A bridge between control and signal processing

What is it about?

A control theoretic spline can be explained as, for example, drawing a spline curve with a robot hand. It can take account of the dynamical properties of the robot for determining the parameters to represent the spline curve. This method is useful not only for robots but also IoT (Internet of Things) or M2M (Machine to Machine) systems where dynamical systems are to connected networks through the Internet. In such systems, estimation and control of dynamical systems through the Internet is a fundamental issue, which can be effectively solved by this method. To solve the spline problem, this paper gives an efficient numerical computation algorithm for computing parameters of control theoretic splines in the case of monotonicity constraint.

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Why is it important?

As described above, the method is useful for solving estimation and control problems in IoT (Internet of Things) or M2M (Machine to Machine) systems, which is one of the most important issues in recent engineering. The numerical algorithm is described in quadratic programming, which can be efficiently solved via numerical optimization softwares. We can easily add constraints that can be described in convex equalities/inequalities, such as monotonicity constraint.