In Proceedings of the Mechatronics and Robotics 2004, Aachen, Germany, 13–15 September 2004. Model Predictive Control of a Mobile Robot Using Linearization. In Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea, 28 June–1 July 2015 pp. Kinematic and dynamic vehicle models for autonomous driving control design. A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles. TS-MPC for Autonomous Vehicles Including a TS-MHE-UIO Estimator. The authors declare no conflict of interest. provided a stability and performance analysis for nonlinear model predictive control schemes subject to input constraints. proposed a nonlinear model predictive control with parametric tail-end contraction constraints in addition, experiments showed that the control effect of model predictive controller with tail-end contraction constraints is significantly better than that of model predictive controller without tail-end contraction constraints. used the method of adding constraints in the terminal area instead of adding contraction constraints at the terminal point to improve the MPC, and gave the open-loop control results and closed-loop control results of model predictive control, which verifies the stability of tail-end constraints. gave a summary of MPC theoretical research, and it is pointed out that the stability of the MPC controller can be enhanced with tail-end contraction constraints, while lead to an accompanying increase in the computing time. proposed a model predictive controller with a PID feedback controller to improve the tracking accuracy of the model predictive control in the path tracking process, so that the state error tended to zero during the path tracking process. proposed a nonlinear model predictive control algorithm, which verifies the feasibility of nonlinear model predictive control in vehicle path tracking. proposed a Particle Swarm Optimization (PSO) algorithm to change the MPC predictive control step size in real time to study the influence of the MPC predictive step size on vehicle path tracking. However, due to the nonlinearity of the system, these problems are not necessarily non-convex. In common with linearized MPC, NMPC needs to iteratively solve the optimal control problem in a finite time domain. pointed out that nonlinear model predictive control (NMPC) is a variant of model predictive control (MPC), which is mainly applicable to the nonlinear control system or the case that is inconvenient to linearize. adopted the kinematics model of self-driving vehicles, and linearly expanded the nonlinear vehicle kinematics model at the trajectory reference point, established a linearized model predictive controller, and finally converted it into a Quadratic Programming (QP) solution. In recent years, nonlinear model predictive control has been widely used in path tracking of self-driving vehicles, nevertheless, most of the current researches use the method of linear expansion at the trajectory reference point, without considering the stability of nonlinear model predictive control. The open-loop and closed-loop simulation results are then presented to demonstrate the improved performance in tracking accuracy and stability compared to traditional model predictive controller. The algorithm was implemented on CasADi numerical optimization framework, which is free, open-source and developed for nonlinear optimization. The cost function of NMPC–WTC consists of two parts: (1) the traditional NMPC cost function responding to tracking errors and controller output, and (2) the augmented terminal cost. In order to improve the squeezing phenomenon of traditional NMPC, a discrete-time nonlinear model predictive controller with terminal cost is then designed, in which the state error of last step is augmented. The path tracking issue is formulated as an optimal control problem. This paper presents a nonlinear model predictive control with terminal cost (NMPC–WTC) algorithm and its open/closed-loop system analysis and simulation validation for accurate and stable path tracking of autonomous vehicles.
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