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 Robust Model Reference Adaptive Control for Tail-Sitter VTOL Aircraft

Actuators ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 162
Author(s):  
Ahmed R. Ajel ◽  
Amjad J. Humaidi ◽  
Ibraheem Kasim Ibraheem ◽  
Ahmad Taher Azar
Keyword(s):  
Adaptive Control ◽  
Stability Analysis ◽  
Dead Zone ◽  
Tracking Error ◽  
Model Reference Adaptive Control ◽  
Roll Motion ◽  
Wind Disturbance ◽  
Model Reference ◽  
Adaptive Laws ◽  
Model Reference Adaptive

This study presents a control design of roll motion for a vertical take-off and landing unmanned air vehicle (VTOL-UAV) design based on the Model Reference Adaptive Control (MRAC) scheme in the hovering flight phase. The adaptive laws are developed for the UAV system under nonparametric uncertainty (gust and wind disturbance). Lyapunov-based stability analysis of the adaptive controlled UAV system under roll motion has been conducted and the adaptive laws have been accordingly developed. The Uniform Ultimate Boundness (UUB) of tracking error has been proven and the stability analysis showed that the incorporation of dead-zone modification in adaptive laws could guarantee the uniform boundness of all signals. The computer simulation has been conducted based on a proposed controller for tracking control of the roll motion. The results show that the drift, which appears in estimated gain behaviors due to the application of gust and wind disturbance, could be stopped by introducing dead-zone modification in adaptive laws, which leads to better robustness characteristics of the adaptive controller.

Switched Fractional Order Model Reference Adaptive Control for an Automatic Voltage Regulator

2021 ◽  
Author(s):  
Norelys Aguila-Camacho ◽  
Jorge E. García-Bustos ◽  
Eduardo I. Castillo-López
Keyword(s):  
Fractional Order ◽  
Adaptive Controller ◽  
System Response ◽  
Voltage Regulator ◽  
Order Model ◽  
Model Reference ◽  
Fractional Order Model ◽  
Automatic Voltage Regulator ◽  
Adaptive Laws ◽  
Model Reference Adaptive

Abstract This paper presents the design and implementation of a Switched Fractional Order Model Reference Adaptive Controller (SFOMRAC) for an Automatic Voltage Regulator (AVR). The fractional orders, adaptive gains and switching times of the controller adaptive laws are tuned offline, using Particle Swarm Optimization (PSO). The functional to be optimized contains not only parameters of the AVR response but also the control energy. The obtained controllers are compared to non switched Integer Order Model Reference Adaptive Controller (IOMRAC) and non switched Fractional Order Model Reference Adaptive Controller (FOMRAC) proposed previously for this process, showing that the SFOMRAC can improve both, the system response and the control energy used.

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.

The development of a model reference adaptive controller to control the knee joint of paraplegics

1991 ◽  
Vol 36 (6) ◽  
pp. 683-691 ◽  
Author(s):  
M.S. Hatwell ◽  
B.J. Oderkerk ◽  
C.A. Sacher ◽  
G.F. Inbar
Keyword(s):  
Knee Joint ◽  
Adaptive Controller ◽  
Model Reference ◽  
Model Reference Adaptive

Evaluation of P-Q Controllers for an Injection Moulding Machine

2000 ◽  
Author(s):  
Paul K. Guerrier ◽  
Kevin A. Edge
Keyword(s):  
Injection Moulding ◽  
Adaptive Controller ◽  
Pi Controller ◽  
Hardware In The Loop ◽  
Model Reference ◽  
Industry Standard ◽  
Moulding Machine ◽  
Single Controller ◽  
Pressure Controller ◽  
Model Reference Adaptive

Abstract There are a number of problems surrounding traditional velocity and pressure controllers used on injection moulding machines. Injection moulding machines are also very expensive and full scale testing is often not appropriate at the beginning of new controller evaluation. This paper presents results for a half scale ‘hardware-in-the-loop’ load emulation of the filling and packing phases of injection moulding, suitable for controller evaluation. The problems linked to the current industry standard velocity and pressure controller are discussed along with alternative strategies. Schemes including single controller fuzzy logic and neural network solutions are discussed and ruled out in favour of ones containing separate velocity and pressure controllers. Results for a model reference adaptive pressure controller are presented and compared with those obtained using a closed loop PI controller experimentally and in simulation. Experimentally the model reference adaptive controller outperforms the PI controller but does suffer from gain drift.

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