Model Reference Adaptive Control Scheme Research Articles

Maximum Power Point Tracking Improvement Using Model Reference Adaptive Control Scheme

This paper presents maximum power point tracking improvement using model reference adaptive control (MRAC) scheme. Solar photovoltaic (PV) energy system remains one of the renewable energy applications which operate by tracking energy from the sun and converting it into useful electrical energy. However, harnessing the generated energy has been the major concern of the engineers hence the adoption of various control strategies for efficient control of the generated power. The idea is to transfer the optimal generated power to the output considering the fact that PV energy produced may have been affected by irradiation and temperature conditions. To avoid the unstable output power, there is need to deploy an efficient control scheme. Most of the conventional control techniques are not optimal to resolve power inaccuracies in the system and they have shortcomings. This work is therefore aimed at analyzing solar photovoltaic energy system using MRAC technique to investigate the control performance. The results obtained by simulation on MATLAB/Simulink software, show an average tracking efficiency of 73.41%, the rise time of 0.7093s and a duty cycle of 0.7. Moreover, MRAC operates at small gain values and the range at which MRAC can operate was found to be 0.1 < 𝛤 < 2.

A new robust nonlinear control strategy for UIPC in isolated hybrid microgrids

This paper focuses on the control of unified interphase power controller (UIPC) in isolated hybrid microgrids (HmGs), where the UIPC is used as the main component for interconnecting of AC and DC sub-grids. The investigated HmG encompasses two AC sub-grids and one purely storage DC sub-grid (PSDC) and multiple adjustable loads. A robust nonlinear model reference adaptive control (NMRC) strategy is offered for controlling the UIPC, keeping an acceptable two-way power exchange control with uncomplicated topology. Further, a novel configuration for the DC link power converter is suggested using the dual-active-bridge topology. Also, a harmonic-based modeling method is used to model the UIPC's power converter. The proposed configuration provides bidirectional power flow, increased power density, straightforward adaptation of zero-voltage switching, and direct access to cascading and parallelism to increase the ratings and reliability of UIPC. The simulation results illustrate the feasibility of the suggested topology for isolated HmGs.

Event-triggered robust model reference adaptive control for drag-free satellite

Aiming at the requirement of bounded disturbance suppression for low-frequency space gravitational wave detection satellite, a robust model reference adaptive drag-free control scheme is proposed in this paper via linear matrix inequalities (LMIs) approach. The multivariable model reference adaptive control (MRAC) scheme is applied to the drag-free control system with parameter uncertainties, which realizes the adaptive tracking to the reference state. The LMI system is established as an adaptive compensation term, which provides robustness against nonlinear disturbances. To reduce the communication burden of the actuation information and further save the total energies, an event-triggered mechanism (ETM) is introduced, with both the actuation inputs and the adaptive laws of the feedback gains updated only at the triggering time instants. The ultimately uniformly boundedness of the closed-loop signals is proved by Lyapunov analysis, with each system state convergence. From the feasibility analysis, Zeno behavior can be further proved to be strictly excluded. The numerical simulation verifies that the proposed scheme has efficient robustness to nonlinear disturbances with low energy cost, and achieves good effect in response to the requirements of space gravitational wave detection mission.

Model reference adaptive disturbance rejection control using partial-state feedback

In this paper, we propose a partial-state feedback model reference adaptive control strategy for uncertain linear time-invariant systems with unmatched disturbances. The new control design is based on parametrization and observer technique. The control structure and output matching of nominal partial-state feedback disturbance rejection are presented. The adaptive partial-state feedback disturbance rejection scheme guarantees closed-loop system stability and asymptotic output tracking. A simulation example indicates the effectiveness of the presented adaptive partial-state feedback control design.

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.

Efficient Uncertainty Mitigation in Automated Vehicles: Combining MPC with Model Reference Adaptive Control

Automated vehicles must be equipped with the ability to plan and execute trajectories in the presence of uncertainties for safe and effective navigation in both on-and of-road environments. Model predictive control (MPC) is a powerful and popular optimal control solution for both planning and control tasks, but even robust MPC solutions still suffer from decreased performance and the curse of dimensionality in the optimization problem when subject to model errors. We propose an adaptive control scheme that combines a fast, nominal MPC with our previously developed Model Reference Adaptive Control strategy in a cascaded architecture. The effectiveness of the resulting framework is demonstrated via robust and computationally efficient trajectory tracking under severe uncertainties. This study includes detailed tuning considerations and numerical evaluation with different fidelity vehicle models.

