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Direct optimal control methods first discretize a continuous-time Optimal Control Problem (OCP) and then solve the resulting Nonlinear Program (NLP). Sequential Quadratic Programming (SQP) is a popular family of algorithms to solve this finite dimensional optimization problem. In the specific case of a least squares cost, the Generalized Gauss-Newton (GGN) method is a popular approach which works...
Time-optimal motion planning for robotic manipulators consists of moving the robot along a path in Cartesian space as fast as possible. In contrast to time-optimal path following, small deviations from a predefined path are acceptable and can be exploited to further reduce the overall motion time. In this paper, we present a new method to compute time-optimal motions around a path. By employing an...
This paper discusses path-following control for robotics, moving a manipulator along a path in Cartesian space, making a trade-off between tracking accuracy and the speed at which the path is followed. We present and validate a nonlinear model predictive control (NMPC) approach suitable for this nonlinear control task. This approach entails a method to model the position of the robot end-effector...
This paper proposes a framework for automated highway driving of an A-double long vehicle combination. The included driving manoeuvres are maintain lane, lane change to right and left, abort lane change to right and left, and emergency brake. A combined longitudinal and lateral driver model is used for the generation of longitudinal acceleration and steering requests. The behaviour of the driver model,...
This paper compares the vehicle dynamics performances of two approaches for automated lane change manoeuvres of a long vehicle combination in simulated highway driving. One of the two approaches is a non-linear model predictive controller (NMPC), and the other is based on driver model control (DMC) theory. Both approaches utilize traffic situation predictions that include motion variable constraints...
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