Modified Model Reference Adaptive Control Research Articles

Application of modified Model Reference Adaptive Controller and Observer (MRACO) for speed control of an unmanned underwater vehicle

In this paper, a modified Model Reference Adaptive Controller and Observer (MRACO), including an integral term to handle external disturbances, is applied to control, independently, the surge and sway speeds of an unmanned underwater vehicle (UUV). The suitability of the method is shown in simulation and in an experiment on a real system where a use-case from aquaculture robotics is demonstrated. The results show that the modified MRACO method is capable of controlling the surge and sway speeds accurately despite being derived on a simplified model of the UUV system used.

Modified model reference adaptive observer‐based control without assumptions on uncertain parameter bounds

AbstractThis paper proposes a Modified model reference adaptive controller and observer (MMRACO) for unknown linear time‐invariant (LTI) systems. The recent literature, and in particular the so‐called MRACO approach, has demonstrated how some deficiencies in standard model reference adaptive control can be overcome by using a combination of modified reference models and observers, but this is done at the expense of knowing bounds on certain unknown parameters. Here, a novel adaptive observer/controller combination is presented which removes the need to know the above bounds but preserves the other desirable features of the MRACO approach. A Lyapunov analysis of the closed‐loop system ensures asymptotic convergence of both tracking and observer errors, with uniform boundedness of parameter estimation errors. A key advantage of the scheme is that the design involves only solutions of two Lyapunov equations (or linear matrix inequalities) and does not require the selection of a “high gain” parameter. The effectiveness of the approach is illustrated in some numerical examples.

Hybrid two‐stage adaptive maximum power point tracking for stand‐alone, grid integration, and partial shaded PV system

SummaryIn a PV array, the power‐voltage characteristic (P‐V) and output power are completely dependent on atmospheric conditions. Consequently, the P‐V curve's maximum power point (MPP) likewise changes significantly. This abrupt oscillation in the MPP may be caused by the PV panel being uniformly or partially shaded by trees, buildings, raindrops, and clouds. However, in both circumstances, the maximum power point tracking (MPPT) turns into a highly nonlinear issue with a time‐bounded solution. The unpredictable weather circumstances change P‐V characteristics after every short time interval. Therefore, this research presents a hybrid two‐stage adaptive MPPT to overcome the environmental uncertainties. The first stage is MPPT control block for determining reference voltage for each MPP and the second stage is modified model reference adaptive controller (MMRAC) block fine‐tunes converter duty cycle to maintain MPP of PV panel. The probabilistic evaluation is executed through four distinct levels of uncertainty, including stand‐alone, partially‐shaded, grid integration, and OPAL‐RT to examine the system robustness in diverse scenario. The possibility of the tracking failure caused by dynamic irradiance change is also verified. Moreover, efficiency, tracking ability, power loss, ripple, and error rate are compared with the state‐of‐the‐art strategies, that is, ANFIS, INC, P&O, and VSPO. The satisfactory performances of the new strategy under simultaneously varying irradiance, temperature, and load; partially shaded and grid integration shows the supremacy.

Transient Improvement of Model-Reference Adaptive Control and Parameter Convergence Rate Analysis

The parameter convergence analysis of a modified model-reference adaptive control (M-MRAC) algorithm is presented. The plants considered are continuous with relative degree one. It is verified that the M-MRAC scheme approaches the MRAC scheme when a control design gain increases. Moreover, before this gain reaches high values, the M-MRAC improves the update parameters’ convergence. This analysis is performed by noting a clear connection between the M-MRAC and the conventional MRAC by adding a control output injection term. The methodology developed allows for estimating the parameter convergence rate, and simulation results illustrate the improvement in the transient behavior and the parameter convergence attained with the analyzed adaptive schemes.

Temperature Control Design with Differential Evolution Based Improved Adaptive-Fuzzy-PID Techniques

This paper presents the design and performance analysis of Differential Evolution (DE) algorithm based Proportional-Integral-Derivative (PID) controller for temperature control of Continuous Stirred Tank Reactor (CSTR) plant in chemical industries. The proposed work deals about the design of Differential Evolution (DE) algorithm in order to improve the performance of CSTR. In this, the process is controlled by controlling the temperature of the liquid through manipulation of the coolant flow rate with the help of modified Model Reference Adaptive Controller (MRAC). The transient response of temperature process is improved by using PID Controller, Differential Evolution Algorithm based PID and fuzzy based DE controller. Finally, the temperature response is compared with experimental results of CSTR.