Partial State Feedback MRAC Based Reconfigurable Fault-Tolerant Control of Drag-Free Satellite With Bounded Estimation Error

Aiming at the ultra-precision and ultra-stable requirements of drag-free control in space detection missions, a multivariable model reference adaptive control (MRAC) scheme is proposed in this paper based on partial observation state, to provide adaptive suppression of uncertain disturbances and improve detection accuracy. The MRAC scheme utilizes model output matching with partial state containing uniform and bounded observation error, and estimates the unknown state parameters through the adaptive law and high-frequency gain decomposition. In response to the actuator bias fault of the drag-free satellite, a set of state error iterative convergence sequence in the actuator loop is established based on the sequence Lyapunov analysis, which is further introduced into the reconfiguration control input to achieve the fault-tolerant target without changing the nominal controller. Through the Lyapunov stability analysis, the convergence of the closed-loop system states in the presence of actuator faults and observation errors is obtained. Numerical simulation results for a 6-DOF drag-free control system demonstrate the effectiveness of the proposed MRAC-based reconfigurable fault-tolerant control scheme.

Fractional order adaptive MRAC controller design for high-accuracy position control of an industrial robot arm

Most of conventional feedback controllers become inefficient in hard industrial environments like in steel industry, because of uncertainties in the plant model, or process dynamics variation due to nonlinear actuators, and changes in the character of the disturbances. This paper proposes an adaptive control design based on fractional order model reference adaptive control (FOMRAC) strategy in order to deal with an uncertain horizontal positioning control of an unloading machine in a rotary hearth furnace for hot rolling operation. The proposed FOMRAC scheme uses the MIT rule as an adaptive mechanism with two main modifications comparatively to the conventional MRAC: the reference model is an adequate fractional order system and the parameter adjustment rule contains a fractional order integrator. Stability analysis of the proposed control scheme is performed using the Lyapunov stability theorem. Numerical simulations are presented to show the effectiveness of the proposed fractional adaptive schemes applied to an industrial robot arm loading round steel blocks from inside a rotary hearth furnace. After comparison with the conventional MRAC, it is shown that the performances of FOMRAC are superior to classical control schemes.

High-Tracking-Precision Sensorless Control of PMSM System Based on Fractional Order Model Reference Adaptation

In order to solve parameter fluctuations and disturbances, a novel fractional order model reference adaptive speed observer that estimates the rotor position and the angular velocity from the stator currents is proposed for sensorless control of permanent-magnet synchronous motors (PMSM). Firstly, a novel fractional order model reference adaptive controller (FOMRAC) for adaptive identification is designed to achieve fast response and high precise identification of load torque in the full speed range when the motor is running at variable-speed or variable-load. Additionally, an appropriate adjustable matrix P is chosen to make the convergence of the adaptive law meet the requirements. Next, an improved model reference adaptive observer (MRAO) is proposed to suppress the serious chattering and compensate rotor position error, which can stabilize the system. The validity of the proposed fractional order model reference adaptive sensorless control strategy for PMSM is demonstrated with simulations.

Decentralized Model-Reference Adaptive Control Based Algorithm for Power Systems Inter-Area Oscillation Damping

Being the primary cause of inter-area oscillations and due to the fact that they limit the generation’s output, Low-Frequency Electromechanical Oscillations (LFEOs) represent a real threat to power system networks. Mitigating their effects is therefore crucial as it may lead to system collapse if not properly damped. As rotor angle instability is the primary cause of LFEOs, this paper presents a novel Model-Reference Adaptive Control (MRAC) scheme that enhances its stability. The proposed scheme is tested using the Single-Machine Infinite Bus (SMIB) network. The results obtained validate the proposed decentralized control architecture. The robustness of this oscillation damping controller is verified through simulations in MATLAB/SIMULINK. With Gaussian noise added to the structure of the generator to emulate small load variations responsible for the rotor angle instability, the results of the simulations show that the rotor angle remains stable. Furthermore, when subjected to faults, the recovery time is less than 500 ms.

