scholarly journals A MRAC Principle for a Single-Link Electrically Driven Robot with Parameter Uncertainties

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Carlos Aguilar-Avelar ◽  
Javier Moreno-Valenzuela
Keyword(s):  
Tracking Error ◽  
Adaptive Controller ◽  
Parameter Uncertainties ◽  
Rigid Link ◽  
Control Schemes ◽  
Single Link ◽  
Electrically Driven Robot ◽  
Model Reference Adaptive ◽  
Control Methodology ◽  
Experimental Comparisons

In this paper, a model reference adaptive control (MRAC) principle for a one-degree-of-freedom rigid-link electrically driven robot is presented. The proposed control methodology addresses the problem of trajectory tracking with parameter uncertainties in the dynamic model of the system and proposes adaptation laws for the electrical and mechanical parameters. Closed-loop stability is rigorously discussed, proving that the tracking error trajectories converge to the origin exponentially. With the aim of performing experimental comparisons, two control schemes are also revisited theoretically and experimentally: one is an algorithm previously reported in the literature and the other is an adaptive controller derived under the assumption that the electrical dynamics of the actuator are negligible. All the discussed controllers have been implemented in an experimental setup consisting in a rigid-link robot actuated with brushed DC motor. The comparison indicates that better results are obtained with the new MRAC scheme.

Composite adaptive posicast controller for the wing rock phenomenon in a delta-wing aircraft

2019 ◽  
Vol 233 (15) ◽  
pp. 5579-5591
Author(s):  
Amir Yousefimanesh ◽  
Alireza Khosravi ◽  
Pouria Sarhadi
Keyword(s):  
Delta Wing ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Composite Model ◽  
Input Delay ◽  
Model Reference ◽  
Wing Rock ◽  
Adaptive Laws ◽  
Regulation And Tracking ◽  
Model Reference Adaptive

The nonlinear dynamic phenomenon like wing rock is one of the important issues in the high performance aircraft autopilot design. This phenomenon occurs in the form of constant amplitude oscillations in the roll dynamics, during the flight at high angles of attack (AOAs) and endangers carrying out the mission of an aircraft. In this paper, a composite adaptive posicast controller is designed for the wing rock phenomenon in a delta-wing aircraft with known input delay. The existence of the input delay besides the parametric uncertainties of the system dynamics adds to the complexity of the problem and can cause undesirable troubles in regulation and tracking performance or instability in the control system. Consequently, there is a need for a controller that can provide the stability and desirable regulation and tracking for the system. The proposed control method uses the system state forecasting and the composite model reference adaptive controller in an integrated control structure based on linear quadratic regulator (LQR). Combining the tracking error and the prediction error to form the adaptive laws in the composite model reference adaptive controller improves the characteristics of the system response and provides a better performance compared to the model reference adaptive controller in which the adaptive laws are formed only with the tracking error. Simulation results show the efficiency of the composite adaptive posicast controller in counteracting the system uncertainties in the presence of considerably large input delay cases.

scholarly journals Modeling and Control for an Underwater Vehicle using Dual Quaternions.

2021 ◽  
Author(s):  
Angel Zamora ◽  
Miguel Garcia ◽  
Adrian Manzanilla ◽  
Filiberto Muñoz ◽  
Sergio Salazar ◽  
...  
Keyword(s):  
Tracking Error ◽  
Computational Cost ◽  
Euler Angle ◽  
Adaptive Controller ◽  
Underwater Vehicle ◽  
System Stability ◽  
Parameter Uncertainties ◽  
Robust Controller ◽  
Dual Quaternions ◽  
Unknown Disturbances

