Symmetrical valve controlled asymmetrical cylinder based on wavelet neural network

2017 ◽  
Vol 34 (7) ◽  
pp. 2154-2167 ◽  
Author(s):  
Haitao Qi ◽  
Zilong Liu ◽  
Yan Lang
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Dynamic Response ◽  
Position Control ◽  
Reference Model ◽  
Wavelet Neural Network ◽  
Content Type ◽  
Hydraulic Servo ◽  
Model Reference Adaptive

Purpose The symmetrical valve is usually used in the hydraulic servo control system to control the asymmetrical cylinder, but this system’s structure involves asymmetry, and so its dynamic characteristics are asymmetrical, which causes issues in the control system of symmetric response. The purpose of this paper is to achieve the aim of symmetric control. Design/methodology/approach In this paper, the authors proposed a method that combined wavelet neural network (WNN) and model reference adaptive control. The reference model determined the dynamic response that the system was expected to achieve, and the WNN adaptive control made the system follow the reference model to achieve the purpose of symmetric control. Findings The experimental results show that the method can achieve a more accurate symmetric control and position control compared with the solutions via the classical PID control. Originality/value The proposed combination of the WNN and the reference model can effectively compensate for the asymmetry of dynamic response of the asymmetric cylinder in forward and return directions, which can be extended to deal with other classes of applications.

Multimode Adaptive Control for Tank Gun Control System Based on Tracking Differentiator

2011 ◽  
Vol 383-390 ◽  
pp. 79-85
Author(s):  
Dong Yuan ◽  
Xiao Jun Ma ◽  
Wei Wei
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
High Speed ◽  
Control Method ◽  
Reference Model ◽  
Gun Control ◽  
Math Model ◽  
Tracking Differentiator ◽  
Switch Control ◽  
Model Reference Adaptive

Aiming at the problems such as switch impulsion, insurmountability for influence caused by nonlinearity in one tank gun control system which adopts double PID controller to realize the multimode switch control between high speed and low speed movement, the system math model is built up; And then, Model Reference Adaptive Control (MRAC) method based on nonroutine reference model is brought in and the adaptive gun controller is designed. Consequently, the compensation of nonlinearity and multimode control are implemented. Furthermore, the Tracking Differentiator (TD) is affiliated to the front of controller in order to restrain the impulsion caused by mode switch. Finally, the validity of control method in this paper is verified by simulation.

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.

Hybrid intelligent adaptive controller for tiltrotor UAV

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jafar Tavoosi
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Hybrid Control ◽  
Adaptive Controller ◽  
Content Type ◽  
Flight Mode ◽  
Horizontal Flight ◽  
Aerial Vehicle ◽  
Model Reference Adaptive

PurposeIn this paper, an innovative hybrid intelligent position control method for vertical take-off and landing (VTOL) tiltrotor unmanned aerial vehicle (UAV) is proposed. So the more accurate the reference position signals tracking, the proposed control system will be better.Design/methodology/approachIn the proposed method, for the vertical flight mode, first the model reference adaptive controller (MRAC) operates and for the horizontal flight, the model predictive control (MPC) will operate. Since the linear model is used for both of these controllers and naturally has an error compared to the real nonlinear model, a neural network is used to compensate for them. So the main novelties of this paper are a new hybrid control design (MRAC & MPC) and a neural network-based compensator for tiltrotor UAV.FindingsThe proper performance of the proposed control method in the simulation results is clear. Also the results showed that the role of compensator is very important and necessary, especially in extreme speed wind conditions and uncertain parameters.Originality/valueNovel hybrid control method. 10;-New method to use neural network as compensator in an UAV.

Adaptive control of missile attitude based on BP–ADRC

2020 ◽  
Vol 92 (10) ◽  
pp. 1475-1481
Author(s):  
Haiyan Qiao ◽  
Hao Meng ◽  
Wei Ke ◽  
Quanxi Gao ◽  
Shaobo Wang
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Control Method ◽  
Extended State ◽  
Dynamics Model ◽  
Closed Loop System ◽  
Content Type