Experimental Validation of Adaptive Augmented LQI Control for a 2 DoF Helicopter

This paper presents a novel adaptive augmented linear quadratic integral (LQI) controller for a 2 degrees of freedom (DoF) helicopter to improve the closed-loop performance of the baseline controller even when the system contains unmodeled dynamics and exogenous disturbances. Although the LQI controller is widely used in aircraft control applications for its excellent reference-tracking capability and inherent robustness, the performance of the baseline LQI controller degrades when the aircraft model has unmodeled dynamics and external disturbances, which are inevitable in aircraft applications. Hence, to deal with the parameter uncertainty and unmeasured disturbances, we present an adaptive control framework using a model reference adaptive control (MRAC) scheme combined with the baseline LQI controller. The key aspect of the proposed control scheme is that the robustness of the closed-loop control system to deal with the matched uncertainty is enhanced by tracking error augmentation with the control law. Moreover, this work proves the asymptotic stability of the closed-loop system using the inverse Lyapunov function and Barbalat’s lemma. The efficacy of the adaptive augmented LQI is experimentally validated through hardware in loop (HIL) implementation on a laboratory scale 2 DoF helicopter workstation for several real-time test cases. Experimental results accentuate that the adaptive augmented control scheme can significantly improve not only the reference-tracking capability but also the robustness of the closed-loop system.

A Stretching Force Control-Based Cyclic Loading Method for the Evaluation of Mechanical Properties of Gelation Methacrylate (GelMA) Microfibers.

Microfluidic spun gelation mechacrylate (GelMA) microfiber has been widely utilized as a promising bioink for 3D bioprinting. However, its weak and easily tuned mechanical properties are still difficult to precisely evaluate, due to the lack of an effective stretching method. In this paper, we propose a force-control-based cyclic loading method for rapidly evaluating the elastic modulus: the E of the microfibers with different GelMA concentrations. A two-tube manipulation system is used to stretch microfiber with a non-destructive process. Based on the model reference adaptive control strategy, the stress response can be fitted into a sinusoidal wave when a small sinusoidal strain is automatically applied onto the microfiber. Afterwards, the maximum tensile stress and tensile stain is obtained to determine the E. Moreover, different stress amplitudes and frequencies are applied to form different stress-strain loops with almost same E. Compared with a frequently-used constant force loading method, the proposed method shows an obvious advantage in measurement accuracy, especially for low-concentration GelMA microfiber. Furthermore, the reasonableness of the measured E for different GelMA concentrations is confirmed by 3D cell culture experiments, and the results show the proposed method has great application potential to investigate the interaction between cell and fibrous bioink substrate.

A quantized output feedback MRAC scheme for discrete-time linear systems

This paper presents a quantized output feedback model reference adaptive control (MRAC) scheme for a class of single-input and single-output discrete-time linear time-invariant systems with unknown parameters. Our method, firstly, integrates the well-known MRAC and quantized control techniques to construct a quantized output feedback adaptive control law with parameter update laws. Then, some vital technical lemmas are developed, fundamentally applicable to finite and infinite quantized output feedback MRAC. Moreover, we prove that in the case of infinite quantization, appropriately choosing the output quantizer’s sensitivity affords the proposed adaptive control law to ensure closed-loop stability and achieve bounded or asymptotic output tracking. The significant advantage of the developed adaptive control scheme is combining the benefits of the classic MRAC and quantized control. Compared with current adaptive tracking control schemes, the developed scheme not only reduces the feedback information requirement, but also has full capability to achieve closed-loop stability and output tracking. The effectiveness of the proposed MRAC scheme is verified through several simulations.

A model reference adaptive control scheme of a high-order nonlinear helicopter subject to input and state constraints

A model reference adaptive control (MRAC) scheme is proposed for helicopter in this article. Based on the concept of μ-modification method and gradient functions, the proposed MRAC scheme is able to handle control problems of helicopter subject to constraints of inputs and states. Performance of transient response of closed-loop helicopter is improved by conducting the command governor. Stability of the closed-loop helicopter system is guaranteed based on Lyapunov stability theory. The effectiveness of the proposed control scheme is tested through numerical simulations of a high-order nonlinear mathematical model of a helicopter in two flight missions.

A Novel MRAC Scheme for Output Tracking

This paper puts forward a novel output feedback model reference adaptive control (MRAC) scheme for solving an adaptive output tracking problem. The proposed control scheme only needs a scalar function to be updated online, which decreases the system adaptation complexity, compared to the existing MRAC schemes. Furthermore, the closed-loop signal boundedness and asymptotic output tracking are guaranteed with the proposed MRAC scheme. A simulation study is carried out to verify the effectiveness of the established approach.

