With the continuous improvement of modern science and technology,various high-tech military equipment continue to emerge,and its characteristics of intelligence,unmannedness,and precision are becoming more and more obvious.Intelligent weapons represented by intelligent combat robots and drones are gradually being used in modern warfare and have played an important role.Flexible manipulators have been widely used as the execution device of intelligent combat robots due to their light weight,low energy consumption and fast response.Its motion control performance directly determines whether intelligent weapons can complete combat tasks with high efficiency and high precision on the battlefield.Therefore,it is necessary to study high-performance motion control strategies to achieve high-precision trajectory tracking and vibration suppression.Based on the above analysis,the following works are focused:1.According to the motion characteristics of the flexible manipulator,a suitable equivalent beam model is selected for it,and the driving principle of the manipulator is introduced.Combining the assumed modal method and Lagrangian equations,a nonlinear mathematical model of the flexible manipulator system is established.The model fully considered load changes,gravity and elastic deformation,and more closely describes the physical dynamics of the manipulator when performing tasks.Features,lay the foundation for the design of subsequent high-performance control strategies.2.Aimed at the problem that it is difficult to measure the vibration amount and vibration speed of the flexible manipulator,a nonlinear state observer is proposed based on the nonlinear model of the flexible manipulator system.The difference between the known state quantity and the estimated state quantity is used as a correction term to observe the system vibration and vibration speed,realize the knowledge of the whole system state,and provide the necessary conditions for the realization of the state feedback in the subsequent controller.3.In order to improve the motion control performance of the flexible manipulator,a composite learning control algorithm is proposed.The algorithm combines RBF neural network and interference estimator.The former is used to approximate and eliminate the nonlinear uncertainty caused by system flexibility,and the latter is used to estimate the RBF neural network’s approximation errors,load changes and nonlinear friction.At the same time,a robust term is designed to ensure the stability of the system.The Lyapunov theory proves that the proposed control method can ensure the boundedness of the tracking error and the stability of the closed-loop system.4.Based on the experimental platform of the flexible manipulator system,the proposed composite learning control method is experimentally verified,and compared with other existing control methods,the effects of trajectory tracking and vibration suppression in the experiment are analyzed and compared,and the proposed control is proved effectiveness of the method. |