhance the handling and yaw stability of the vehicle lateral dynamics. The corrective yaw moment and additive steering angle are generated using direct yaw moment control (DYC) and active front steering (AFS) at the upper control level in the hierarchical control algorithm. A nonlinear term is added to the conventional adaptive control laws to handle parametric uncertainties and disturbances. The desired yaw moment generated by the upper-level controller is converted to the brake forces and is distributed to the rear wheels by an optimal procedure at the lower-level. The major contribution of this study is the introduction of a nonlinear integrated adaptive control method based on a constraint optimization algorithm. To verify the effectiveness of the proposed control strategy, the nonlinear integrated adaptive controller, and linear time-varying MRAC are designed and used for comparison. Simulation results are performed for the J-turn and double lane change (DLC) manoeuvres at high speeds and low tyre-road friction coefficients. The desired performance of the proposed controller exhibited significant improvement compared to the conventional MRAC in terms of yaw rate tracking and handling of sideslip limitation.

Neural Network based Direct MRAC Tech-nique for Improving Tracking Performance for Nonlinear Pendulum System

This paper investigates the application of a neural network-based model reference adaptive intelligent controller for controlling the nonlinear systems. The idea is to control the plant by minimizing the tracking error between the desired reference model and the nonlinear system using conventional model reference adaptive con-troller by estimating the adaptation law using a multilayer backpropagation neural network. In the conventional model reference adaptive controller block, the controller is designed to realize the plant output converges to reference model output based on the plant, which is linear. This controller is effective for controlling the linear plant with unknown parameters. However, controlling of a nonlinear system using MRAC in real-time is difficult. The Neural Network is used to compensate the nonlinearity and disturbance of the nonlinear pendulum that is not taken into consideration in the conventional MRAC therefore, the proposed paper can significantly improve the system behaviour and force the system to behave the reference model and reduce the error between the model and the plant output. Adaptive law using Lyapunov stability criteria for updating the controller parameters online has been formulated. The behaviour of the proposed control scheme is verified by developing the simulation results for a simple pendulum. It is shown that the proposed neural network-based Direct MRAC has small rising time, steady-state error and settling time for a different disturbance than Conventional Direct MRAC adaptive control.

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.

Model Reference Robust Adaptive Control of Control Element Drive Mechanism in a Nuclear Power Plant

In the nuclear power plant, the control element drive mechanism (CEDM: A type of solenoid actuators) is used to insert nuclear fuel rods into the core to regulate the neutron absorption within the reactor. The control environment is full of uncertainty and disturbance, and the CEDM is supposed to cope with the varying conditions in the reactor. In this paper, a model reference adaptive control (MRAC) of CEDM is investigated. The existing MRAC scheme that assumes the known system order is first extended to the system with unknown high-frequency gain and output disturbance, and then the extended version is applied to the control problem of CEDM. A simplified relationship between the input voltage and the magnetic force of the CEDM is established based on Kirchhoff’s voltage law and the magnetic co-energy theory. The system order of the plant is identified by the prediction error method using experimental input/output data: A 3rd order linear model with three poles and two zeros turns out to be the best model for control. Based on this model, a 3rd order reference model is selected, and an adaptive control law with s-modified adaptation laws is designed under output disturbance. The performance of the proposed control law is compared with a well-tuned PI controller and the conventional MRAC. The superiority of the proposed method is also demonstrated in an abnormal condition. Simulation and experiment results are provided.