Purpose To improve the robustness of missile control system and reduce the error, a missile attitude adaptive control method based on active disturbance rejection control technology (ADRC) and BP neural network is innovatively proposed. Design/methodology/approach ADRC improves the performance of the missile control system by estimating and eliminating the total disturbance of the system. BP neural network adjusts the parameters of ADRC controller according to the state of the system to realize adaptive control. Based on the control system and missile dynamics model, the convergence analysis of the extended state observer and the stability analysis of the closed-loop system after embedding BP neural network are given. Findings The simulation results show that the adaptive control method can adjust the coefficient of error feedback rate according to the system input, output and error change rate, which accelerates the response speed of missile attitude angle and reduces the attitude angle error. Practical implications BP–ADRC further improves the robustness and environmental adaptability of the missile control system. The BP–ADRC control method proposed in this paper is proved feasible. Originality/value Different from the traditional ADRC, the BP–ADRC feedback signal proposed in this paper uses the output signal and its rate of the closed-loop system instead of the system state quantity estimated by extended state observer (ESO). This innovative method combined with BP neural network can make the system output meet the requirements when ESO has errors in the estimation of missile dynamics model.

scholarly journals Model Reference Adaptive Control based on a Self-Recurrent Wavelet Neural Network Utilizing Micro Artificial Immune Systems

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Omar Farouq Lutfy ◽  
Maryam Hassan Dawood
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Control ◽  
Artificial Immune System ◽  
Model Reference Adaptive Control ◽  
Wavelet Neural Network ◽  
Control Performance ◽  
Artificial Immune ◽  
Model Reference ◽  
Model Reference Adaptive

Abstract  This paper presents an intelligent model reference adaptive control (MRAC) utilizing a self-recurrent wavelet neural network (SRWNN) to control nonlinear systems. The proposed SRWNN is an improved version of a previously reported wavelet neural network (WNN). In particular, this improvement was achieved by adopting two modifications to the original WNN structure. These modifications include, firstly, the utilization of a specific initialization phase to improve the convergence to the optimal weight values, and secondly, the inclusion of self-feedback weights to the wavelons of the wavelet layer. Furthermore, an on-line training procedure was proposed to enhance the control performance of the SRWNN-based MRAC. As the training method, the recently developed modified micro artificial immune system (MMAIS) was used to optimize the parameters of the SRWNN. The effectiveness of this control approach was demonstrated by controlling several nonlinear dynamical systems. For each of these systems, several evaluation tests were conducted, including control performance tests, robustness tests, and generalization tests. From these tests, the SRWNN-based MRAC has exhibited its effectiveness regarding accurate control, disturbance rejection, and generalization ability. In addition, a comparative study was made with other related controllers, namely the original WNN, the artificial neural network (ANN), and the modified recurrent network (MRN). The results of these comparison tests indicated the superiority of the SRWNN controller over the other related controllers. Keywords: Artificial neural network, micro artificial immune system, model reference adaptive control, self-recurrent wavelet neural network , Wavelet neural network.

Impedance Control of a Pneumatic Servo System with Adaptive Control Method

1991 ◽  
Vol 3 (6) ◽  
pp. 463-469 ◽  
Author(s):  
Toshiro Noritsugu ◽  
◽  
Tsutomu Wada ◽  
Toshiaki Asanoma ◽  
◽  
...  
Keyword(s):  
Adaptive Control ◽  
Control System ◽  
Position Control ◽  
Control Method ◽  
Reference Model ◽  
Impedance Control ◽  
Servo System ◽  
Operating Conditions ◽  
Pneumatic Cylinder ◽  
Pneumatic Servo System

One of the typical features of a pneumatic servo is a relatively high compliance due to air compressibility. This feature may be useful for constrained tasks such as deburring, polishing, and assisting humans, in which the relationship between position and force is important. If this relationship of a pneumatic servo becomes actively controllable, it can be effectively applied to these tasks. In order to control this relationship, an impedance control concept has recently been proposed. The impedance of the overall control system depends not only on the manipulator but also on the manipulated object of which the characteristics are usually unknown. Therefore, to attain the desired impedance over extensive operating conditions, an adaptive control strategy is required. This paper proposes an impedance control method of a pneumatic servo, using a position based approach, where an adaptive position control system is constructed inside the force feedback loop. The proposed method is applied to an experimental pneumatic servo system comprised of a pneumatic cylinder, electro-pneumatic proportional control valves, and a spring object. From the experiments, the following has been verified: 1) both static stiffness and dynamic impedance of the pneumatic servo system can be independently regulated by setting a desired reference model; 2) the impedance can be held constant with changes in system parameter such as object stiffness; and 3) the instability problem for the low stiffness setting can be overcome by setting high damping in the reference model. The proposed impedance control method may prove to be effective for both improving a pneumatic servo and developing its new applications.

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