Periodic constraint-based control using dynamic wireless sensor scheduling

Abstract

Constraint-based control over wireless sensor networks (WSNs) require control strategies that achieve a desired closed-loop system performance while using minimal network resources. In addition to constraints associated with distributed control, WSNs have limitations on bandwidth, energy consumption, and transmission range. This paper introduces and experimentally evaluates a new receding-horizon approach for performing constraint-based control using a WSN. By leveraging the system controllability, the receding-horizon controller is formulated as a mixed-integer programming problem which, at each time step, simultaneously generates a control sequence and sensor selection schedule such that the desired performance is achieved while minimizing the energy required to perform data acquisition and control. For systems containing many sensors, a multi-step state estimator is employed to implement the receding-horizon controller using a conservative abstraction-relaxation approach that simplifies the original mixed-integer programming problem into a convex quadratic programming problem. A wireless process control test bed consisting of 8 coupled water tanks and 16 wireless sensors are used to experimentally evaluate the receding-horizon controller.