Reconfigurable Nonlinear Dynamic Inversion for Attitude Control of a Structurally Damaged Aircraft

Structural damage induces extensive parameter changes of an aircraft, which leads to the mismatch problem between the offline model and a flying aircraft. Due to extensive parameter changes, motion of a damaged aircraft is difficultly described with a mathematical model. This article uses disturbances in accelerations and angular accelerations to quantify the effects of extensive parameter changes, and derives a mathematical model for a damaged aircraft. Provided that the flight controller of a damaged aircraft has a fixed structure, the mismatch problem may cause the aircraft unstable. Based on the proposed mathematical model, this article proposed a reconfigurable nonlinear dynamic inversion (RNDI) controller for attitude control of a structurally damaged aircraft, which synthesizes a nonlinear disturbance observer (NDO) with a modified nonlinear dynamic inversion (NDI) controller. The NDO is explored to estimate the disturbances in angular accelerations. Afterwards, the estimations are fed into the modified NDI controller realizes control structure reconfiguration. Conventional NDI control law and model reference adaptive control (MRAC) law are compared, and simulation results demonstrate that the proposed RNDI control law is robust to structural damage and possesses satisfactory control performance.

Trajectory Regulating Model Reference Adaptive Controller for Robotic Systems

The new trajectory regulating model reference adaptive controller (TRMRAC) has been proposed in this brief. The intermediate reference model in the TRMRAC is self-regulated to enhance the stability and robustness of adaptation, with the trajectories of the controlled system proven to be ultimately uniformly bounded. The developed controller is implemented and simulated in a multivariable robotic arm system with its system dynamics approximated by a neural network, showing superior stability characteristics even under unmodeled actuator dynamics and input saturation. To demonstrate its practicality, a nested version of the controller was tested on a quadcopter for quaternion attitude tracking, showing enhanced robustness over the conventional model reference adaptive control strategy.

Optimization of PID for industrial electro-hydraulic actuator using PSOGSA

The Electro-hydraulic actuator (EHA) systems known to be extremely nonlinear due to its dynamic characteristics and these existing nonlinearities and uncertainties yield to the constraint in the control of EHA system, which influences the position tracking accuracy and affect the occurrences of leakage and friction in the system. The purpose of this work is to develop the mathematical model for the industrial electrohydraulic actuator, then to design a controller by proportional-integral-derivative (PID) and optimize the parameters using Particle Swarm Optimization - Gravitational Search Algorithm (PSOGSA). A few controllers such as conventional PID (CPID) and model reference adaptive control (MRAC) designed for comparison. The performance of PID, PID-PSOGSA and modern controller MRAC will be compared in order to determine the most efficient controller. Despite all controllers are capable to provide good performance, PID-PSOGSA control methods generate good response compared to PID and MRAC in term of positioning.

Initialized Model Reference Adaptive Control for Lower Limb Exoskeleton

In this paper, a Proportional-Integral-Derivative (PID) controller tuning scheme by Initialized Model Reference Adaptive Control (IMRAC) for a Lower Limb Exoskeleton (LLE) is presented. Mathematical expression of the LLE structure is determined using Lagrangian and Kirchoff's equations. The transfer function of the structure based on the physical features of the links, and actuators is established under Range of Motion (RoM) condition. The PID controller of the LLE is tuned in a closed-loop control system using Ziegler-Nichols (Z-N) for initializing parameters of IMRAC. Adjustment mechanism is a gradient based method for real-time adaptation of tuned PID controller. A Lyapunov function has been applied to confirm the stability of IMRAC. The proposed IMRAC shows faster convergence in comparison with conventional non-initialized model reference adaptive control. It can be ascertained the proposed tuning scheme is applicable for real-time tuning of PID controller of LLE.

Adaptive control of a jet turboshaft engine driving a variable pitch propeller using multiple models

In this paper, a multiple model adaptive control (MMAC) method is proposed for a gas turbine engine. The model of a twin spool turbo-shaft engine driving a variable pitch propeller includes various operating points. Variations in fuel flow and propeller pitch inputs produce different operating conditions which force the controller to be adopted rapidly. Important operating points are three idle, cruise and full thrust cases for the entire flight envelope. A multi-input multi-output (MIMO) version of second level adaptation using multiple models is developed. Also, stability analysis using Lyapunov method is presented. The proposed method is compared with two conventional first level adaptation and model reference adaptive control techniques. Simulation results for JetCat SPT5 turbo-shaft engine demonstrate the performance and fidelity of the proposed method.