Improved fractional augmented control strategies for continuously stirred tank reactors

AbstractControlling concentration and temperature of continuously stirred tank reactor (CSTR) is an extremely challenging task in chemical process industries. This is because conventional unity feedback schemes do not guarantee stable operating conditions for the CSTR process. Therefore, fractional calculus is augmented with multi‐loop control to achieve enhanced stability and closed‐loop performance than unity feedback structure in this work. Accordingly, three different fractional‐order‐based novel multi‐loop control structures are proposed based on time domain analysis. In two of the proposed strategies, fractional‐order PID controller (FOPID) and internal model control (IMC)‐based FOPID controller with fractional filter are used in the inner‐loop. Moreover, fractional‐order‐based Lyapunov stability rule of model reference adaptive control (MRAC) is used in outer‐loops for both of the above mentioned methods. Finally, an advanced multi‐loop predictor with FOPD controller in the inner‐loop and FOIMC controller in the outer‐loop is also proposed. In this work, fractional‐order, filter parameters, and FOPID settings are obtained by minimizing multi‐objective functions using modified particle swarm optimization (PSO) algorithm. Closed‐loop responses and control efforts of the proposed control strategies are compared with that of FO‐Lyapunov‐based MRAC scheme. Quantitative performance analysis is also carried out on all proposed methods based on error metrics and total variation of the control signal.

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.

Robust Adaptive Control of Knee Exoskeleton-Assistant System Based on Nonlinear Disturbance Observer

This study presents a control design of an angular position for the exoskeleton knee assistance system based on a model reference adaptive control (MRAC) strategy. Three schemes of the MRAC design have been proposed: the classical MRAC, MRAC with an adaptive disturbance observer, and MRAC with a nonlinear observer. The stability analysis for each scheme has been conducted and developed based on the Lyapunov theorem to prove the uniform ultimate bound of tracking and estimation errors. In addition, the adaptive laws have been developed for the proposed schemes according to the stability analysis. The effectiveness of the proposed state and output feedback controllers has been verified via computer simulation. The results based on numerical simulation have shown that the MRAC with a nonlinear observer could give better robustness characteristics and better performance in terms of tracking and estimation errors as compared to the other controllers.

Design of Optimal Fractional Order Lyapunov Based Model Reference Adaptive Control Scheme for CSTR

When a first-order irreversible exothermic reaction takes place in a continuously stirred tank reactor (CSTR), the plant model relating the reactor and jacket temperatures along with a measurement delay is of second-order unstable type. Such plants are very challenging to control. Hence, this article focuses on developing an optimal fractional order Lyapunov (FOL) based model reference adaptive control (MRAC) scheme. The optimal values of adaptive gain, as well as extra degree of freedom, are obtained using a modified particle swarm optimization (PSO) algorithm. The closed-loop responses and control efforts are compared with that of FOL based MRAC scheme by using an artificial bee colony (ABC) algorithm to optimize adaptive gain and extra degree of freedom. Moreover, the aforementioned optimization methods are also employed to develop respective optimal fractional order Massachusetts institute of technology (FOMIT) based MRAC schemes for comparison purposes. In both these algorithms, a multi-objective function involving minimization of rise-time, settling-time, integrated absolute error, integrated square error, integrated time-weighted absolute error is used. Simulation studies validate the effectiveness of the proposed particle swarm optimized FOL-based MRAC scheme.

Research on real-time control strategy of multi-power flow of dual-mode power-split hybrid electric vehicle

With the continuous development of hybrid vehicle control technology, great progress has been made in the research of multi-power flow collaborative control. Due to the internal delay link of each power component, the role of energy storage element, and the limitation of electric power in the whole system, the inevitable delay characteristic of state transfer is caused. Therefore, the speed of multi-power flow control torque coordinated response of hybrid vehicles needs to be improved. The dual-mode power-split hybrid electric vehicle (DMPS-HEV) overall structure and working modes are analyzed, by adopting the combination of theory and experiment method. In order to solve the problem that the power components of dual-mode power-split hybrid electric vehicle cannot follow the optimal control command of the upper energy management strategy quickly due to the engine response delay, thus affecting the control effect of the upper energy management strategy. The research on torque coordination control strategy is carried out, the reference model of electromechanical composite drive is established, and the model reference adaptive coordination control strategy based on Lyapunov stability theory is proposed. The results show that the proposed model reference adaptive torque coordinated control strategy significantly improves the effect of engine response delay on the optimization effect of energy management strategy, and can achieve the control effect of the optimal control strategy of 93.58%. The test platform of the dual-mode power-split hybrid electric vehicle was built. The control system was built based on the rapid control prototype, and the data acquisition system was built based on the NI data acquisition module. The coordinated control strategy of the dual-mode power-split hybrid electric vehicle power system proposed in this paper was verified through the bench test to significantly improve the vehicle fuel economy and the real-time performance of the control strategy, which has a good practical value