Model Reference Adaptive Control Research Articles

Improving MRAC Transient Response Using Generalized Dynamic Inversion and Nullspace Parametrization

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

DESIGN OF A TWO-LINK ROBOT MANIPULATOR ADAPTIVE CONTROL SYSTEM BY THE GAIN SCHEDULING METHOD

The application areas of robot manipulators are increasingly growing year by year. Rehabilitation exoskeletons and upper limb prostheses are special cases of robot manipulators. Such systems, being nonlinear, multidimensional, and dynamic systems, have parametric uncertainties. All this becomes a challenge in the design process of the control system. One of the advanced technologies in this field is adaptive control, which is an important and interesting area of scientific and industrial research. The nonlinear control system of the two-linked robot manipulator was considered in the work. In this case, classical control methods become inapplicable and there is a need to develop and apply other control methods. Using the gain scheduling method, a group of PID controllers are designed to ensure the stability of the system at various operating points. The non-linearity of the system is compensated by the gain scheduling method and the only problem that remained is the uncertainties of the system depending on the weight of the operator. A model reference adaptive controller (MRAC) was designed and augmented with the gain scheduling method for eliminating uncertainties. Simulation is done in the MATLAB Simulink environment. A comparative performance has been quantified with different adaptation gains for the upper limb prosthesis system. The obtained results show the viability of the proposed control method.

Joint Level Adaptive Control for Mobile Robots in ROS 2

This paper proposes an adaptive controller for mobile robots using the navigation framework of the Robot Operating System. The navigation framework implements a kinematic controller which considers kinematic constraints such as robot nonholonomic constraints and obstacles and outputs linear and angular velocity commands to be implemented by the joint level controller. Usually the joint level relies on velocity servos, which neglect the dynamics of the robot or PID controllers, which are difficult to tune, due to the nonlinear model and unknown parameters of the model. The proposed model reference adaptive controller accepts velocities references, as required by the navigation framework and outputs the wheel torques to impose the commanded velocities. A formal proof of stability including the robot dynamics, controller and adaptation law is provided. Although it is not proved, as it depends on persistence of excitation on the reference signal, experimental results shows that the estimated parameters converge to values close to the actual ones.

Adaptive control of DC motor without identification of parameters

Parameter identification is a major problem in industrial environments where it might be difficult or even impossible in some situations. Moreover, non-measurable and unknown variations of system parameters can affect the performance of conventional proportional-integral (PI) controllers. The concept of developing a controller that does not depend on the system parameters seems very interesting. Therefore, this paper deals with the experimental implementation of model reference adaptive control of a DC motor without identifying parameters. Adaptive control is considered an online solution to control a system without knowing system parameters since it can be adjusted automatically to maintain favorable tracking performance. The simulation and experimental results are presented to demonstrate the effectiveness of the proposed control method.

Spatial High Precision Scanning Control Method Based on Feedforward Closed-Loop Model Reference Adaptive

The time-modulated Fourier transform spectrometer realizes spectrum detection by scanning the optical path of the corner mirror. During the scanning process, the servo system is required to have high-precision and low-speed characteristics. Aiming at the fluctuation of scanning speed caused by spatial micro-vibration during scanning, a closed-loop model reference adaptive control algorithm based on feedforward is studied. The permanent magnet synchronous linear motor is used to drive the angle mirror to move back and forth along the guide rail to achieve large optical path and high-precision scanning with the maximum optical path difference of ± 34cm, the speed stability ≥ 99%.

Control technology and development status of flywheel energy storage system

Flywheel energy storage technology has attracted more and more attention in the energy storage industry due to its high energy density, fast charge and discharge speed, long service life, clean and pollution-free characteristics. It is wwidely used in uninterruptible power system, grid frequency modulation, energy recovery and reuse and other fields. With the development of flywheel rotor materials, motors, bearings and control technology, flywheel energy storage technology has been greatly developed. Introducing the basic structure of the flywheel energy storage system in the above three applications. Typical charge-discharge control strategies are given for the three sensor-less algorithms of model reference adaptive control, sliding mode observer andextended Kalman filter, which are suitable for flywheel energy storage devices.

Lyapunov-Bregman functions for speed-gradient adaptive control of nonlinear time-varying systems

The paper is devoted to application of Bregman divergence to nonlinear adaptive control. Using Bregman divergence instead of quadratic function leads to a new class of Lyapunov functions in adaptive control that can be called Lyapunov-Bregman functions. An extended control algorithms design scheme is proposed and the properties of the designed control systems in presence of disturbances and time-varying parameters are studied. A special case of model reference adaptive control for nonlinear time-varying systems is studied based on the speed-gradient methodology.

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.

