Abstract
As a simple and effective force tracking control method, impedance control is widely used in robot contact operations. The internal control parameters of traditional impedance control are constant and cannot be corrected in real time, which will lead to instability of control system or large force tracking error. Therefore, it is difficult to be applied to the occasions requiring higher force accuracy, such as robotic medical surgery, robotic space operation and so on. To solve this problem, this paper proposes a model reference adaptive variable impedance control method, which can realize force tracking control by adjusting internal impedance control parameters in real time and generating a reference trajectory at the same time. The simulation experiment proves that compared with the traditional impedance control method, this method has faster force tracking speed and smaller force tracking error. It is a better force tracking control method.
Highlights
With the progress of robotics technology, robots are widely used in contact operations such as grinding, assembly, space operations, medical surgeries and so on
Literature [2,3] uses the learning impedance control method to achieve effective force tracking control, but the training process of this method requires a lot of data and is difficult to apply to real-time systems
Model reference adaptive impedance control (MRAIC) [11] can correct the trajectory in real time, and it can keep the system stable even when the external environment changes greatly
Summary
With the progress of robotics technology, robots are widely used in contact operations such as grinding, assembly, space operations, medical surgeries and so on. Literature [2,3] uses the learning impedance control method to achieve effective force tracking control, but the training process of this method requires a lot of data and is difficult to apply to real-time systems. Literature [6,7] adopt neural network control method to achieve force tracking control, but this method is usually designed complex and it takes several weeks or even longer to training data. Adaptive control is widely used because it does not require sample training, and can adjust impedance control parameters in real time according to force errors. Literature [9] proposed a nonlinear bilateral adaptive impedance control method for teleoperation systems. Literature [10] proposed a novel backstepping adaptive impedance control for an upper limb rehabilitation robot. In the rest of the article, three impedance control methods are introduced and simulated, which are traditional position-based impedance control method, model reference adaptive impedance control method and model reference adaptive variable impedance control method
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