A Modified Model Reference Adaptive Control for High-Performance Pantograph Robot Mechanism

Pantograph Robot Mechanism is considered a type of parallel manipulator which has been developed largely for industrial applications that need high accuracy and speed. Whereas, it needs a high-performance controller to track preselected trajectory planning. It is also able to carry higher weights than the open-chain mechanism with suitable accuracy and stability; this is because it consists of four active links and one passive link, instead of two links as in the open chain. This study presents a mathematical model for a closed chain pantograph mechanism, where the boundary conditions are taken into account. A complete MATLAB Simulink has been developed to simulate the dynamics of the pantograph robot mechanism. To validate the proposed mathematical model of the pantograph, the corresponding Simscape model had been developed. Also, three different tracking controllers were designed. The first control is the PID controller which had optimized by Flower Pollination (FP) optimization. The second control is an enhanced Nonlinear PID (NLPID) controller where its parameters were obtained by Flower Pollination (FP) optimization based on the effective objective function. The third control is the model reference adaptive control. A comparative study between the control techniques was accomplished. A rectangular trajectory was selected to be a position reference of the end effector of the pantograph robot. This task was done using the proposed controllers to investigate the performance. The results show that the model reference adaptive control has a better performance compared to the NLPID and PID controllers. The end effector has a less rise time and settling time with high accuracy in the case of the model reference adaptive control.

A Modified Model Reference Adaptive Controller (M-MRAC) Using an Updated MIT-Rule for the Altitude of a UAV

Unmanned Aerial Vehicles (UAVs) are playing an increasingly important role in a wide variety of areas and the range of applications increases daily, which can also be seen in the research of the topic. At the University of Wuerzburg drones are to be used in a project, where the aim is to catch possibly dangerous UAVs in mid air using a net, carried by two drones. This very special scenario poses new problems to the control of the drones, so that traditionally used Proportional-Integral-Differential (PID) controllers are no longer sufficient. Therefore a model-based adaption mechanism was chosen to be used to control the altitude of the drones. Though adaption based controllers have been used in the field of drone research before, the existing algorithms had to be modified to work with the special conditions of the altitude control of UAVs. The design and implementation of the modified Model Reference Adaptive Controllers (MRACs) with an updated Massachusetts Institute of Technology (MIT)-rule will be presented in this work. The behavior of the drones with and without the adaption as well as the changes to the original MRAC are then compared in simulation as well as on a real system and show very promising results in further improving the stability of the altitude control of the drones.

CONTROLLING THE MEAN ARTERIAL PRESSURE BY MODIFIED MODEL REFERENCE ADAPTIVE CONTROLLER BASED ON TWO OPTIMIZATION ALGORITHMS

This paper Presents Modified Model Reference Adaptive Controller (MRAC) to regulate the hight blood pressure. It is based on slate model that represent the mathematical equation that clarifies relationship between blood pressure and vasoactive drug injection. In this work Squirrel Search Algorithm (SSA) and Grey Wolf Optimizer (GWO)algorithms are considered to optimize the controller parameters. the results showed that the suggested controller has good performance and stabilize the mean arterial pressure with small settling time (below than 400s) and small overshoot (below than 1 mmHg) with low amount of error.

Robust adaptive control for unmatched systems with guaranteed parameter estimation convergence

SummaryThis paper provides a modified model reference adaptive control (MRAC) scheme to achieve better transient control performance for systems with unknown unmatched dynamics, where an adaptive law with guaranteed convergence is introduced. We first revisit the standard MRAC system and analyze the tracking error bound by using L2‐norm and Cauchy‐Schwartz inequality. Based on this analysis, we suggest a feasible way to compensate the undesired transient dynamics induced by the gradient descent–based adaptive laws subject to sluggish convergence or even parameter drift. Then, a modified adaptive law with an alternative leakage term containing the parameter estimation error is developed. With this adaptive law, the convergence of both the estimation error and tracking error can be proved simultaneously. This enhanced convergence property can contribute to deriving smoother control signal and improved control response. Moreover, this paper provides a simple and numerically feasible approach to online verify the well‐known persistent excitation condition by testing the positive definiteness of an introduced auxiliary matrix. Comparative simulations based on a benchmark 3‐DOF helicopter model are given to validate the effectiveness of the proposed MRAC approach and show the improved performance over several other MRAC schemes.

An unconventional adaptive flutter suppression actuation system: From modeling to experimentation

This article contributes to the definition of an unconventional actuation system coupled with an adaptive control algorithm, it is intended specifically for slender/highly flexible wings flutter suppression. The design and validation process of the novel actuation architecture is presented together with the performance analysis of the post-flutter dynamics control. Robustness of the overall control architecture is verified with respect to the uncertainties deriving from the unpredictable degradation of the structural properties. The proposed actuation system is based on a row of multiple mini-spoilers, located in proximity of the leading edge and coordinated by a modified model reference adaptive control algorithm. The spoiler configuration is optimized by computational fluid dynamics numerical simulation, whereas the aerodynamic database is derived by wind tunnel tests on the prototype by means of a six-axes force balance. The resulting aeroelastic mathematical model is then used to implement and validate the adaptive control algorithm for a wide range of conditions, from on-design flutter speed and nominal structural stiffness to post-flutter speed and reduced structural stiffness. The two degree of freedom aeroelastic model is successfully controlled in all conditions. This article aims at defining a robust procedure for aeroelastic phenomena control system design, which employs a synergy of modeling, simulation, and experimental approaches. Pertinent conclusions are discussed in the final section of the article.