Abstract In this work, the analysis of the dynamic general model of an unmanned underwater vehicle (UUV) based on dual quaternions is presented, then the general dynamic model is reduced to a specific vehicle of 4 DoF, this model eliminates the singularities that exist with the representation of the Euler angle and that the model is more compact than others proposed in the literature [1],[2]. To demonstrate the applicability of the model, three controller strategies are proposed for tracking a trajectory, the first controller is a PD + G, under unknown disturbances it produces a considerable tracking error, the second is an adaptive controller that estimates unknown hydrodynamic parameters, and the third is a robust controller for unknown disturbances and parameter uncertainties. The closed-loop system stability analysis for each controller is based on Lyapunov’s theory, a set of numerical simulations is performedto show the behavior of the vehicle with the proposed controllers. The efficiency of the controllers is shown in Table 2 where it is deduced that the adaptive controller has a better performance. The graphics show that the robust controller has little error tracking and the computational cost is lower.

scholarly journals Fractional order adaptive MRAC controller for an active suspension system

2020 ◽  
Vol 5 (2) ◽  
pp. 112-117
Author(s):  
SEIF EDDINE KHELAS ◽  
SAMIR LADACI ◽  
YASSINE BENSAFIA
Keyword(s):  
Adaptive Control ◽  
Fractional Order ◽  
Order Parameter ◽  
Active Suspension ◽  
Adaptive Controller ◽  
Suspension System ◽  
Conventional Model ◽  
Control Schemes ◽  
Classical Control ◽  
Model Reference Adaptive

This paper investigates the use of fractional order operators in conventional model reference adaptive control (MRAC). A fractional adaptive controller is designed based on the use of a fractional-order parameter adjustment rule. Applied in numerical simulations for an active suspension system and compared with the conventional MRAC, it is shown that the performances of FOMRAC are superior to classical control schemes.

scholarly journals Neural Network based Direct MRAC Technique for Improving Tracking Performance for Nonlinear Pendulum System

2020 ◽  
Vol 1 (2) ◽  
pp. 1-15
Author(s):  
Alemie Assefa ◽  
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Reference Model ◽  
Tracking Error ◽  
Adaptive Controller ◽  
Conventional Model ◽  
Model Reference ◽  
Pendulum System ◽  
Nonlinear Pendulum ◽  
Model Reference Adaptive

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

Synthesis Design and Analysis of a Hybrid Controller for Robotic Arms

2016 ◽  
Author(s):  
Dan Zhang ◽  
Bin Wei
Keyword(s):  
Pid Controller ◽  
Adaptive Controller ◽  
Convergence Speed ◽  
Model Reference ◽  
Robotic Arms ◽  
Hybrid Controller ◽  
Comparison Results ◽  
Convergence Performance ◽  
Model Reference Adaptive ◽  
Better Than

In this paper, a hybrid controller for robotic arms is proposed and designed by combining a proportional-integral-derivative controller (PID) and a model reference adaptive controller (MRAC) in order to further improve the accuracy and joint convergence speed performance. The convergence performance of the PID controller, the model reference adaptive controller and the PID+MRAC hybrid controller for 1-DOF and 2-DOF manipulators are compared. The comparison results show that the convergence speed and its performance for the MRAC and the PID+MRAC controllers are better than that of the PID controller, and the convergence performance for the hybrid control is better than that of the MRAC control.

A Design Method of LS-SVM Based Stable Adaptive Controller for a Class of Nonlinear Systems

2011 ◽  
Vol 48-49 ◽  
pp. 17-20
Author(s):  
Chun Li Xie ◽  
Tao Zhang ◽  
Dan Dan Zhao ◽  
Cheng Shao
Keyword(s):  
Nonlinear Systems ◽  
Design Method ◽  
Nonlinear Function ◽  
Lyapunov Stability Theory ◽  
Adaptive Controller ◽  
Control Law ◽  
Continuous Systems ◽  
Control Schemes ◽  
Closed Systems ◽  
Simulation Results

A design method of LS-SVM based stable adaptive controller is proposed for a class of nonlinear continuous systems with unknown nonlinear function in this paper. Due to the fact that the control law is derived based on the Lyapunov stability theory, the scheme can not only solve the tracking problem of this class of nonlinear systems, but also it can guarantee the asymptotic stability of the closed systems, which is superior to many LS-SVM based control schemes. The effectiveness of the proposed scheme is demonstrated by simulation results.

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