Model Reference Adaptive Control Research Articles

Adaptive nonlinear controllers based approach to improve the frequency control of multi islanded interconnected microgrids

Islanded microgrids (MGs) are now widely used to electrify rural areas at a lesser cost and with greater efficiency. To maintain system balance and guarantee stability when exposed to various disturbances, MGs should be equipped with efficient controllers. Traditional controllers (like the PI controller) are linear and offer the best performance at a certain operating point, but the performance may degrade when the operating situation changes. To mitigate the drawbacks of fixed parameters controller, the paper suggested a fuzzy PI controller-based model reference adaptive control (FPI-MRAC) optimized by an advanced meta-heuristic optimization technique coronavirus herd immunity optimizer (CHIO) for enhancing the dynamic performance of several interconnected MGs. The proposed controller is non-linear adaptive controller that can improve the system performance over a wide range of operating conditions. The effectiveness of FPI-MRAC is assessed by subjecting the system to various disturbances, such as generation variation, load change, changing in uncertain system parameters and occurrence short circuit faults. Additionally, it investigated how quick reaction supercapacitors can improve the dynamic performance of the system. The acquired results show that, for all applied scenarios, the FPI-MRAC offers a much superior dynamic response than PI controller. Using super-capacitors also improves the system frequency when there are disruptions.

Pitch Angle Control of an Airplane Using Fractional Order Direct Model Reference Adaptive Controllers

This paper deals with the longitudinal movement control of an airplane (pitch angle) using fractional order adaptive controllers (FOACs). It shows the improvements achieved in the plane’s behavior, in terms of the minimization of a given performance index. At the same time, less control effort is needed to accomplish the control objectives compared with the classic integer order adaptive controllers (IOACs). In this study, fractional order direct model reference adaptive control (FO-DMRAC) is implemented at the simulation level, and exhibits a better performance compared with the classic integer order (IO) version of the DMRAC (IO-DMRAC). It is also shown that the proposed control strategy for FO-DMRAC reduces the resultant system control structure down to a relative degree 2 system, for which the control implementation is simpler and avoids oscillations during the transient period. Moreover, it is interesting to note that this is the first time that an FOAC with fractional adaptive laws is applied to the longitudinal control of an airplane. A suitable model for the longitudinal movement of the airplane related to the pitch angle θ as the output variable with the lifter angle (δe) as the control variable, is first analyzed and discussed to obtain a reliable mathematical model of the plane. All of the other input variables acting on the plane are considered as perturbations. For certain operating conditions defined by the flight conditions, an FO-DMRAC is designed, simulated, and analyzed. Furthermore, a comparison with the implementation of the classical adaptive general direct control (relative degree ≥ 2) is presented. The boundedness and convergence of all of the signals are theoretically proven based on the new Lemma 3, assuring the boundedness of all internal signals ω(t).

The role of closed-loop attitude dynamics in adaptive UAV position control

This paper presents the design and the stability analysis of an adaptive position controller for Unmanned Aerial Vehicles (UAVs). Considering a hierarchical control scheme, the novelty of this work is the definition of a systematic approach to design a position controller based on Model Reference Adaptive Control (MRAC) theory taking into account not-fast closed-loop attitude dynamics. After having reformulated the problem considering the attitude dynamics as pseudo-actuator, the authors exploit an existing Linear Matrix Inequality (LMI) based hedging framework designed such that the adaptation performance is not affected by the presence of actuator dynamics. Results from simulations and from experiments on a platform designed to replicate the longitudinal motion of quadrotors are provided to illustrate the performance of the proposed control scheme.

