Adaptive neural network tracking control for unmanned electric shovel intelligent excavation system

Abstract

This study proposes an adaptive control strategy for unmanned mining shovel digging trajectory tracking based on radial basis function neural network (RBFNN) and a class of unmanned mining shovel time-varying systems with model uncertainty and external disturbances. A new set of Lagrangian dynamics differential equations is reconstructed by utilizing the kinematic model of the electric shovel and considering external disturbances along with modeling uncertainties. This approach lays the groundwork for subsequent adaptive controllers. The proposed controller is designed to regulate the position errors of the unmanned mining electric shovel system, which is characterized by a complex structure, high load, large size, and strong coupling. It takes the deviation values and their derivatives of the lifting and pushing movements as inputs and adjusts the output torque to converge the bucket position to the desired trajectory. The controller utilizes the RBFNN in the control law to compensate for uncertainties in this type of system with large disturbances and inertia. This compensation helps eliminate the impact of external disturbances and modeling uncertainties on the unmanned mining electric shovel’s ability to follow the excavation trajectory. The consistent boundedness of the closed-loop system’s ultimate limits is proven through Lyapunov stability theory. Finally, the effectiveness of the proposed solution is validated through simulation experiments.

Graphical abstract

Keywords

adaptive control / RBFNN / unmanned electric shovel / trajectory tracking

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