KINEMATIC MINIMUM-TIME TRAJECTORY PLANNING FOR A MANIPULATOR

By using a phase-plane analysis method,the minimum-time trajectory plan-ning problem of a manipulator moving along a given geometric path subject to the con-straints of joint velocities and accelerations is solved in this paper.The simulation resultfor the first three joints of PUMA-560 is given.

Minimum-time trajectory planning based on the shortest path for the wheeled mobile robot

The time-optimal trajectory planning is proposed under kinematic and dynamic constraints for a 2-DOF wheeled robot. In order to make full use of the motor’s capacity, we calculate the maximum torque and the minimum torque by considering the maximum heat-converted power generated by the DC motor. The shortest path is planned by using the geometric method under kinematic constraints. Under the bound torques, the velocity limits and the maximum acceleration (deceleration) are obtained by combining with the dynamics. We utilize the phase-plane analysis technique to generate the time optimal trajectory based on the shortest path. At last, the computer simulations for our laboratory mobile robot were performed. The simulation results prove the proposed method is simple and effective for practical use.

MPC-based path tracking with PID speed control for high-speed autonomous vehicles considering time-optimal travel

In order to track the desired path as fast as possible,a novel autonomous vehicle path tracking based on model predictive control(MPC)and PID speed control was proposed for high-speed automated vehicles considering the constraints of vehicle physical limits,in which a forward-backward integration scheme was introduced to generate a time-optimal speed profile subject to the tire-road friction limit.Moreover,this scheme was further extended along one moving prediction window.In the MPC controller,the prediction model was an 8-degree-of-freedom(DOF)vehicle model,while the plant was a 14-DOF vehicle model.For lateral control,a sequence of optimal wheel steering angles was generated from the MPC controller;for longitudinal control,the total wheel torque was generated from the PID speed controller embedded in the MPC framework.The proposed controller was implemented in MATLAB considering arbitrary curves of continuously varying curvature as the reference trajectory.The simulation test results show that the tracking errors are small for vehicle lateral and longitudinal positions and the tracking performances for trajectory and speed are good using the proposed controller.Additionally,the case of extended implementation in one moving prediction window requires shorter travel time than the case implemented along the entire path.

Multilevel Techniques for the Solution of HJB Minimum-Time Control Problems

The solution of minimum-time feedback optimal control problems is generally achieved using the dynamic programming approach,in which the value function must be computed on numerical grids with a very large number of points.Classical numerical strategies,such as value iteration(VI)or policy iteration(PI)methods,become very inefficient if the number of grid points is large.This is a strong limitation to their use in real-world applications.To address this problem,the authors present a novel multilevel framework,where classical VI and PI are embedded in a full-approximation storage(FAS)scheme.In fact,the authors will show that VI and PI have excellent smoothing properties,a fact that makes them very suitable for use in multilevel frameworks.Moreover,a new smoother is developed by accelerating VI using Anderson’s extrapolation technique.The effectiveness of our new scheme is demonstrated by several numerical experiments.

Time-optimal guidance for intercepting moving targets with impact-angle constraints

The minimum-time path for intercepting a moving target with a prescribed impact angle is studied in the paper.The candidate paths from Pontryagin’s maximum principle are synthesized,so that each candidate is related to a zero of a real-valued function.It is found that the real-valued functions or their first-order derivatives can be converted to polynomials of at most fourth degree.As a result,each candidate path can be computed within a constant time by embedding a standard polynomial solver into the typical bisection method.The control strategy along the shortest candidate eventually gives rise to the time-optimal guidance law.Finally,the developments of the paper are illustrated and verified by three numerical examples.

Approximate finite-horizon optimal control for input-affine nonlinear systems with input constraints

The finite-horizon optimal control problem with input constraints consists in controlling the state of a dynamical system over a finite time interval(possibly unknown)minimising a cost functional,while satisfying hard constraints on the input.In this framework,the minimum-time optimal control problem and some related problems are of interest for both theory and applications.For linear systems,the solution of the problem often relies upon the use of bang-bang control signals.For nonlinear systems,the“shape”of the optimal input is in general not known.The control input can be found solving a Hamilton–Jacobi–Bellman(HJB)partial differential equation(PDE):it typically consists of a combination of bang-bang controls and singular arcs.In this paper,a methodology to approximate the solution of the HJB PDE is proposed.This approximation yields a dynamic state feedback law.The theory is illustrated by means of two examples:the minimum-time optimal control problem for an industrial wastewater treatment plant and the Goddard problem,i.e.a maximum-range optimal control problem.