Leader‐following consensus of a class of heterogeneous uncertain multi‐agent systems with a distributed model reference adaptive control law

SummaryThis paper considers the leader‐following consensus problem of a multi‐agent system whose agents have heterogeneous uncertain dynamics in homogeneous state dimensions. Each agent utilizes a distributed model reference adaptive control (MRAC) law to deal with uncertainties. The nominal part of the MRAC is a distributed static state feedback control law. The reference model is the leader‐following consensus problem of reference agents with homogeneous linear time‐invariant dynamics. This problem becomes an N‐player graphical differential game under a given cost function. The reference agents utilize distributed static state feedback controllers that constitute a Nash equilibrium solution to the graphical differential game. This paper provides the conditions for the distributed MRAC to guarantee that each agent asymptotically tracks the corresponding reference agent; consequently, the multi‐agent system solves the leader‐following consensus problem as the reference model does. These conditions yield a straightforward design method for the MRAC. A numerical example demonstrates an application of the proposed approach to a multi‐robot system with nonlinear dynamics.

Novel Lyapunov-based rapid and ripple-free MPPT using a robust model reference adaptive controller for solar PV system

The technological, economic, and environmental benefits of photovoltaic (PV) systems have led to their widespread adoption in recent years as a source of electricity generation. However, precisely identifying a PV system's maximum power point (MPP) under normal and shaded weather conditions is crucial to conserving the maximum generated power. One of the biggest concerns with a PV system is the existence of partial shading, which produces multiple peaks in the P–V characteristic curve. In these circumstances, classical maximum power point tracking (MPPT) approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point (GMPP). To overcome such obstacles, a new Lyapunov-based Robust Model Reference Adaptive Controller (LRMRAC) is designed and implemented to reach GMPP rapidly and ripple-free. The proposed controller also achieves MPP accurately under slow, abrupt and rapid changes in radiation, temperature and load profile. Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods, i.e., ANFIS, INC, VSPO, and P&O. MPP and GMPP are accomplished in less than 3.8 ms and 10 ms, respectively. Based on the results presented, the LRMRAC controller appears to be a promising technique for MPPT in a PV system.

A novel robust maximum power extraction framework for sustainable PV system using incremental conductance based MRAC technique

AbstractThis study proposes a maximum power point tracking (MPPT) approach with two components: an Incremental Conductance (INC) MPPT for reference voltage regulation and a Model Reference Adaptive Controller (MRAC) for adjusting the duty cycle of the DC‐DC converter switch. A robustness test is performed on the system considering real‐world situations that involve an abrupt change in atmospheric conditions with load uncertainties. The probabilistic load distribution analysis is accomplished through levels of uncertainty (i.e., Probabilistic DOWN and UP) to guarantee the operation of the proposed INC‐MRAC controller while generating unexpected disturbances in the system. Using MATLAB/Simulink, the performances of the novel INC‐MRAC MPPT are comparatively analyzed with PO, INC, VSSPO, and ANN under realistic case studies in seven states. After comparative analysis, it is evident that the proposed MPPT offers less tracking time, i.e., 3.8 ms, to track maximum power point (MPP) with negligible steady‐state oscillation and ripples. It is about 3, 7, 9, and 10 times faster than ANN, VSSPO, INC and PO, respectively. Moreover, the tracking efficiency of the proposed controller is up to 99.63% as well as overall efficiency of the system is more than 98%. The tracking power loss and error rate in finding MPP for the proposed controller is the lowest among all state‐of‐the‐art MPPT approaches. Finally, the effectiveness of the proposed INC‐MRAC approach is experimentally validated by employing a real‐time simulator OPAL‐RT (OP4510). The study helps in environmental protection and participation in sustainable development.

Distributed fault‐tolerant control for over‐actuated multi‐agent systems with uncertain perturbations using control allocation

AbstractThis paper proposes a distributed fault‐tolerant control for over‐actuated multi‐agent systems with uncertain perturbations and faults including partial loss of effectiveness or even complete failures. The entire distributed control law is divided into two parts that are the distributed observer and the decentralised controller with a hierarchical structure. The adaptive distributed observer estimates not only the leader's state, but also the leader's system matrix, which makes the whole distributed design more rigorous. For each over‐actuated subsystem, the control allocation based hierarchical control structure is used. The virtual control is developed based on adaptive optimal control approaches to cope with uncertain perturbations robustly. The model reference adaptive control is used to design the control allocator without the need for the fault detection and diagnosis module. The stability and robustness of the systems are rigorously demonstrated, and the feasibility and effectiveness of the control law are verified in simulations on the attitude dynamics system of spacecraft.