Tracking with asymptotic sliding mode and adaptive input delay effect compensation of nonlinearly perturbed delayed systems applied to traffic feedback control

ABSTRACTAsymptotical sliding mode-model reference adaptive control design for a class of systems with parametric uncertainty, unknown nonlinear perturbation and external disturbance, and with known input and state delays is proposed. To overcome the difficulty to directly predict the plant state under uncertainties, a control design is based on a developed decomposition procedure, where a ‘generalised error’ in conjunction with auxiliary linear dynamic blocks with adjustable gains is introduced and the sliding variable is formed on the basis of this error. The effect of such a decomposition is to pull the input delay out of first step of the design procedure. As a result, similarly to the classical Smith predictor, the adaptive control architecture based only on the lumped-delays, i.e. without conventional in such cases difficult-implemented distributed-delay blocks. Two new adaptive control schemes are proposed. A linearisation-based control design is constructed for feedback control of an urban traffic region model with uncertain dynamics. Simulation results demonstrate the effectiveness of the developed adaptive control method.

A hierarchical integrated chassis control strategy based on wheel force feedback

The motivation to investigate a novel integrated chassis control (ICC) strategy emerges from recent advances in on-board force transducers. This study proposes a novel hierarchical framework adopting feedback control on tyre force level, and an on-target high level control algorithm including Force Reference Generator and allocation strategy to supervise front steering and braking subsystems. In the proposed method, force reference is generated regarding linear vehicle behaviour and guaranteed to be achievable, bounded by tyre grip limit. Allocation strategy tracks force reference and penalises undesired vehicle body motion, capturing significantly faster dynamics. In order to address the non-linearities and uncertainties of the vehicle model, a modified model reference adaptive control (MRAC) scheme is used, which both stabilises the lateral dynamics and maintains maximum lateral performance. The proposed method is validated and evaluated on a multi-body vehicle model simulation.

Improved Transient Performance of a Fuzzy Modified Model Reference Adaptive Controller for an Interacting Coupled Tank System Using Real-Coded Genetic Algorithm

The main objective of the paper is to design a model reference adaptive controller (MRAC) with improved transient performance. A modification to the standard direct MRAC called fuzzy modified MRAC (FMRAC) is used in the paper. The FMRAC uses a proportional control based Mamdani-type fuzzy logic controller (MFLC) to improve the transient performance of a direct MRAC. The paper proposes the application of real-coded genetic algorithm (RGA) to tune the membership function parameters of the proposed FMRAC offline so that the transient performance of the FMRAC is improved further. In this study, a GA based modified MRAC (GAMMRAC), an FMRAC, and a GA based FMRAC (GAFMRAC) are designed for a coupled tank setup in a hybrid tank process and their transient performances are compared. The results show that the proposed GAFMRAC gives a better transient performance than the GAMMRAC or the FMRAC. It is concluded that the proposed controller can be used to obtain very good transient performance for the control of nonlinear processes.

Real-coded Genetic Algorithm for system identification and tuning of a modified Model Reference Adaptive Controller for a hybrid tank system

Modeling and controlling of level process is one of the most common problems in the process industry. As the level process is nonlinear, Model Reference Adaptive Control (MRAC) strategy is employed in this paper. To design an MRAC with equally good transient and steady state performance is a challenging task. The main objective of this paper is to design an MRAC with very good steady-state and transient performance for a nonlinear process such as the hybrid tank process. A modification to the MRAC scheme is proposed in this study. Real-coded Genetic Algorithm (RGA) is used to tune off-line the controller parameters. Three different versions of MRAC and also a Proportional Integral Derivative (PID) controller are employed, and their performances are compared by using MATLAB. Input–output data of a coupled tank setup of the hybrid tank process are obtained by using Lab VIEW and a system identification procedure is carried out. The accuracy of the resultant model is further improved by parameter tuning using RGA. The simulation results shows that the proposed controller gives better transient performance than the well-designed PID controller or the MRAC does; while giving equally good steady-state performance. It is concluded that the proposed controllers can be used to achieve very good transient and steady state performance during the control of any nonlinear process.

