论文标题
非全面移动机器人的安全模型预测控制方法
Safe Model Predictive Control Approach for Non-holonomic Mobile Robots
论文作者
论文摘要
我们设计了一种模型预测控制（MPC）方法，以计划和控制非全面移动机器人。在预计的参考轨迹周围线性化系统动力学会产生时间变化的LQ MPC问题。我们在分析上表明，通过专门设计MPC控制器，随时间变化的线性化系统可以在跟踪任务中的来源周围产生渐近稳定性。我们进一步提出了两种避免障碍方法。我们表明，通过定义速度空间中的线性约束，并根据当前状态明确耦合两个控制输入，我们的第二种方法直接说明了系统的非独立属性，因此可以减轻优化问题的不可行性。仿真结果表明，关于静态和动态障碍物的避免，我们的LQ MPC方法的计划轨迹与解决非线性编程（NLP）问题相对平稳有效，但以更有效的方式。
We design an model predictive control (MPC) approach for planning and control of non-holonomic mobile robots. Linearizing the system dynamics around the pre-computed reference trajectory gives a time-varying LQ MPC problem. We analytically show that by specially designing the MPC controller, the time-varying, linearized system can yield asymptotic stability around the origin in the tracking task. We further propose two obstacle avoidance methods. We show that by defining linearized constraint in velocity-space and explicitly coupling the two control inputs based on current state, our second method directly accounts for the non-holonomic property of the system and therefore alleviates infeasibility of the optimization problems. Simulation results suggest that regarding both static and dynamic obstacle avoidance, the planned trajectories by our LQ MPC approach are comparably smooth and effective as solving non-linear programming (NLP) problems, but in a more efficient way.
