article · Engineering Research Express
A linear parameter varying-model predictive control method has been developed to improve trajectory tracking for autonomous vehicles. Built upon a time-varying state-space dynamic model of the vehicle, the approach captures changing vehicle behaviour with greater accuracy and manages operational constraints encountered during trajectory tracking. To enhance tracking performance, particle swarm optimisation is utilised to tune the weighting matrices of the control parameters, refining the overall system response. Extensive simulation tests were carried out to benchmark the framework against conventional linear and non-linear model predictive control systems. The findings demonstrate that this method achieves tracking performance comparable to computationally heavy non-linear controllers while significantly lowering computational expense. Furthermore, it demonstrates marked improvements over linear controllers, especially when following non-linear reference trajectories.
Autonomous vehicles require navigation systems that maintain high tracking precision without overloading onboard processors. Non-linear control methods are accurate but computationally demanding, whereas standard linear models often struggle with complex trajectories. This method provides an effective compromise, delivering accuracy close to non-linear controllers while reducing processor load, which is critical for real-time vehicular control.
This control technique could be used by autonomous vehicle software developers and automotive manufacturers seeking efficient path-tracking algorithms for onboard vehicle computers. As the method has only been evaluated through simulation studies, it remains at an early, analytical stage of development and requires validation on physical vehicle hardware before commercial integration.
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Abstract In this paper, Linear Parameter Varying-Model Predictive Control (LPV-MPC) for trajectory tracking for Autonomous Vehicles (AVs) is proposed. This method is based on the time-varying LPV is the form of the state space representation from the mathematical model of the vehicle. The LPV representation form which uses the dynamic model of the vehicle allows the incorporation of time-varying dynamics, providing a more accurate representation of the vehicle's behavior. The designed LPV-MPC controller for AVs is specifically designed to handle constraints in trajectory tracking. To enhance its performance, Particle Swarm Optimization (PSO) is employed as an optimization technique. PSO is used to tune the weighting matrices of the control parameters, optimizing the system response and improving trajectory tracking performance. To evaluate the effectiveness of the LPV-MPC system, extensive simulations are conducted and results are compared with Linear and Non-Linear MPCs. The main benefit of using the LPV-MPC method is its ability to calculate solutions almost as good as the non-linear MPC version yet significantly reducing the computational cost. The capability of the LPV-MPC controller as compared to the linear version is in its effective tracking, particularly for the non-linear reference trajectories.
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DOI: 10.1088/2631-8695/ad722e
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