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.

Robust Fixed Point Transformations in the Model Reference Adaptive Control of a Three DoF Aeroelastic Wing

2013 ◽  
Vol 300-301 ◽  
pp. 1505-1512 ◽  
Author(s):  
József Kázmér Tar ◽  
Imre J. Rudas ◽  
János F. Bitó ◽  
Krisztián Kósi
Keyword(s):  
Fixed Point ◽  
Reference Model ◽  
Tracking Error ◽  
External Control ◽  
Underactuated System ◽  
Control Parameters ◽  
Model Reference ◽  
Adaptive Controllers ◽  
Point Transformations ◽  
Model Reference Adaptive

The Model Reference Adaptive Controllers (MRAC) of dynamic systems have the purpose of simulating the dynamics of a reference system for an external control loop while guaranteeing precise tracking of a prescribed nominal trajectory. Such controllers traditionally are designed by the use of some Lyapunov function that can guarantee global and sometimes asymptotic stability but pays only little attention to the primary design intent, has a great number of arbitrary control parameters, and also is a complicated technique. The Robust Fixed Point Transformations (RFPT) were recently introduced as substitutes of Lyapunov’s technique in the design of adaptive controllers including MRACs, too. Though this technique guarantees only stability (neither global nor asymptotic), it works with a very limited number of control parameters, directly concentrates on the details of tracking error relaxation, and it is very easily can be designed. In the present paper this novel technique is applied for the MRAC control of a 3 Degrees-of-Freedom (DoF) aeroelastic wing model that is an underactuated system the model-based control of which attracted much attention in the past decades. To exemplify the efficiency of the method via simulations it is applied for PI and PID-type prescribed error relaxation for a reference model the parameters of which considerably differ from that of the actual system.

Adapting Dynamic Braking of AC Motors to Varying Wheel/Rail Adhesion Condition

2013 ◽  
Author(s):  
Husain Ahmad ◽  
Mehdi Ahmadian
Keyword(s):  
Reference Model ◽  
Excitation Frequency ◽  
Adaptive Controller ◽  
Rolling Stock ◽  
Accurate Estimation ◽  
Model Reference ◽  
Braking Distance ◽  
Estimation And Control ◽  
Dynamic Braking ◽  
Model Reference Adaptive

Model reference adaptive control (MRAC) is developed to control the electrical excitation frequency of AC traction motors under various wheel/rail adhesion conditions during dynamic braking. More accurate estimation and control of train braking distance can allow more efficient braking of rolling stock, as well as spacing trains closer together for Positive Train Control (PTC). In order to minimize the braking distance of a train, dynamic braking forces need to be maximized for varying wheel/rail adhesion. The wheel/rail adhesion coefficient plays an important role in safe train braking. Excessively large dynamic braking can cause wheel lockup that can damage the wheels and rail, or may lead to large coupler forces, possibly causing derailment or broken components. In this study, a multibody formulation of a locomotive and three railcars is used to develop a model reference adaptive controller for adjusting the voltage excitation frequency of an AC motor such that the maximum dynamic braking is achieved, without locking up the wheels. A relationship between creep forces, creepages, and motor braking torque is established. This relationship is used to control the motor excitation frequency in order to closely follow the reference model that aims at achieving maximum allowable adhesion during dynamic braking. The results indicate that MRAC significantly improves braking distance while maintaining better wheel/rail adhesion and coupler dynamics during dynamic braking.

The Adaptive Ship Motion Control Based on Fuzzy Neural Network

2011 ◽  
Vol 135-136 ◽  
pp. 989-994 ◽  
Author(s):  
Guan Shan Hu ◽  
Hai Rong Xiao
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Motion Control ◽  
Reference Model ◽  
Tracking System ◽  
Good Control ◽  
Ship Motion ◽  
Network Systems ◽  
Model Reference ◽  
Model Reference Adaptive

Given the uncertainty of parameters and the random nature of disturbance, a ship motion, is a complicated control problem. This paper has researched adaptive neural network systems and its application to ship’s motion control. In paper, Ship’s mathematical model is researched. Aimed at ship mathematical motion model, the model reference adaptive auto pilot is first designed based on the analysis of the model reference adaptive control theory. We used fuzzy logic and neural networks to design the feedback controller, used multilayer perceptron neural network to design the reference model and the ship course identification model network. Based on the fuzzy control and neural network, an intelligent adaptive control algorithm was presented in the paper. In consideration of the forces and moments from the environmental disturbance, such as winds, waves, currents, etc., Simulation experiments are carried out by using Matlab’s Simulink toolbox. The simulating result indicates the designed adaptive controller can get a good control performance for ship course tracking system.

The Model Reference Adaptive Control of the DC Electric Drive System

2014 ◽  
Vol 875-877 ◽  
pp. 2030-2035 ◽  
Author(s):  
Marian Gaiceanu ◽  
Cristian Eni ◽  
Mihaita Coman ◽  
Romeo Paduraru
Keyword(s):  
Adaptive Control ◽  
Reference Model ◽  
Sliding Mode ◽  
Tracking Error ◽  
Model Reference Adaptive Control ◽  
Drive System ◽  
Mode Switching ◽  
Sigmoid Function ◽  
Model Reference ◽  
Model Reference Adaptive

Due to the parametric and structural uncertainty of the DC drive system, an adaptive control method is necessary. Therefore, an original model reference adaptive control (MRAC) for DC drives is proposed in this paper. MRAC ensures on-line adjustment of the control parameters with DC machine parameter variation. The proposed adaptive control structure provides regulating advantages: asymptotic cancellation of the tracking error, fast and smooth evolution towards the origin of the phase plan due to a sliding mode switching k-sigmoid function. The reference model can be a real strictly positive function (the tracking error is also the identification error) as its order is relatively higher than one degree. For this reason, the synthesis of the adaptive control will use a different type of error called augmented or enhanced error. The DC machine with separate excitation is fed at a constant flux. This adaptive control law assures robustness to external perturbations and to unmodelled dynamics.

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.

Vehicle Active Suspension Control: Using AaVariable Structure Model Reference Adaptive Controller

2005 ◽  
Author(s):  
Ardeshir Karami Mohammadi
Keyword(s):  
Reference Model ◽  
Variable Structure ◽  
Active Suspension ◽  
Adaptive Controller ◽  
Structure Model ◽  
Model Reference ◽  
Quarter Car Model ◽  
Working Space ◽  
Fixed Structure ◽  
Model Reference Adaptive

A Variable Structure Model Reference Adaptive Controller (VS-MRAC) is proposed for Active control of vehicle suspension. One DOF quarter car model has been considered. The reference model is a one DOF vibrating system with skyhook damper. The structure of the switching functions is designed based on global exponential stability requirements, and shows perfect model following at a finite time. Sliding surfaces are independent of system parameters and therefore VS-MRAC is insensitive to system parameter variations. Simulation is presented for some road irregularities, and compares to active suspension with fixed structure MRAC, and passive suspension. VS-MRAC shows the best performance on ride and working space

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