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Abstract-We present a methodology and a software framework for the automatic design exploration of the communication network among sensors, actuators and controllers in building automation systems. Given 1) a set of end-toend latency, throughput and packet error rate constraints between nodes, 2) the building geometry, and 3) a library of communication components together with their performance and cost characterization, a synthesis algorithm produces a network implementation that satisfies all end-to-end constraints and that is optimal with respect to installation and maintenance cost. The methodology is applied to the synthesis of wireless networks for an essential step in any control algorithm in a distributed environment: the estimation of control variables such as temperature and air-flow in buildings.

In this paper, an improved approach for the solution of the regulator problem for linear discrete-time dynamical systems with non-Gaussian disturbances and quadratic cost functional is proposed. It is known that a sub-optimal recursive control can be derived from the classical LQG solution by substituting the linear filtering part with a quadratic, or in general polynomial, filter. However, we show that when the system is not asymptotically stable the polynomial control does not improve over the classical LQG solution, due to the lack of the internal stability of the polynomial filter. In order to enlarge the class of systems that can be controlled, we propose a new method based on a suitable rewriting of the system by means of an output injection term. We show that this allows to overcome the problem and to design a polynomial optimal controller also for non asymptotically stable systems. Numerical results show the effectiveness of the method.

The problem of maximizing a utility function while limiting the outage probability below an appropriate threshold is investigated. A coded-division multi access wireless network under mixed Nakagami-lognormal fading is considered. Solving such a utility maximization problem is difficult because the problem is non-convex and non-geometric with mixed integer and real decision variables and no explicit functions of the constraints are available. In this paper, three methods for the solution of the utility maximization problem are proposed. By the first method, a simple explicit outage approximation is used and the constraint that rates are integers is relaxed yielding a standard convex programming optimization that can be solved quickly but at the price of a reduced accuracy. The second method uses a more accurate outage approximation, which allows one solving the utility maximization problem by the Lagrange duality for non-convex problems and contraction mapping theory. The third method is a combination of the first and the second one. Numerical results show that the first method performs well for average values of the outage requirements, whereas the second one is always more accurate, but is also more computationally expensive. Finally, the third method gives same accuracy as the second one, but has a lower computational complexity only for a small number of transmitters.Index Terms-Radio power control, CDMA, outage, nonconvex optimization.

A radio power control strategy to achieve maximum throughput for the up-link of CDMA wireless systems with variable spreading factor is investigated. The system model includes slow and fast fading, rake receiver, and multi-access interference caused by users with heterogeneous data sources. The quality of the communication is expressed in terms of outage probability, while the throughput is dened as the sum of the users' transmit rates. The outage probability is accounted for by resorting to a lognormal approximation. A mixed integer-real optimization problem P1, where the objective function is the throughput under outage probability constraints, is investigated. Problem P1 is solved in two steps: rstly, we propose a modied problem P2 to provide feasible solutions, and then the optimal solution is obtained with an efcient branch-and-bound search. Numerical results are presented and discussed to assess the validity of our approach.

This paper proposes an optlmlzatmn-based approach to the robust 7-to~ control problem of uncertain contmuous or dmcrete-tlme linear tzme-mvariant systems with different tzme-varying delays m the state vector and control input of the dynamic equation and controlled output Sufficient delay-dependent conditions are derived for the control stabflmatmn problem, where both the sine of the tzme-varying delay and the raze of its time derivative (m the continuous-time case) play a crucml role for the closed-loop stabflzty with a guaranteed TLc¢ performance index. The solutmns that are proposed for the Hoo control problem are similar or less conservative, when compared to other recent approaches (~)

In this paper we propose a solution to the problems of detecting a generally correlated stochastic output delay sequence of a linear system driven by Gaussian noise. This is the model for uncertain observations resulting from losses in the propagation channel due to fading phenomena or packet dropouts that is common in wireless sensor networks, networked control systems, or remote sensing applications. The solution we propose consists of a nonlinear detector which identifies online the stochastic delay sequence. The solution provided is optimal in the sense that minimizes the probability of error of the delay detector. Finally, a filtering stage fed with the information given by the detector can follow to estimate the state of the system. Numerical simulations show good performance of the proposed method.

This paper addresses a principal problem of in vivo evolution of modular multi-cellular robots. To evolve robot morphologies and controllers in real-space and real-time we need a generic learning mechanism that enables arbitrary modular shapes to obtain a suitable gait quickly after 'birth'. In this study we investigate a reinforcement learning method and conduct simulation experiments using robot morphologies with different size and complexity. The experiments give insights into the online dynamics of gait learning, the distribution of lucky / unlucky runs and their dependence on the size and complexity of the modular robotic organisms.