Output Feedback Control for Launch Platform Using Neural Network Observer and Output Constraint

doi:10.12382/bgxb.2022.0229

Abstract

Abstract:

A launch platform, driven by two permanent magnet synchronous motors, is used to launch kinetic energy loads. However, the platform often faces strong parameter uncertainties and unknown external disturbances, such as air impact, during practical operation. These factors can greatly reduce the platform’s tracking accuracy. To solve this problem, an output feedback controller based on adaptive neural network observer with output constraints is proposed for high-precision motion control of the launch platform. The controller uses ESO to estimate system parameter uncertainties in the system, and uses the velocity value of the system to design relevant control parameters to achieve the purpose of output feedback control. An improved Spline CMAC neural network is designed to estimate the unknown disturbances in the system. Feedforward compensation technique is used to compensate parameter uncertainty and time-varying disturbance. Considering the output constraints of the launch platform in practice, the control rate is designed by using the obstacle Lyapunov function analysis method. The stability of the system is also proved. The simulation and experimental results demonstrate that the proposed composite controller can achieve uniform final bounded stability of the system with good tracking performance and strong anti-interference capability while considering the output constraints. The proposed approach represents a great improvement over traditional control methods.

Key words: launch platform, output feedback control, cerebellar model articulation controller neural network, output constraint, barrier lyapunov function

SONG Qiuyu, HU Jian, YAO Jianyong, BAI Yanchun, YANG Zhengyin. Output Feedback Control for Launch Platform Using Neural Network Observer and Output Constraint[J]. Acta Armamentarii, 2023, 44(7): 2184-2196.

Figures/Tables 16

Fig.1 Block diagram of launch platform system

Fig.2 A spline CMAC neural network with three layers and two inputs-only the activated basis functions indexed by the input are illustrated

Fig.3 Assuming that the prediction is correct (the index moves from left to right): the index base function predicts that the next index cell will be on the right at time 1 (Fig. 3(a)); the original base function is still active at time 2 (Fig. 3(b)), which overlaps with the new base function on the index layer at time 2 (Fig. 3(c))

Fig.4 Control strategy diagram

Table 1 Parameter values of the electromechanical actuation system of the launch platform

系统参数	数值
电机端转动惯量J/(kg·m²)	0.031
电机力矩系数k_u/(N·m·V^-1)	1.04
黏性摩擦系数B/(N·m·s·rad^-1)	0.035
减速比	190

Fig.5 Output curve of the command signals

Fig.6 Position curve of OF_ESO_ Spline CMAC

Fig.7 Tracking error of OF_ESO_ Spline CMAC

Fig.8 Comparison of three neural network estimates

Table 2 Simulation tracking error index (°)

控制器	最大值	平均值	标准方差
OF_ESO	0.01077	0.00299	0.005498
OF_ESO_RBF	0.006459	0.001493	0.003516
OF_ESO_CMAC	0.005864	0.001278	0.003234
OF_ESO_ Spline CMAC	0.003762	0.0003677	0.002393

Fig.9 Comparison of tracking errors of four controllers

Fig.10 Launch platform and control circuit board based on DSP (the top is the launch platform, and the bottom is the control circuit board)

Table 3 System parameter values ofthe electromechanical actuation system of the launching platform

系统参数	数值
电机端转动惯量J/(kg·m²)	0.031
电机力矩系数k_u/(N·m·V^-1)	1.04
黏性摩擦系数B/(N·m·s·rad^-1)	0.035
减速比	190
定子电阻R/Ω	1.32
绕组电感L/H	0.0311
额定电流I/A	3.1

Fig.11 Comparison of tracking errors of five controllers under the working condition Ⅰ

Fig.12 Comparison of tracking errors of five controllers under the working condition Ⅱ

Fig.13 Comparison of tracking errors of five controllers under the working condition Ⅲ

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