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

Model reference adaptive control for aero-engine based on system equilibrium manifold expansion model

The role of control system for aero-engine is to provide sufficient thrust and maintain physical and operational limits. Model reference adaptive control (MRAC) technique has been paid more attention for advantages in eliminating the effects of uncertainties arising from nonlinearity or component degradation and satisfying both safety and performance requirements in the entire flight envelope. However, due to the accuracy of aero-engine models, the implementation of the MRAC algorithm is challenging. In this paper, an MRAC architecture based on equilibrium manifold expansion (EME) model for an aero-engine is presented. An EME model is taken as the reference model in this architecture, which has high accuracy in the aero-engine modelling and contains both dynamic and steady-state characteristics of aero-engine. By using Lyapunov stability method, an MRAC based on EME model is designed and used to control the aero-engine obtaining desired performance. In the designed controller, uncertainty arising from component degradation of the aero-engine is estimated and the values of the uncertainty are replaced by the estimator. In order to indicate the control performance of the method provided in this article, the proposed MRAC architecture has been tested on a hardware-in-loop simulation platform, the simulation results have shown that compared the control performance with other traditional controllers, the aero-engine could quickly provide the desired control requirements without steady-state errors under the control of designed MRAC.

Model Reference Adaptive Control and Fuzzy Neural Network Synchronous Motion Compensator for Gantry Robots

A model reference adaptive control and fuzzy neural network (FNN) synchronous motion compensator for a gantry robot is presented in this paper. This paper proposes the development and application of gantry robots with MRAC and FNN online compensators. First, we propose a model reference adaptive controller (MRAC) under the cascade control method to make the reference model close to the real model and reduce tracking errors for the single axis. Then, a fuzzy neural network compensator for the gantry robot is proposed to compensate for the synchronous errors between the dual servo motors to improve precise movement. In addition, an online parameter training method is proposed to adjust the parameters of the FNN. Finally, the experimental results show that the proposed method improves the synchronous errors of the gantry robot and demonstrates the methodology in this paper. This study also successfully integrates the hardware and successfully verifies the proposed methods.

Issues of Designing a Model Adaptive Controller Without a State Observer

It can be challenging to develop a controller using conventional techniques for a plant with a linear or nonlinear dynamical system or model uncertainty. Model adaptive control is a new alternative to classical control techniques and a simple way to update controller parameters. Because model reference adaptive control is unable to anticipate the state in real time if the state observer is not designed with, we will reviewsome of the most major disadvantages of the most commonly used design techniques without state observer in this work.

Corrigendum to “Multivariable Model Reference Adaptive Control of an Industrial Power Boiler Using Recurrent RBFN”

Corrigendum to “Multivariable Model Reference Adaptive Control of an Industrial Power Boiler Using Recurrent RBFN”

AnesthesiaGUIDE: a MATLAB tool to control the anesthesia

The goals of this paper are: (a) to investigate adaptive and fractional-order adaptive control algorithms for an automatic anesthesia process, using a closed-loop system, and (b) to develop an easy-to-use tool for MATLAB/Simulink to facilitate simulations for users with less knowledge about anesthesia and adaptive control. A model reference adaptive control structure was chosen for the entire system. First of all, to control the patient’s state during the surgery process, the patient mathematical model is useful, or even required for simulation studies. The pharmacokinetic/pharmacodynamics (PK/PD) model was determined using MATLAB’s SimBiology tool, starting from a previously available block diagram, and validated through simulation. Then, to achieve the desired control performances, two controllers are designed: a PI adaptive controller and a PIλ (PI-fractional) adaptive controller, using the MIT algorithm. The time response during anesthetic drug infusion for each patient can be plotted with the AnesthesiaGUIDE tool, which is also designed in MATLAB/Simulink. The tool was tested on data from 12 patients, subjected to general anesthesia, with successful results. Through this tool, the article provides a good opportunity for any user to experience with adaptive control for the anesthesia process.

Lyapunov stability analysis of discrete-time robust adaptive super-twisting sliding mode controller

This paper presents a discrete-time Robust Adaptive Super-Twisting Sliding Mode controller and its stability analysis by means of Lyapunov stability theory. The control law is composed by two different control structures: a Robust Model Reference Adaptive Controller (RMRAC) and an Adaptive Super-Twisting Sliding Mode (STSM) Controller. The main advantage of the control structure is its robust performance and high adaptability in face of unmodelled dynamics, presenting a fast reference tracking without chattering, since it uses a second-order Sliding Mode action into adaptive control structure. The stability and robustness analysis of the control structure are presented in discrete time, considering the overall plant, that is, in presence of matched and unmatched dynamics. In addition, the control structure performance is corroborated with simulation results, considering a non-minimum phase unstable plant. Moreover, the presented results are compared with an adaptive first-order SM control-based RMRAC, which can be clearly seen that proposed controller has better performance, mainly by means of chattering mitigation.

