Underactuated systems like sea vessels have degrees of motion that are insufficiently matched by a set of independent actuation forces. In addition, the underlying trajectory-tracking control problems grow in complexity in order to decide the optimal rudder and thrust control signals. This enforces several difficult-to-solve constraints that are associated with the error dynamical equations using classical optimal tracking and adaptive control approaches. An online machine learning mechanism based on integral reinforcement learning is proposed to find a solution for a class of nonlinear tracking problems with partial prior knowledge of the system dynamics. The actuation forces are decided using innovative forms of temporal difference equations relevant to the vessel's surge and angular velocities. The solution is implemented using an online value iteration process which is realized by employing means of the adaptive critics and gradient descent approaches. The adaptive learning mechanism exhibited well-functioning and interactive features in react to different desired reference-tracking scenarios.
翻译:此外,潜在的轨迹跟踪控制问题越来越复杂,以便决定最佳舵和推力控制信号。这强制实施与错误动态方程式相关的若干难以解决的制约因素,这些制约因素是利用传统的优化跟踪和适应性控制方法,与错误动态方程式相关联的。基于综合强化学习的在线机器学习机制建议寻找一种非线性跟踪问题的解决办法,先部分了解系统动态,然后找到非线性跟踪问题的解决办法。启动力是使用与船只的浮力和角速度相关的时间差异方程式来决定的。解决方案采用在线价值迭代程序,通过采用适应性批评家和梯度下降方法实现。适应性学习机制在应对不同的参考跟踪情景时表现出功能良好和互动的特点。