Memristive Neural Network Circuit Implementation of Model Predictive Control for Trajectory Tracking

Model Predictive Control (MPC), a receding-horizon optimal control strategy, predicts system dynamics and optimizes control actions to satisfy performance and constraint requirements, making it widely adopted in control engineering. However, contemporary computing platforms struggle to meet the real-time and energy-efficient demands of MPC’s computationally intensive matrix operations, stemming from high data movement overhead, extensive circuit resource utilization, and frequent data conversions inherent in physical system interfaces. These challenges collectively impose significant latency and power penalties, particularly critical as systems grow in complexity and scale within the big-data era. This article introduces a Zeroing Neural Network (ZNN)-based memristive neural network circuit that directly converges the MPC error function to zero in one step. Theoretical analysis and simulations validate the closed-loop circuit’s stability. For a 32-step prediction horizon, evaluations show that the control output from the proposed circuit matches the ideal digital MPC solution with 96.0% accuracy. The circuit also executes at least an order of magnitude faster and consumes less energy than traditional MPC solvers. Additionally, the circuit successfully accelerates the proposed trajectory tracking algorithm, achieving 98.0% accuracy compared with the theoretical result and 318.2× improvement in computation time compared to CPU.