A Novel Adaptive Steering Torque Control Approach for Human–Machine Cooperation Autonomous Vehicles

Human–machine cooperation (HMC) driving can realize the sharing of vehicle control rights between drivers and autonomous driving systems. However, the system disturbances and parameter uncertainties introduced by HMC driving are also inevitable. To solve the above problems, this study proposes an HMC steering torque control method based on the model reference adaptive control (MRAC). First, to describe the driver’s operating behavior and the transient characteristics of the steering system and the vehicle, an HMC vehicle-road model with steering torque input is established. Then, the exponential decay function is taken as a reference model to increase the decay rate of road lateral deviation. Besides, for the problem of model parameter uncertainties, the parameter adaptive laws are designed in the control process, and the robust term is considered into the parameter adaption law to improve the robustness of the closed-loop system. Robust stability under bounded disturbances is guaranteed by boundary estimation of unknown disturbances between driver input and steering load. Finally, simulations and experiments are carried out to verify the proposed control strategy. The results demonstrate that the proposed control strategy can converge rapidly under the disturbances and parameter uncertainties introduced by HMC.

A Unified Framework for Adaptive Leaderless Consensus of Uncertain Multiagent Systems Under Directed Graphs

Due to the nonsymmetric property of the associated Laplacian matrix and the existence of uncertainties in the agent dynamics, the leaderless consensus problem of uncertain multiagent systems under general directed graphs is challenging. Motivated by the classical model reference adaptive control, in this article, we propose a simple yet efficient scheme, called the model reference adaptive consensus, by arranging each agent a reference output to track, where the output is generated by a linear reference model with the relative state measurements as input. We consider two typical agent dynamics, namely, the general linear dynamics with matching uncertainties and the second-order dynamics with extended matching uncertainties. Different linear reference models are designed for the above uncertain agent dynamics. For the first one, dynamical consensus is achieved under a fixed directed graph containing a directed spanning tree. For the latter one, position consensus is achieved under switching uniformly jointly connected graphs. In the proposed algorithms, only the relative states are interacted among the agents and there is no requirement on the communication of virtual signals.

An improved 2-level MPPT scheme for photovoltaic systems using a novel high-frequency learning based adjustable gain-MRAC controller

Under rapidly changing environmental conditions, the model reference adaptive control (MRAC) based MPPT schemes need high adaptation gain to achieve fast convergence and guaranteed transient performance. The high adaptation gain causes high-frequency oscillations in the control signals resulting in numerical instability and inefficient operation. This paper proposes a novel high-frequency learning-based adjustable gain MRAC (HFLAG-MRAC) for a 2-level MPPT control architecture in photovoltaic (PV) systems to ensure maximum power delivery to the load under rapidly changing environmental conditions. In the proposed 2-level MPPT control architecture, the first level is the conventional ripple correlation control (RCC) that yields a steady-state ripple-free optimum duty cycle. The duty cycle obtained from the first level serves as an input to the proposed HFLAG-MRAC in the second level. In the proposed adaptive law, the adaptation gain varies as a function of the high-frequency ripple content of the tracking error. These high-frequency contents are the difference between the tracking error and its low-pass filtered version representing the fluctuations in output due to rapid changes in the environmental conditions. Thus, adjusting the adaptation gain by high-frequency content of the tracking error ensures fast convergence, guaranteed transient performance, and overall system stability without needing high adaptation gain. The adaptive law of the proposed HFLAG-MRAC is derived using the Lyapunov theory. Simulation studies, experimental analysis, and performance comparison with recent similar work validate the effectiveness of the proposed work.

Mechatronics design of self-adaptive under-actuated climbing robot for pole climbing and ground moving

AbstractClimbing robots have broad application prospects in aerospace equipment inspection, forest farm monitoring, and pipeline maintenance. Different types of climbing robots in existing research have different advantages. However, the self-adaptability and stability have not been achieved at the same time. In order to realize the self-adaptability of holding and climbing stability, this work proposes a new type of climbing robot under the premise of minimizing the driving source. The robot realizes stable multifinger holding and wheeled movement through two motors. At the same time, the robot has two different working modes, namely pole climbing and ground crawling. The holding adaptability and climbing stability are realized by underactuated holding mechanism and model reference adaptive controller (MRAC). On the basis of model design and parameter analysis, a prototype of the climbing robot is built. Experiments prove that the proposed climbing robot has the ability to stably climb poles of different shapes. The holding and climbing stability, self-adaptability, and climbing and crawling speed of the proposed climbing robot are verified by experiments.

Maximum power point tracking using adjustable gain based model reference adaptive control

Maximum power point tracking using adjustable gain based model reference adaptive control