Iterative learning control of Hamiltonian systems: I/O based optimal control approach

In this note, a novel iterative learning control scheme for a class of Hamiltonian control systems is proposed, which is applicable to electromechanical systems. The proposed method has the following distinguished features. This method does not require either the precise knowledge of the model of the target system or the time derivatives of the output signals. Despite the lack of information, the tracking error monotonously decreases in L₂ sense and, further, perfect tracking is achieved when it is applied to mechanical systems. The self-adjoint related properties of Hamiltonian systems proven in this note play the key role in this learning control. Those properties are also useful for general optimal control. Furthermore, experiments of a robot manipulator demonstrate the effectiveness of the proposed method.