Backstepping-based model reference adaptive controller for a multi-axial system in the presence of external disturbance and saturated input

2017 ◽  
Vol 40 (3) ◽  
pp. 776-784 ◽  
Author(s):  
Van Tu Duong ◽  
Huy Hung Nguyen ◽  
Jae Hoon Jeong ◽  
Hak Kyeong Kim ◽  
Sang Bong Kim
Keyword(s):  
Control Method ◽  
Dead Zone ◽  
External Disturbance ◽  
Adaptive Controller ◽  
Auxiliary System ◽  
Design Stage ◽  
Recursive Structure ◽  
Model Reference ◽  
Model Reference Adaptive ◽  
Axial System

This paper presents a backstepping-based model reference adaptive controller for a multi-axial system in the presence of external disturbance and saturated input. The proposed controller synthesizes the backstepping technique and the model reference adaptive control method to construct control inputs for recursive structure and uncertain modelling of the multi-axial system. To cope with the limit of saturated input, an auxiliary system is adopted. A dead-zone modification is introduced to avoid the drift phenomenon of adjusted adaptive parameters. The stability of the proposed controller is proven by Lyapunov’s theory while considering the effect of the auxiliary system and the dead-zone modification in the design stage. The effectiveness and performance of the proposed controller are evaluated by experiment on a transformer winding system.

scholarly journals Research on Variable Cycle Engine Control Based on Model Reference Adaptive Sliding Mode Control Method

2018 ◽  
Vol 36 (5) ◽  
pp. 824-830
Author(s):  
Hongliang Xiao ◽  
Huacong Li ◽  
Jia Li ◽  
Jiangfeng Fu ◽  
Kai Peng
Keyword(s):  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
External Disturbance ◽  
Adaptive Sliding Mode Control ◽  
Engine Control ◽  
Model Reference ◽  
Adaptive Sliding Mode ◽  
Mode Control ◽  
Model Reference Adaptive

As to solve the problem of multivariable output tracking control of variable cycle engine under system uncertainties and external disturbances, an augmented model reference adaptive sliding mode control method based on LQR method was developed. Firstly, the model is augmented and the reference state is provided to the controller by designing the reference model using the optimal LQR method. Then, based on the state tracking sliding mode control method, the adaptive law is derived based on the strict stability condition of Lyapunov function to estimate the upper bound of the system perturbation matrix and the upper bound of the external disturbances. Finally, the controller achieves the asymptotic zero tracking error of the system under the conditions of uncertainty and external disturbance. The simulation results showed that the LQR-based augmented model reference adaptive sliding mode control method can solve the problem that the traditional sliding mode control method needs to specify the reference state in advance and improve the control performance of the variable cycle engine control with system uncertainties and external disturbance. The tracking of the control command is effectively achieved and the steady-state and dynamic performance are improved. The steady-state control errors under different conditions are less than 0.1%, the system overshoot is less than 0.5%, and the adjustment time is less than 1s, which conformed to the requirements of the aero engine control system technology.

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.

scholarly journals New Adaptive Control Strategy for a Wind Turbine Permanent Magnet Synchronous Generator (PMSG)

Inventions ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 3
Author(s):  
Wenping Cao ◽  
Ning Xing ◽  
Yan Wen ◽  
Xiangping Chen ◽  
Dong Wang
Keyword(s):  
Adaptive Control ◽  
Wind Turbine ◽  
Permanent Magnet ◽  
Control Method ◽  
Synchronous Generator ◽  
Permanent Magnet Synchronous Generator ◽  
Model Reference ◽  
Integral Controller ◽  
Proportional Integral Controller ◽  
Model Reference Adaptive

Wind energy conversion systems have become a key technology to harvest wind energy worldwide. In permanent magnet synchronous generator-based wind turbine systems, the rotor position is needed for variable speed control and it uses an encoder or a speed sensor. However, these sensors lead to some obstacles, such as additional weight and cost, increased noise, complexity and reliability issues. For these reasons, the development of new sensorless control methods has become critically important for wind turbine generators. This paper aims to develop a new sensorless and adaptive control method for a surface-mounted permanent magnet synchronous generator. The proposed method includes a new model reference adaptive system, which is used to estimate the rotor position and speed as an observer. Adaptive control is implemented in the pulse-width modulated current source converter. In the conventional model reference adaptive system, the proportional-integral controller is used in the adaptation mechanism. Moreover, the proportional-integral controller is generally tuned by the trial and error method, which is tedious and inaccurate. In contrast, the proposed method is based on model predictive control which eliminates the use of speed and position sensors and also improves the performance of model reference adaptive control systems. In this paper, the proposed predictive controller is modelled in MATLAB/SIMULINK and validated experimentally on a 6-kW wind turbine generator. Test results prove the effectiveness of the control strategy in terms of energy efficiency and dynamical adaptation to the wind turbine operational conditions. The experimental results also show that the control method has good dynamic response to parameter variations and external disturbances. Therefore, the developed technique will help increase the uptake of permanent magnet synchronous generators and model predictive control methods in the wind power industry.

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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