IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-05-23T11:29:08ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2018-03-12T10:28:15Z2018-03-12T10:28:15Zhttp://eprints.imtlucca.it/id/eprint/4022This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/40222018-03-12T10:28:15ZTowards time-optimal race car driving using nonlinear MPC in real-timeThis paper addresses the real-time control of autonomous vehicles under a minimum traveling time objective. Control inputs for the vehicle are computed from a nonlinear model predictive control (MPC) scheme. The time-optimal objective is reformulated such that it can be tackled by existing efficient algorithms for real-time nonlinear MPC that build on the generalized Gauss-Newton method. We numerically validate our approach in simulations and present a real-world hardware setup of miniature race cars that is used for an experimental comparison of different approaches.Robin VerschuerenStijn De BruyneMario Zanonmario.zanon@imtlucca.itJanick V. FraschMoritz Diehl2018-03-09T14:15:46Z2018-03-09T14:15:46Zhttp://eprints.imtlucca.it/id/eprint/4007This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/40072018-03-09T14:15:46ZTime-optimal race car driving using an online exact hessian based nonlinear MPC algorithmThis work presents an embedded nonlinear model predictive control (NMPC) strategy for autonomous vehicles under a minimum time objective. The time-optimal control problem is stated in a path-parametric formulation such that existing reliable numerical methods for real-time nonlinear MPC can be used. Building on previous work on timeoptimal driving, we present an approach based on a sequential quadratic programming type algorithm with online propagation of second order derivatives. As an illustration of our method, we provide closed-loop simulation results based on a vehicle model identified for small-scale electric race cars.Robin VerschuerenMario Zanonmario.zanon@imtlucca.itRien QuirynenMoritz Diehl2018-03-09T14:12:55Z2018-03-09T14:12:55Zhttp://eprints.imtlucca.it/id/eprint/4008This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/40082018-03-09T14:12:55ZA Sparsity Preserving Convexification Procedure for Indefinite Quadratic Programs Arising in Direct Optimal ControlQuadratic programs (QP) with an indefinite Hessian matrix arise naturally in some direct optimal control methods, e.g., as subproblems in a sequential quadratic programming scheme. Typically, the Hessian is approximated with a positive definite matrix to ensure having a unique solution; such a procedure is called regularization. We present a novel regularization method tailored for QPs with optimal control structure. Our approach exhibits three main advantages. First, when the QP satisfies a second order sufficient condition for optimality, the primal solution of the original and the regularized problem are equal. In addition, the algorithm recovers the dual solution in a convenient way. Second, and more importantly, the regularized Hessian bears the same sparsity structure as the original one. This allows for the use of efficient structure-exploiting QP solvers. As a third advantage, the regularization can be performed with a computational complexity that scales linearly in the length of the control horizon. We showcase the properties of our regularization algorithm on a numerical example for nonlinear optimal control. The results are compared to other sparsity preserving regularization methods.
Read More: https://epubs.siam.org/doi/10.1137/16M1081543Robin VerschuerenMario Zanonmario.zanon@imtlucca.itRien QuirynenMoritz Diehl