IMPROVED ADAPTIVE NONLINEAR CONTROL FOR VARIABLE SPEED WIND-TURBINE FED BY DIRECT MATRIX CONVERTER

This paper proposes a robust decoupling power algorithm based on a doubly fed induction generator (DFIG) for variable speed wind-turbine (WT). The DFIG rotor circuit is fed by the direct matrix converter (DMC), which presents several features such as no need to the dc-bus voltage, sinusoidal supply, rotor side waveforms, bidirectional power flow, and adjustable input power factor. The 18 bidirectional switches are controlled using the Venturini modulation technique. On the other hand, the DFIG stator circuit is connected directly to the grid. The nonlinear control strategy based on feedback linearization is applied to control the stator power (Ps and Qs) independently using the rotor quadrature and direct currents (irq and ird), which present the images of the previous stator powers. Some limitations appear in the power algorithm using the conventional pi controller, especially in power tracking, error, and quality. In this context, the model reference adaptive controller (MRAC) presents an alternative solution, a robust and efficient controller proposed instead of the pi controllers to control stator powers. Finally, the simulation results confirm that the proposed algorithm could work under hard conditions and demonstrate that the wind energy conversion system (WECS) provides enhanced dynamic responses in transient and steady states and good power quality delivered to the grid.

A Droop-Based Adaptive Power Management System for Energy Storage Integration to DC Grid Using a Modified Dual Active Bridge Converter

This article proposes an adaptive power management system (APMS) for energy storage integration to the dc grid using a modified dual active bridge (DAB) bi-directional dc–dc converter (BDC). The converter can achieve high voltage (HV) gain and zero voltage switching (ZVS) for a wide range of loading conditions with distributed voltage stress. An APMS is designed with a voltage droop control-based model reference adaptive control (MRAC). A reduced-order harmonic model of the converter is used for the APMS design. The proposed APMS does not require detailed knowledge of the system parameters and is also robust to uncertainty and multiple disturbances. The proposed converter and its control technique are verified in MATLAB/SIMULINK platform. An experimental laboratory prototype is also developed for 500 W rating, and the results are presented to validate the system’s efficacy by comparing it with conventional proportional-integral (PI) and sliding mode controllers.

Reduced-Sensor-Based Multistage Model Reference Adaptive Control of PV-Fed PMSM Drive for Water Pump

Typically, photovoltaic (PV) array fed permanent magnet (PM) synchronous motor based water pumping system uses a current sensor to realize maximum power point tracking (MPPT) of PV array, and an encoder to obtain rotor speed. These sensors reduce reliability and increases cost of system. Besides, they are sensitive to both the temperature and electromagnetic noise. This necessitates the elimination of both the PV current sensor and encoder from the system. Thus, first, a multistage model reference adaptive control (MS-MRAC) for encoderless operation of system is presented in this article. The MS-MRAC is a three-stage structure wherein motor speed is estimated by first-stage, while resistance and PM flux linkages are estimated by the remaining two stages. Thus, the MS-MRAC is independent of motor parameters like resistance and PM flux linkages unlike conventional MRAC. Then, the motor speed as estimated by MS-MRAC is used to estimate PV array power, which along with PV voltage is used to realize MPPT, thereby eliminating PV current sensor. The effectiveness of presented control technique is shown through simulated results followed by their experimental validation under different operating scenarios.

