697,591 research outputs found
An Optimal Query Assignment for Wireless Sensor Networks
A trade-off between two QoS requirements of wireless sensor networks: query
waiting time and validity (age) of the data feeding the queries, is
investigated. We propose a Continuous Time Markov Decision Process with a drift
that trades-off between the two QoS requirements by assigning incoming queries
to the wireless sensor network or to the database. To compute an optimal
assignment policy, we argue, by means of non-standard uniformization, a
discrete time Markov decision process, stochastically equivalent to the initial
continuous process. We determine an optimal query assignment policy for the
discrete time process by means of dynamic programming. Next, we assess
numerically the performance of the optimal policy and show that it outperforms
in terms of average assignment costs three other heuristics, commonly used in
practice. Lastly, the optimality of the our model is confirmed also in the case
of real query traffic, where our proposed policy achieves significant cost
savings compared to the heuristics.Comment: 27 pages, 20 figure
A Novel Learning Based Model Predictive Control Strategy for Plug-in Hybrid Electric Vehicle
The multi-source electromechanical coupling renders energy management of plug-in hybrid electric vehicles (PHEVs) highly nonlinear and complex. Furthermore, the complicated nonlinear management process highly depends on knowledge of driving conditions, and hinders the control strategies efficiently applied instantaneously, leading to massive challenges in energy saving improvement of PHEVs. To address these issues, a novel learning based model predictive control (LMPC) strategy is developed for a serial-parallel PHEV with the reinforced optimal control effect in real time application. Rather than employing the velocity-prediction based MPC methods favored in the literature, an original reference-tracking based MPC solution is proposed with strong instant application capacity. To guarantee the optimal control effect, an online learning process is implemented in MPC via the Gaussian process (GP) model to address the uncertainties during state estimation. The tracking reference in LMPC based control problem in PHEV is achieved by a microscopic traffic flow analysis (MTFA) method. The simulation results validate that the proposed method can optimally manage energy flow within vehicle power sources in real time, highlighting its anticipated preferable performance
Adaptive Horizon Model Predictive Control and Al'brekht's Method
A standard way of finding a feedback law that stabilizes a control system to
an operating point is to recast the problem as an infinite horizon optimal
control problem. If the optimal cost and the optmal feedback can be found on a
large domain around the operating point then a Lyapunov argument can be used to
verify the asymptotic stability of the closed loop dynamics. The problem with
this approach is that is usually very difficult to find the optimal cost and
the optmal feedback on a large domain for nonlinear problems with or without
constraints. Hence the increasing interest in Model Predictive Control (MPC).
In standard MPC a finite horizon optimal control problem is solved in real time
but just at the current state, the first control action is implimented, the
system evolves one time step and the process is repeated. A terminal cost and
terminal feedback found by Al'brekht's methoddefined in a neighborhood of the
operating point is used to shorten the horizon and thereby make the nonlinear
programs easier to solve because they have less decision variables. Adaptive
Horizon Model Predictive Control (AHMPC) is a scheme for varying the horizon
length of Model Predictive Control (MPC) as needed. Its goal is to achieve
stabilization with horizons as small as possible so that MPC methods can be
used on faster and/or more complicated dynamic processes.Comment: arXiv admin note: text overlap with arXiv:1602.0861
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