We introduce optimal energy shaping as an enhancement of classical passivity-based control methods. A promising feature of passivity theory, alongside stability, has traditionally been claimed to be intuitive performance tuning along the execution of a given task. However, a systematic approach to adjust performance within a passive control framework has yet to be developed, as each method relies on few and problem-specific practical insights. Here, we cast the classic energy-shaping control design process in an optimal control framework; once a task-dependent performance metric is defined, an optimal solution is systematically obtained through an iterative procedure relying on neural networks and gradient-based optimization. The proposed method is validated on state-regulation tasks.
翻译:我们引入了最佳能源塑造,以此加强传统的被动控制方法。被动理论与稳定一样,传统上都声称,被动理论的一个大有希望的特征是在执行特定任务时进行直觉的性能调整。然而,在被动控制框架内调整性能的系统方法尚未制定,因为每种方法都依赖很少的、针对具体问题的实际见解。在这里,我们把典型的节能控制设计程序置于一个最佳控制框架之中;一旦确定了依赖任务的性能衡量标准,通过依赖神经网络和梯度优化的迭接程序,系统地获得最佳解决方案。拟议的方法在国家监管任务上得到验证。