Finite time self-adaptive stabilization control method and system for mechanical arm system

Abstract

The invention provides a finite time self-adaptive stabilization control method and system for a mechanical arm system. The method comprises the steps of building a kinetic equation of the mechanical arm system, converting the kinetic equation into an equivalent Hamilton model, and then designing a finite time self-adaptive observer and a controller of the mechanical arm system to achieve expansion into a high-dimension Hamilton model; and constructing a Lyapunov function, proving that the zero-state response of a closed-loop system meets the condition that an L2 gain is not larger than the disturbance attenuation level through first-order derivation of the Lyapunov function, ensuring robust self-adaptive stabilization of the mechanical arm system, and proving that when the interference attenuation is zero, the system meets the condition of finite time stability, and converges in finite time to ensure the stability of the system. Based on the method, the invention further provides a finite-time self-adaptive stabilization control system for the mechanical arm system. According to the method and the system, the finite time observer closed-loop system is adopted, rapid convergence can be achieved, good robustness is achieved for external interference, and the self-adaptability of the mechanical arm system is improved.

Description

technical field [0001] The invention belongs to the technical field of self-adaptive stabilizing control of a manipulator, in particular to a limited-time self-adaptive stabilizing control method and system of a manipulator system. Background technique [0002] The manipulator is a highly complex nonlinear system with time-varying coupling dynamics, which has inaccuracies in measurement or modeling, and is often affected by system load changes and external disturbances. Therefore, the problem of robust control of uncertain systems has attracted extensive attention. The existing technology adopts PID control, synovial film control, calculated torque control, robust control, neural network control, etc. The control law of PID control method is simple and easy to implement, and does not require accurate dynamic model parameters of the manipulator, but its control accuracy is poor. , Poor robustness. The sliding film control method is not affected by the parameters of the dyna...

AI Technical Summary

Problems solved by technology

The existing technology adopts PID control, synovial film control, calculated torque control, robust control, neural network control, etc. The control law of PID control method is simple and easy to implement, and does not require accurate dynamic model parameters of the manipulator, but its control accuracy is poor. , poor robustness

The sliding film control method is not affected by the dynamic model parameters of the manipulator and the external disturbance changes, and the response speed is fast, but there is a phenomenon of "chattering" in the control process, which affects certain tracking accuracy and wears out the equipment

The calculation torque control method has better control accuracy, but it needs accurate model parameter support

However, in practical situations, it is difficult to guarantee the exact model parameter requirements

The robust control method achieves the effect of stable control by setting the maximum upper bound of the disturbance, which is easy to implement, but it needs to determine the maximum upper bound range of the disturbance based on the experience and subjective judgment of engineers, without certain learning ability and adaptability; The network control method has a good universal approximation effect, and it can approximate the unknown nonlinear function of the system without modulus parameters

However, it does not consider the unknown external disturbance of the system, and a robust term needs to be introduced for compensation

Images