Model Predictive Control for Automated Vehicle Steering

Abstract

The autonomous vehicle steering system, a multi-input multi-output (MIMO) system, is challenging to design using traditional controllers due to the interaction between inputs and outputs. If PID controllers are used the control loops are executed independently of each other as there is no interaction between the loops. Designing a larger system increases the controller parameters requiring tuning. Model Predictive Control (MPC) overcomes this problem, as it is a multi-variable control method taking into account the interactions of the variables in the target system. Achieving a high safety level is also critical for autonomous vehicle systems. This can be provided by an MPC controller, which can handle constraints such as maintaining a safe distance from other cars. Wider applicability of the Model Predictive Controller calls for more efficient hardware architectures for implementation. The aim of this paper is to achieve optimal implementation of the MPC controller by increasing the computational speed in order to reduce execution time for optimization. An MPC controller is used to control the steering system of an autonomous vehicle to keep it on the desired path. A traditional MPC controller is used to control the system where the plant dynamics do not change, whereas an Adaptive MPC controller is used when the system is nonlinear or its characteristics vary with time (the longitudinal velocity changes as the vehicle moves). Results are discussed in terms of performance, resource utilization, cost, and energy-effective implementations taking into consideration a reasonable size number of constraints handled by the controller.

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