A Novel Model-Free Adaptive Control Algorithm for the Unmanned Excavation of a Class of Large Mining Electric Shovels

To improve the intelligence and unmanned operation level of mining electric shovels, a Model-Free Adaptive Control algorithm is proposed for precise control of mining trajectories. First, the control problem of the excavation trajectory of the bucket is transformed into a control problem involving the push arm and lifting rope. Then, a novel partial form dynamic linearization (PFDL) technique is then adopted to transform the dynamic model of the mining electric shovel into a linear data model with a time-varying pseudo gradient (PG), and the proposed controller (PFDL-MFAC) is designed based on this data model. Notably, the implementation of this controller involves no model information, making it a purely data-driven control algorithm. The effectiveness of the proposed method is validated through numerical simulations in MATLAB platform by restoring the structural parameters of a 1:7 scaled prototype of the mining electric shovel.