Model reference adaptive control based on a novel scalar update law

AbstractThis paper puts forward a model reference adaptive control (MRAC) framework with a compensator based on a novel scalar update law for the dynamical systems with matched uncertainty. The scalar update law is given by an algebraic expression of basis functions and system errors. The main advantage of the proposed MRAC framework is that only a scalar function requires updating online such that the architecture is simple and the computational burden in the existing results can be relieved. Furthermore, the asymptotic stability of the system error dynamics is guaranteed by the proposed controller. Two numerical examples are given to demonstrate the effectiveness of the proposed MRAC framework.

Distributed fault‐tolerant control for high‐speed trains based on adaptive terminal sliding mode control

AbstractHere, a distributed multi‐body dynamics model of high‐speed train (HST) with actuator faults is established. Based on this detail model, a non‐singular terminal sliding mode control method is designed for HST distributed fault‐tolerant control, and the stability of the proposed controller is proved strictly. Considering the high uncertainty of the distributed dynamics model, a radial basis function neural network is introduced to approximate the system uncertainties. Then a universal fuzzy system is proposed to eliminate the chattering problem from sliding mode controller in train control, which further improves the comfort of the train. Finally, numerical simulations are carried out using data from CRH380B train, and model reference adaptive control (MRAC) and adaptive sliding mode control are also tested under the same condition for performance comparison. The results confirm the effectiveness of the proposed controller. Compared with existing MRAC, the root mean square of the speed and position tracking error are reduced by 23.4% and 52%, respectively.

Output feedback Model Reference Adaptive Control of nuclear reactor

This paper presents an output feedback Model Reference Adaptive Controller (MRAC) for controlling the total power of a nuclear reactor. It ensures set point tracking and estimation of unmeasurable states in the presence of unknown external disturbances and parametric uncertainties. A mathematical model of reactor core based on normalized point kinetic equations, with six groups of delayed neutron precursors, is considered along with reactivity feedbacks due to lumped fuel and coolant temperature. Parametric variations in thermal feedback coefficients are taken as uncertainties, reactivity disturbance is considered as a matched external disturbance and change in inlet coolant temperature is considered as an unmatched disturbance. The desired setpoints for reactor power control are generated by choosing a stable reference model based on LQR theory. An observer is designed based on the asymptotic properties of LQG/LTR for the estimation of unmeasurable states as well as external disturbances. Adaptive laws based on output error are developed to compensate for the effect of matched uncertainties and external disturbances. Further, a feedforward controller is augmented to the adaptive laws to compensate for unmatched disturbances. Overall stability and ultimate boundedness of the closed loop signals are analyzed with Lyapunov theory. Performance of the proposed controller is demonstrated through dynamic nonlinear simulations.

Self-tuning methodology for adaptive controllers based on genetic algorithms applied for grid-tied power converters

The performance and stability of adaptive controllers is highly dependent on its parameter initialization. For non-expert designers, the parametrization of these controllers can be highly time consuming and an exhausting task. In order to improve the response of adaptive controllers without needing more specialized knowledge, this work proposes a procedure for self-tuning (optimal parametrization) of direct type adaptive controllers using a genetic algorithm. The proposal does not harm any property of stability and convergence of the adaptive strategy, only adding an off-line stage of parameter selection, which is carried out in a reasonable computational time. As a case study, this self-tuning procedure is applied for initialization of nine parameters of a Robust Model Reference Adaptive Controller and Adaptive Super-Twisting Sliding Mode, a known adaptive structure from the literature, suitable for current regulation of three-phase voltage source converters operating under grid impedance variations. For comparison with the proposed self-tuning, a similar procedure using fmincon and simulated annealing algorithms are also tested. The experimental results show that the proposed procedure using Genetic Algorithm can provide lower tracking error, faster regulation dynamics and reduced settling time, leading to better performance than the same controller with parameters initialized empirically.

