不定理想轨迹下的跟踪控制问题

许多重要应用领域(如导弹拦截，卫星追踪，电力系统用电量调度及匹配等)均涉及对未知或不确定理想（目标）轨迹的全程跟踪控制。本课题拟研究的核心内容是，如何建立不依赖精确目标轨迹的自适应跟踪控制理论及方法。基本研究思路是，利用神经网络(NN)、扩展卡尔曼滤波(EKF)及记忆推理(MR)建立理想轨迹重构与预测方法，解决目标轨迹无法直接用于反馈设计难题；通过搭建可以连结预测轨迹与不定理想轨迹的关联模型，借助受限李亚普洛夫稳定性理论，设计基于重构轨迹的鲁棒自适应跟踪控制策略，并综合论证和评估跟踪实效性。项目将取得系列创新成果，这对丰富和完善跟踪控制理论内涵具有重要意义，同时在包括导弹拦截、变轨飞行目标追踪等领域有广泛应用前景。

Tracking control under uncertain or unknown target trajectory is frequently encountered in many important applications, such as missile interception,satellite tracking, power system planning and dispatching, etc.In this project,we study the adaptive tracking control problem for a class of systems without the need for a priori precise knowledge of the desired target trajectory.To circumvent the difficulty that the uncertain desired trajectory can not directly be utilized for feedback control,a mathematical models based on the extended Kalman filter (EKF),neural network (NN) and memory reasoning (MR) for reconstructing the uncertain desired trajectory are proposed, which facilitates the control design and stability analysis. A barrier Lyapunov function based design technique is used to ensure that the inputs to the neural network remain within the compact set such that the neural network unit maintains its learning/approximating functionality during the entire process of system operation.The study of this project is expected to produce a series of innovative results,which not only have important signifigence of enriching tracking control theory,but also have potential for applications in missile interception, aerospace vehicle flight control,etc.