Research on hydraulic motor control system based on fuzzy neural network combing sliding mode control and time delay estimation
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The precise motion control of a hydraulic motor system has some problems due to uncertain disturbance, complex nonlinear dynamics. Traditional methods are difficult to obtain the desired control performance. In this paper, a new fuzzy neural network (FNN) combined with terminal sling mode control (TSMC) and time delay estimation (TDE) is proposed. FNN is used to adjust the parameter of TSMC to reduce the time for the system to reach the equilibrium point and chatting. To increase the accuracy of the system, TDE is used to compensate the error caused by uncertain disturbance. This controller was simulated in Amesim and Simulink, and the results showed that the control scheme proposed in this paper has the smallest angular displacement error, angular velocity error and variance than other control schemes, such as PID and sliding mode control (SMC). Furthermore, the designed controller was implemented on a drill pipe automatic handling manipulator, and its control performance was verified.The PID controller is widely used in two-axis photoelectric platform velocity loop, however the control precision and the stability of controller are contradictory, especially when friction torque is added. In order to solve this problem, the characteristics of PID controller were analyzed detail in this paper, then a sliding mode controller was designed based on the approach law. First, the model of two-axis photoelectric platform velocity loop was established, then the friction model was linearized according to the interval analysis theory, after that the sliding mode function and the controller was designed, next the simulation model was build, finally, the performance of the velocity loop was compared between sliding mode controller and PID controller, the results showed the sliding mode controller has higher control precision and the stability.
Open-loop controller
Photoelectric effect
Mode (computer interface)
Line-of-sight
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Lyapunov stability
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Mode (computer interface)
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Mode (computer interface)
Operating point
Dual mode
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The application of a sliding mode controller and an optimised proportional–integral–derivative (PID) controller to a nonlinear two-mass system is presented. Both of the proposed controller structures are strengthened with a grey estimator. A complete state-space mathematical model for a nonlinear two-mass system is first developed and numerically simulated. Then, an optimised PID controller and a sliding-mode controller are both designed to regulate the speed of the system. Finally, corresponding results are compared. The grey estimators of the torsional torque and the load machine speed are tested with open-loop and closed-loop control structure. The simulation results are confirmed by the experimental results.
Open-loop controller
Mode (computer interface)
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The author describes the implementation of advanced nonlinear control systems by combining recent advances in nonlinear control system synthesis with a rule-based system approach to real-time control. The basic idea is that a nonlinear plant to be controlled could be quite sensitive to both operating point and input amplitude, so that the best control system performance is obtained with a nonlinear controller that is returned or resynthesized whenever the operating point changes significantly in the sense that the nonlinear control system input/output behavior changes substantially in an undesirable way. The control system thus obtained is hierarchically organized, with a standard reprogrammable controller under the direction of a rule-based system.< >
Operating point
Automatic control
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Position (finance)
Synchronous motor
Mode (computer interface)
Smoothing
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This article described the principles of control system of 6-DOF motive platform,According to the characteristics of the control system a neural network controller was proposed for improving the control performance of the traditional PID controller.The configuration of the control system was based on RBF and BP neural networks.A new algorithm was put forward,in which parameters of the PID control was optimized online by BP network based on RBF network identifying the Jacobian matrix of the controlled plant.At last,the programming steps under Matlab platform were also mentioned.
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In order to compare the control performance differences between the traditional PID control and sliding mode control( SMC) in the non-linear changes variable load,a control test platform which includes PID / SMC controller model,permanent magnet synchronous motor( PMSM) motor control model,and a movement mechanism and load models were built. In this platform,through the software co-simulation of MATLAB and ADAMS,a detailed analysis on the performance of both was done which was based on the control performance of the two control algorithms. By comparison,the sliding mode control can be found better than traditional PID control in the control precision,adjustable time,robustness and energy control.
Robustness
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Based on nonlinear decentralized theory,a nonlinear decentralized controller was designed for the pose stabilization of X-Cell50 helicopter in vertical flight.This simple-structured and easy-to-realized controller does not require the precise math model,however,the integral action included in it compensates unknown factors of plant.Simulations compared with sliding mode control,show that the proposed control system overcomes the disadvantage of sliding mode control just as chattering phenomenon and steady-state error.It is important that the excellent robust performance withstanding the outside disturbances and dynamical uncertainties is obtained by using the nonlinear decentralized controller.
Integral sliding mode
Decentralised system
Mode (computer interface)
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