Active disturbance rejection control based on inertia estimation and variable gain for servomechanism of industrial robot

Abstract

This paper proposes an adaptive PID controller based on linear extended state observer (LESO) for the two-degree-of-freedom joint servomechanism of industrial robot with time-varying load, uncertainties of parameters and disturbance. The third-order extended state space equations of the system approximate model is established to obtain LESO which is applied to estimate the state values and the total disturbance. The model reference adaptive algorithm is used to estimate the variable moment of inertia to design the controller parameter for control law which is designed with disturbance compensation. By appropriately selecting the adaptive gain coefficient of model reference adaptive algorithm and the bandwidth of LESO, the influences of parameter uncertainty, unknown dynamics and disturbances are effectively attenuated. Simulation and experimental results show that the proposed method achieves both satisfactory disturbance rejection and tracking performances of the two-degree-of-freedom joint servomechanism.

Research on Robotic Compliance Control for Ultrasonic Strengthening of Aviation Blade Surface

“…The regulation and tracking performance of the control algorithm is very important for nonlinear systems with uncertain dynamics and external disturbances [36,37]. To verify the control performance of the Fuzzy-RBF-PID controller for contact force, the step response and positive sinusoidal tracking response of the output force are simulated.…”

An Improve Nonlinear Robust Control Approach for Robotic Manipulators with PSO-based Global Optimization Strategy

“…However, due to the lack of consideration of the dynamic characteristics of the system during the control process, PID control methods can effectively handle system uncertainty, making it difficult to achieve high-precision tracking of the control system. Therefore, many scholars have integrated other algorithms into PID control [11][12][13][14]. For instance, in [14], an adaptive PID controller is developed on basis of linear extended state observer (LESO) for the two-degree-of freedom joint servomechanism of industrial robot with timevarying load, uncertainties of parameters and disturbance.…”

“…Therefore, many scholars have integrated other algorithms into PID control [11][12][13][14]. For instance, in [14], an adaptive PID controller is developed on basis of linear extended state observer (LESO) for the two-degree-of freedom joint servomechanism of industrial robot with timevarying load, uncertainties of parameters and disturbance. In addition, to improve the accuracy of robotic motion control, many model-based nonlinear control strategies have emerged [15][16][17], such as sliding mode control (SMC) [18], adaptive control [19], neural network control (NN) [20], model predictive control (MPC) [21], fuzzy control [22], and auto disturbance rejection control (ADRC) [23][24].…”