Optimally Initialized Model Reference Adaptive Controller of Wearable Lower Limb Rehabilitation Exoskeleton

A wearable lower-limb rehabilitation exoskeleton functions to fulfill the recovery process of limb functionality and assist physiotherapists. This paper presents an optimized adaptive control system for a wearable lower-limb rehabilitation exoskeleton. The tuning of the controller gains is defined as an optimization problem for a closed-loop control system of the wearable lower-limb rehabilitation robot by genetic algorithm and particle swarm optimization. We presented a novel initialized model reference adaptive controller (IMRAC) for real-time joint trajectory tracking, in which controller gains are adjusted by the gradient-based method. An experimental test of a 4-degree of freedom lower-limb rehabilitation exoskeleton was carried out to observe the closed-loop performance of IMRAC for bipedal human walking. The statistical comparison between IMRAC and MRAC shows an efficient performance and robustness of our proposed method for the joint trajectory tracking of the lower-limb rehabilitation exoskeleton in real time.

A Comprehensive Examination of Vector-Controlled Induction Motor Drive Techniques

This paper introduces a comprehensive examination of vector-controlled- (VC-) based techniques intended for induction motor (IM) drives. In addition, the evaluation and critique of modern control techniques that improve the performance of IM drives are discussed by considering a systematic literature survey. Detailed research on variable-speed drive control, for instance, VC and scalar control (SCC), was conducted. The SCC-based systems’ speed and V/f control purposes are clarified in closed and open loops of IM drives. The operations, benefits, and drawbacks of the direct and indirect field-oriented control systems are illustrated. Furthermore, the direct torque control (DTC) method for IMs is reviewed. Numerous VC methods established along with microprocessor/digital control, model reference adaptive control (MRAC), sliding mode control (SMC), and intelligent control (in terms of fuzzy logic (FL) and artificial neural networks (ANNs)) are described and examined. Uncertainties in the IM parameter are a considerable problem in VC drives. Therefore, this problem is addressed, and some studies that attempted to provide solutions are listed. Magnetic saturation and core loss impact are mentioned, as they are important issues in IM drives. Toward demonstrating the strengths and limitations of various VC configurations, a few experiments were simulated via MATLAB® and Simulink® that show the influence of machine parameter variation. Efforts are made to supply powerful guidelines for practicing engineers and researchers in AC drives.

Projection operator‐based robust adaptive control of an aerial robot with a manipulator

AbstractThis paper describes a quadcopter manipulator system, an aerial robot with an extended workspace, its controller design, and experimental validation. The aerial robot is based on a quadcopter with a three degree of freedom robotic arm connected to the base of the vehicle. The work aims to create a stable airborne robot with a robotic arm that can work above and below the airframe, regardless of where the arm is attached. Integrating a robotic arm into an underactuated, unstable system like a quadcopter can enhance the vehicle's functionality while increasing instability. To execute a mission with accuracy and reliability during a real‐time task, the system must overcome the inter‐coupling effects and external disturbances. This work presents a novel design for a robust adaptive feedback linearization controller with a model reference adaptive controller and hardware implementation of the quadcopter manipulator system with plant uncertainties. The closed‐loop stability of the aerial robot and the tracking error convergence with the robust controller is analyzed using Lyapunov stability analysis. The quadcopter manipulator system is custom developed in the lab with an off‐the‐shelf quadcopter and a 3D‐printed robotic arm. The robotic system architecture is implemented using a Jetson Nano companion computer for autonomous onboard flight. Experiments were conducted on quadcopter manipulator system to evaluate the autonomous aerial robot's stability and trajectory tracking with the proposed controller.

