Energy optimal control in mobile sensor networks using hybrid systems theory

Abstract

This paper studies optimal control in a sensor network system consisting of mobile robots to minimize the overall energy consumption of the whole network. With communication energy cost and mobility energy cost taken into consideration, the problem is formulated as an optimal control of a hybrid system, which is solved by switched Linear Quadratic Regulator (LQR). Though switched LQR obtains globally optimal solution, the computational complexity is too high to implement the algorithm when the control horizon expands. For this reason, we resort to a switched system version of Receding Horizon Control (RHC), which is stable and provides a solution close to the optimal one. Finally, in order to attenuate the complexity due to the large networked system, the centralized RHC is modified into a distributed algorithm, which converges to a solution that can approximate the optimizer quite well as verified by simulations.Index Terms-Mobile ad hoc network, energy optimization, optimal control, hybrid control systems, distributed algorithm.

Minimizing mobility and communication energy in robotic networks: An optimal control approach

“…Most of these papers either ignore the cost of energy needed for mobility of the sensors, or assume unlimited sources of such energy. However, since the pioneering work of Goldenberg et al [5], the question of balancing the energy costs of mobility and communication has attracted considerable attention; see, e.g., [9], [14], [15], [12], [6] and references therein.…”

“…That paper uses graph-theoretic techniques to compute an optimal strategy comprised of the sequential scheduling of motion followed by transmission. Reference [15] addresses the combinedenergy minimization problem through the dynamic setting of optimal control of the agents' trajectories. Having a quadratic cost function the problem is cast in the framework of LQR, where complexity reduction is obtained first via modelpredictive control and then by having a distributed algorithm.…”

“…This paper also considers the total energy-minimization problem in the dynamic setting of optimal control, where the agents carry out their communication tasks while in motion. It is different from [15] in that it assumes a general, convex quadratic energy function and hence does not fall in the category of LQR. Furthermore, it does not use an existing algorithm but rather develops a new computational technique.…”