Order Formation in the Learning and Adaptive System and Its Application to Mechanical Control

针对不确定性复杂系统之间的竞争或合作，通过学习或自适应系统设计控制系统以实现复杂机械系统的有用功能的问题进行了研究，分别考虑了具有确定性和非结构不确定性的系统、具有确定性和结构不确定性的系统以及具有随机不确定性的系统，提出了几种利用神经网络设计鲁棒控制器的方法。我们发现，非常简单的顺序，即幂律形成的性能和鲁棒性之间的关系的一组控制器的训练所提出的方法，其中考虑到被控对象的不确定性。从最优化的观点看，鲁棒控制器的学习设计方法实质上是多目标优化问题。简单序是形成简单序的主要原因之一，它广泛存在于许多自然系统中 关于我们 在多目标优化问题的状态空间中，Pareto解集的形成是多目标优化问题的关键。因此，针对环境不确定性的竞争学习，以提高鲁棒性，与简单的订单形成密切相关。固定鲁棒控制器对所考虑的鲁棒性是鲁棒的，但它们可能对设计控制器时从未考虑的不确定性是脆弱的。因此，不仅竞争学习对考虑的不确定性，而且适应是重要的。研究了模块化控制器自适应任务分解及其阶次形成问题。提出了一种自适应模块强化学习算法，能够对复杂机械系统进行任务分解。我们应用所提出的方法设计了无人直升机的飞行控制器，特别是高度控制器。在设计控制器时，垂直风被视为不确定性。发现自适应模块将高度控制任务进行了分解，形成了简单的指令。根据直升机的运动方向对控制器的任务进行分解，以提高对垂直风的鲁棒性，并通过学习自适应地为每个任务选择合适的控制器。少

Research on designing control system to realize useful functions of complicated mechanical systems by learning or adaptive system considering competition or cooperation between the uncertain complex systems was performed.In our researches, systems with deterministic and un-structured uncertainties, systems with deterministic and structured uncertainties, and systems with stochastic uncertainties were considered, and we proposed several design methods of robust controllers by using neural networks. We found that the very simple order that is power law is formed in the relation between the performance and the robustness of the set of controllers which are trained by the proposed methods in which uncertainties of the controlled object are taken into account. From the viewpoint of the optimization, design methods of robust controllers by learning is equals to the multi objective optimization problems. One of main causes of forming simple order, which is widely found in many natural systems … More , is forming of the set of Pareto solutions in the state space of multi-objective optimization. Therefore the competitive learning against the environmental uncertainties to improve robustness has close relation to simple order formation. Fixed robust controllers are robust against the considered robustness but they may be fragile to uncertainties which are never considered in designing controllers. Therefore not only the competitive learning against considered uncertainties but also adaption is important. Task decomposition by adaptation of modular controller and its order formation was investigated. We proposed an reinforcement learning algorithm for adaptive modules, which are able to decompose tasks of the complicated mechanical system. We applied the proposed method to design flight controllers, especially altitude controllers, for an unmanned helicopter. In designing controllers, vertical wind was considered as uncertainties. We found that the adaptive modules decomposed the task of the altitude control and the modules formed the simple order. As a result, controller's task was decomposed according to the direction of the helicopter's motion to improve the robustness against vertical wind and suitable controllers for each tasks were adaptively obtained by learning. Less