Optimal Control with Adaptive Internal Dynamics Models
Optimal Control with Adaptive Internal Dynamics Models
复制标题
自适应内部动力学模型的最优控制
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
S. Vijayakumar
中科院分区：
文献类型：
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作者：
Djordje Mitrović;Stefan Klanke;S. Vijayakumar
最优反馈控制是拟人机械臂系统中一种有吸引力的运动生成策略。对于具有非线性动态和非二次成本的系统，可以通过迭代方法（例如迭代线性二次高斯（iLQG）算法）来找到最优反馈控制律。到目前为止，该框架依赖于系统动力学的分析形式，其通常可能是未知的，对于更现实的控制系统难以估计，或者可能受到频繁的系统变化的影响。在本文中，我们提出了一种新的组合学习的前向动力学模型的iLQG框架内。利用这种自适应内部模型可以在线方式补偿受控系统的复杂动态扰动。引入的特定自适应框架有助于在不牺牲控制精度的情况下计算更有效地实现iLQG优化-允许该方法扩展到大型DoF系统。
Optimal feedback control has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. The optimal feedback control law for systems with non-linear dynamics and non-quadratic costs can be found by iterative methods, such as the iterative Linear Quadratic Gaussian (iLQG) algorithm. So far this framework relied on an analytic form of the system dynamics, which may often be unknown, difficult to estimate for more realisti c control systems or may be subject to frequent systematic changes. In this paper, we present a novel combination of learning a forward dynamics model within the iLQG framework. Utilising such adaptive internal models can compensate for complex dynamic perturbations of the controlled system in an online fashion . The specific adaptive framework introduced lends itself to a computationally more efficient implementation o f the iLQG optimisation without sacrificing control accuracy - allowing the method to scale to large DoF systems.