Toyota Research Institute, Inc.

Toyota Motor Engineering & Manufacturing North America, Inc.

Kevin M Zaseck

Avinash Balachandran

Carrie Bobier-Tiu

Mike Xuli Huang

Frank Permenter

Soonho Kong

Sarah Koehler

Dominic M Liao-McPherson

Systems and methods for automatically generating solver code for a nonlinear model predictive controller are disclosed. In one embodiment, a method of automatically generating solver code for a nonlinear model predictive control solver includes receiving an optimal control problem code, wherein the optimal control problem code represents an optimal control problem comprising a cost function, one or more constraints, and a continuous time model representing dynamics of a system. The method further includes receiving a discretization method preference, a linearization point preference, and a parameter specification, and encoding the optimal control problem into an optimization problem by discretizing the optimal control problem according to the discretization method preference, and linearizing the optimal control problem according to the linearization point preference. The method further includes generating the solver code from the optimization problem.

Systems and methods for automatically generating solver code for nonlinear model predictive control solvers

System, methods, and other embodiments described herein relate to selectively intervening in manual control of a vehicle by a driver. In one embodiment, a method includes predicting a future state of the vehicle according to at least a current state and a control input. The current state defines at least one attribute of a current trajectory of the vehicle, and the control input defines at least one driver input for controlling the vehicle. The method includes comparing the future state with a state constraint indicating a range within which a target path of the vehicle is acceptable. The target path defines a subsequent trajectory for the vehicle. The method includes selectively modifying the target path according to whether the future state violates the state constraint. The method includes controlling the vehicle according to the target path.

Systems and methods for shared control of a vehicle

A system including a controller configured to, in each sampling period, minimize a distance of the autonomous vehicle from a target path by solving a constrained control problem, input sensor values and estimators that are calculated based on the sensor values and dynamic models and record the sensor values and the estimators in a memory of the controller, incorporate the sensor values and the estimators into conditions for minimizing the distance of the autonomous vehicle from the target path associated with the constrained control problem, map the conditions for minimizing the distance of the autonomous vehicle from the target path to a non-smooth system using Fischer-Burmeister function, smooth the non-smooth system and apply Newton method iterations to the smoothed system in order to converge on a solution, and issue commands including a steering command that control actuators of the autonomous vehicle based on the solution.

Smoothed and regularized Fischer-Burmeister solver for embedded real-time constrained optimal control problems in autonomous systems

Growing community of inventors

New Hudson, MI, United States of America

Kevin M Zaseck