Control of a jet engine using predictor-based adaptive strategy in different flight modes

In this article, a predictor-based model reference adaptive control method is proposed for a JetCat SPT5 turboshaft engine in full thrust, cruise, and idle modes. In the predictor-based model reference adaptive control method, in addition to the tracking error, the predictor error is also utilized in adaptive laws to achieve the desired control objectives, such as improving the tracking performance and control signals, and reducing the tracking error. The proposed method is implemented on a turboshaft engine system with actual dynamic values. First, three properly separated equilibrium points are selected on the nominal plant equilibrium manifold to linearize the plant model at the equilibrium points. Then, the predictor-based model reference adaptive control controller is designed and investigated for the three equilibrium points generating three operating modes of the engine, that is, full thrust, cruise, and idle. The stability of the control system is proved by the Lyapunov method. To evaluate the efficiency of the state predictor method in the simulation scenarios, the proposed method is compared with the classic model reference adaptive control method. The simulation results illustrate the superiority of the predictor-based model reference method due to the reduction of unwanted fluctuations in tracking performance, faster convergence of the tracking error to zero, and smoother control signals for all the equilibrium points.

Performance comparison of discrete-time robust adaptive controllers for grid-tied power converters under unbalanced grids

Renewable energy generation systems largely use grid-tied power converters with LCL filters. A well-known issue in this kind of system is the resonance of the LCL filter, whose frequency is straightforwardly affected by grid condition. Variations on grid impedance tends to make the controller performance poor and until turn the closed-loop unstable. Therefore, some controllers are designed for specific operation ranges to ensure performance and global stability. However, a deep study of Model Reference Adaptive-based Controllers for this kind of issue was not conducted yet. The aim of this paper is to fit this research gap. Thus, this paper presents a performance comparison of feasible discrete-time robust adaptive controllers, based on reduced order reference model, for grid-tied power converters with LCL filter under unbalanced grid conditions, providing an assessment of the benefits and drawbacks of each considered control strategy. The evaluated control laws are: Robust Model Reference Adaptive Controller, Robust Model Reference-based Adaptive Super-Twisting Sliding Mode Controller, Robust Adaptive One Sample Ahead Preview Controller, and Robust Adaptive Proportional-Integral Controller. Through performance comparison, it can be defined what controller is more adequate in face of grid condition, taking into consideration grid-injected currents quality and design complexity of controller. Experimental results of grid-injected current control of a grid-tied 5.8 kW Voltage-Source Inverter with LCL filter considering relevant grid voltage unbalance are presented to corroborate the controllers’ performance and discuss their reference tracking response, robustness to the parametric variation, exogenous disturbance rejection, parameters adaptability, and global stability. In evaluated scenarios, Robust Adaptive Proportional-Integral controller presented slower regulation dynamics between compared adaptive structures, but reduced tracking error, as well as lower total harmonics distortion, while Robust Model Reference-based Adaptive Super-Twisting Sliding Mode Controller has the smaller overshoot in the initial transient regime, as well as the shorter transient regime.

Multi‐input enhanced model reference adaptive control strategies and their application to space robotic manipulators

AbstractThe Enhanced Model Reference Adaptive Control (EMRAC) algorithm, augmenting the MRAC strategy with adaptive integral and adaptive switching control actions, is an effective solution to impose reference dynamics to plants affected by parameter uncertainties, unmodeled dynamics and disturbances. However, the design of the EMRAC solutions has so far been limited to single‐input systems. To cover the gap, this paper presents two extensions of EMRAC to multi‐input systems. The adaptive mechanism of both solutions includes the ‐modification strategy to assure the boundedness of the adaptive gains also in presence of persistent disturbances. The closed‐loop system is analytically studied, and conditions for the asymptotic convergence of the tracking error are presented. Furthermore, when the plant is subjected to unmatched disturbances, the ultimate boundedness of the closed‐loop dynamics, which are made discontinuous by the adaptive switching control actions, is systematically proven by using Lyapunov theory for Filippov systems. The problem of trajectory tracking for space robotic arms in presence of unknown and noncooperative targets is used to test the effectiveness of the novel multi‐input EMRAC algorithms for taming uncertain systems. Four EMRAC solutions are designed for this engineering application, and tested within a high fidelity simulation framework based on the Robot Operating System. Finally, the tracking performance of the EMRAC implementations is quantitatively evaluated via a set of key performance indicators in the joint space and operational space, and compared with that of four benchmarking controllers.