A modified model reference adaptive control with application to MEMS gyroscope

Micro electro-mechanical systems (MEMS) are increasingly being used in measurement and control problems due to their small size, low cost, and low power consumption. The vibrating gyroscope is a MEMS device that will have a significant impact on stability control systems in the transportation industry. This paper investigates the application of a modified model reference adaptive control for MEMS gyroscope. Using this adaptive control algorithm, an estimation of the angular velocity and the damping and stiffness coefficients in real time is easily computable. Changing the conventional model reference input makes it feasible to utilize a low pass filter to remove un- wanted oscillations caused by high adaptation gain. This new adaptive control technique enables quick compensation for large changes in the system dynamics, providing consistent estimation of gyroscope parameters including angular velocity and large robustness to pa- rameter variations and external disturbances. The asymptotic stability of the mentioned adaptive controller is guaranteed using the Lyapunov direct method. Numerical simulation is presented to verify the effectiveness of the proposed control scheme.

Robust adaptive‐sliding mode tracking of state delayed nonlinear plants with actuator failures

AbstractIn this paper, we develop two sliding mode—model reference adaptive control (MRAC) schemes for a class of delayed nonlinear dynamic systems under actuator failure that are robust with respect to actuator failures, to an unknown plant delay, to a nonlinear perturbation, and to an external disturbance with unknown bounds. Appropriate Lyapunov–Krasovskii‐type functionals with ‘virtual’ adaptation gains are introduced to design the adaptation algorithms, and to prove stability. Two different controllers are designed: one with discontinuous and another with continuous control actions, respectively. Copyright © 2011 John Wiley & Sons, Ltd.

Design and Test of a Novel Closed-Loop System That Exploits the Nociceptive Withdrawal Reflex for Swing-Phase Support of the Hemiparetic Gait

A novel closed-loop system for improving gait in hemiparetic patients by supporting the production of the swing phase using electrical stimulations evoking the nociceptive withdrawal reflex was designed. The system exploits the modular organization of the nociceptive withdrawal reflex and its stimulation site- and gait-phase modulation in order to evoke movements of the hip, knee, and ankle joints during the swing phase. A modified model-reference adaptive controller (MRAC) was designed to select the best stimulation parameters from a set of 12 combinations of four electrode locations on the sole of the foot and three different stimulation onset times between heel-off and toe-off. It was hypothesized that the MRAC system would result in a better walking pattern compared with an open-loop preprogrammed fixed pattern of stimulation (FPS) controller. Thirteen chronic or subacute hemiparetic subjects participated in a study to compare the performance of the two control schemes. Both control schemes resulted in a more functional gait compared to no stimulation (P < 0.05) with a weighted joint angle peak change of 4.0 ± 1.6 (mean ± Standard deviation) degrees and 3.1 ± 1.4 degrees for the MRAC and FPS schemes, respectively. This indicates that the MRAC scheme performed better than the FPS scheme (P < 0.001) in terms of reaching the control target.

Tracking for nonlinear plants with multiple unknown time‐varying state delays using sliding mode with adaptation

AbstractIn this paper, we develop a sliding mode model reference adaptive control (MRAC) scheme for a class of nonlinear dynamic systems with multiple time‐varying state delays, which is robust with respect to unknown plant delays, to a nonlinear perturbation, and to an external disturbance with unknown bounds. An appropriate Lyapunov–Krasovskii‐type functional is introduced to design the adaptation algorithms, and to prove stability. Copyright © 2009 John Wiley &amp; Sons, Ltd.

A modified model reference adaptive control approach for systems with noise or unmodelled dynamics

In this paper, a modified model reference adaptive control (MRAC) strategy is developed for use on plants with noise or unmodelled high-frequency dynamics. MRAC consists of two parts, an adaptive control part and a fixed gain control part. The adaptive algorithm uses a combination of low- and high-pass filters such that the frequency range for the adaptive part of the strategy is limited. The mechanism for noise-induced gain wind-up is demonstrated analytically, and it is shown how MRAC can be modified to eliminate this wind-up. Further to this, an additional filter is proposed to improve MRAC robustness to high-frequency unmodelled dynamics. Two test plants, one with added noise and the other with unmodelled high-frequency dynamics, are considered. Both plants exhibit unstable behaviour when controlled using standard MRAC, but, with the modified strategy, robustness is significantly improved.

Model reference adaptive control of a nonsmooth dynamical system

In this paper a modified model reference adaptive control (MRAC) technique is presented which can be used to control systems with nonsmooth characteristics. Using unmodified MRAC on (noisy) nonsmooth systems leads to destabilization of the controller. A localized analysis is presented which shows that the mechanism behind this behavior is the presence of a time invariant zero eigenvalue in the system. The modified algorithm is designed to eliminate this zero eigenvalue, making all the system eigenvalues stable. Both the modified and unmodified strategies are applied to an experimental system with a nonsmooth deadzone characteristic. As expected the unmodified algorithm cannot control the system, whereas the modified algorithm gives stable robust control, which has significantly improved performance over linear fixed gain control.