Remarks on motion control of nonholonomic system (falling cat) by using a quantum neural controller

This paper investigates the application of quantum neural networks to control nonholonomic systems. In this study, the falling cat is treated as an example of a control object. The cat is modelled as two similar cylindrical rigid bodies that represent the front and rear half of its body and the motion of the falling cat is formulated by considering the law of conservation of angular momentum. Because of nonholonomic constraints, the motion control of the falling cat can be treated as a symmetrically affine system without a drift term. A quantum neural controller in which the quantum neural network uses qubit neurons as the information processing unit is designed to solve the control problem that brings the nonholonomic system from the initial to desired position. The computational experiments indicate the feasibility and characteristics of the quantum neural controller in dealing with a nonholonomic system.