Nonlinear Model Predictive Control for Tractors Based on an Efficient Neural Network Optimization Strategy

Abstract

The application and performance of Nonlinear Model Predictive Control (NMPC) are critically limited by the computational efficiency of solving nonlinear combinatorial optimization problems. To address this challenge, this study proposes an efficient optimization strategy that employs a neural network to solve the constrained L-1 norm minimization problem within the NMPC framework, thereby enhancing motion control performance. Inspired by the flexible representational capacity and powerful optimization capabilities of neural networks, we explicitly encode the NMPC objective function into a network architecture. The optimal control solution is then obtained efficiently through network training. We validate the proposed strategy in a tractor path-tracking control task, detailing the processes of network construction and optimization. Benefiting from the inherent parallelism and computational efficiency of neural networks, the resulting controller demonstrates excellent real-time performance. Specifically, with prediction horizons set to 5, 10, and 20 steps, the solution times are reduced to less than 0.12, 0.28, and 0.84 s, respectively, under typical operating constraints.