A Marching Stable Control Method for a New Vehicle-Mounted Multi-Tube Load

doi:10.12382/bgxb.2021.0782

Abstract

Abstract:

A novel vehicle multi-tube load faces internal and external disturbances, as well as instability on the move in travelling. In order to solve these problems, a stable controller with adaptive compensation of disturbance velocity is designed based on neural network. The load structure is optimized to balance the loads at both ends of the trunnion and reduce energy consumption and nonlinear interference caused by the unbalanced torque. The main control quantity is calculated by PI controller, and the velocity and acceleration of disturbance are obtained by the extended state observer and the hybrid differentiator, respectively. The single neuron controller is adopted to calculate the compensation control quantity, of which the inputs are disturbance velocity and acceleration. Gradient information is employed by the RBF neural network for online learning of the compensation coefficients of velocity and acceleration. Numerical simulation and bench test results show that the proposed stability controller has strong self-learning and adaptation ability, and it is robust to disturbances of different frequencies and amplitudes. The time for parameter learning is less than 3.7 seconds, and the mean square deviation of integral position errors is less than 0.33 mil. The results verify the feasibility and effectiveness of the proposed controller for vehicle multi-tube loads.

Key words: vehicle multi-tube load, fractional order PID, extended state observer, hybrid differentiator, RBFNN

GAO Qiang, HOU Yuanlong, LÜ Mingming, MAO Bin, HOU Runmin, YANG Shuyi, WU Bin. A Marching Stable Control Method for a New Vehicle-Mounted Multi-Tube Load[J]. Acta Armamentarii, 2023, 44(3): 736-747.

Figures/Tables 20

Fig. 1 Structural diagram of a new vehicle-mounted multi-tube load system

Fig. 2 Block diagram of the controller

Fig. 3 Diagram of Sine velocity disturbance observations

Fig. 4 Diagram of Sine velocity disturbance observation errors

Fig. 5 Comparison of disturbed accelerations

Fig. 6 Position errors for the disturbance of 0.25 Hz/0.5○

Fig. 7 Position errors for the disturbance of 0.25 Hz/2.0○

Fig. 8 Position errors for the disturbance of 0.25 Hz/2.0○ with NN

Fig. 9 β1 and β2 learning curves for the disturbance of 0.25 Hz/2.0○

Fig. 10 Position errors for the disturbance of 0.5 Hz/4.0○ with NN

Fig.11 β1 and β2 learning graph of disturbance 0.5 Hz/4.0○

Fig. 12 Position errors for the disturbance of 1.0 Hz/2.0○ with NN

Fig.13 β1 and β2 learning graph of disturbance 1.0 Hz/2.0○

Fig. 14 Comparison of position errors using HD and SOSMD

Fig. 15 Stewart platform

Fig. 16 Operational interface of the host computer of a Stewart platform

Fig. 17 Position errors for the disturbance of 0.25 Hz/2.0○ on a Stewart platform

Fig. 18 Position errors for the disturbance of 0.5 Hz/4.0○ on a Stewart platform

Fig. 19 Position errors for the disturbance of 1.0 Hz/2.0○ on a Stewart platform

Fig. 20 Position errors for the disturbance of 1.0 Hz/2.0○ , tracking speed of 0.5○/s on a Stewart platform

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