Modelling and Simulation of Non Linear Spherical Tank Level Process

Control of process parameters is one of the most important problems in process industry. The process is considered for modeling of spherical tank liquid level system. Control of liquid level spherical tank is nonlinear due to the variation in particular area of cross section of the level system with change in shape. The aim of this paper is to implement an optimum controller for a spherical tank. The main objective of this controller is to maintain the level inside the process tank in desired value. System identification of this nonlinear process is done using piece-wise models. The control system is intended to maintain the level of the tank at desired set point. Conventional PID controller is a powerful controller used in this process industry to regulate and control process variable without the nonlinearity. In this work consider the nonlinearity of the system so, different controllers are used for maintain the level. According to the nonlinearity, need to implement the conventional controllers like PI and PID, Model Reference Adaptive Controller for adapt the external behavior of the system, IMC based PI controller and Model Based Skogestad Controller (SIMC). Controller design is compared based on conventional controllers and Model Reference Adaptive Controller with Internal Model Control (IMC) based on Skogested's settings in terms of performance. Out of the control algorithms SIMC performs in no overshoot, faster settling time, better set point tracking.

On the Discrete Adaptive Posicast Controller

In this paper, we present the discrete version of the Adaptive Posicast Controller (APC) that deals with parametric uncertainties in systems with input time-delays. The continuous-time APC is based on the Smith Predictor and Finite Spectrum Assignment with time-varying parameters adjusted online. Although the continuous-time APC showed dramatic performance improvements in experimental studies with internal combustion engines, the full benefits could not be realized since the finite integral term in the control law had to be approximated in computer implementation. It is shown in the literature that integral approximation in time-delay compensating controllers degrades the performance if care is not taken. In this work, we present a development of the APC in the discrete-time domain, eliminating the need for approximation. In essence, this paper attempts to present a unified development of the discrete-time APC for systems that are linear with known/unknown input time-delays. Performances of the continuous-time and discrete-time APC, as well as conventional Model Reference Adaptive Controller (MRAC) for linear systems with known time-delay are compared in simulation studies. It is shown that discrete-time APC outperforms its continuoustime counterpart and MRAC. Further simulations studies are also presented to show the performance of the design for systems with uncertain time-delay.

Variable Reference Model for Model Reference Adaptive Control System

In a conventional model reference adaptive control system, a reference model which has desired response characteristics is designed in advance. However, there is a fear that a control output cannot follow a reference model output under the model uncertainty. In this paper, we propose an adaptive control scheme with variable reference model based on the partial model matching method and show the effectiveness by a numerical simulation.

Wavelet Neural Network Model Reference Adaptive Control Trained by a Modified Artificial Immune Algorithm to Control Nonlinear Systems

This paper presents a wavelet neural network (WNN)-based model reference adaptive control (WNNMRAC) scheme to control arbitrary complex nonlinear systems. As the training method for the WNN, a newly developed optimization technique, called the micro artificial immune system (Micro-AIS), is employed to find the optimal values for the WNN parameters. Two modifications were suggested to enhance the performance of the original Micro-AIS, resulting in a more powerful optimization algorithm. Utilizing the proposed control approach, it is not necessary to construct a pseudo-plant, which was a prerequisite in other works, for controlling the nonlinear systems. To demonstrate the effectiveness of the proposed direct WNNMRAC, three single-input single-output complex nonlinear systems are selected, including a non-minimum phase system, a time-delay system, and a minimum phase system. From several performance evaluation tests, the WNNMRAC has shown its effectiveness in terms of accurate control performance, applicability to different types of nonlinear systems, robustness to external disturbances, and good generalization ability. In addition, a simulation test to control nonlinear multi-input multi-output (MIMO) system has shown that the WNNMRAC can be extended to control nonlinear MIMO systems. Finally, from a comparative study, the WNNMRAC has confirmed its superiority over a conventional neural network model reference adaptive control.