Optimal co-design of control, scheduling and routing in multi-hop control networks

A Multi-hop Control Network consists of a plant where the communication between sensors, actuators and computational units is supported by a (wireless) multi-hop communication network, and data flow is performed using scheduling and routing of sensing and actuation data. Given a SISO LTI plant, we will address the problem of co-designing a digital controller and the network parameters (scheduling and routing) in order to guarantee stability and maximize a performance metric on the transient response to a step input, with constraints on the control effort, on the output overshoot and on the bandwidth of the communication channel. We show that the above optimization problem is a polynomial optimization problem, which is generally NP-hard. We provide sufficient conditions on the network topology, scheduling and routing such that it is computationally feasible, namely such that it reduces to a convex optimization problem.Comment: 51st IEEE Conference on Decision and Control, 2012. Accepted for publication as regular pape

Sensor Scheduling for Optimal Observability Using Estimation Entropy

We consider sensor scheduling as the optimal observability problem for partially observable Markov decision processes (POMDP). This model fits to the cases where a Markov process is observed by a single sensor which needs to be dynamically adjusted or by a set of sensors which are selected one at a time in a way that maximizes the information acquisition from the process. Similar to conventional POMDP problems, in this model the control action is based on all past measurements; however here this action is not for the control of state process, which is autonomous, but it is for influencing the measurement of that process. This POMDP is a controlled version of the hidden Markov process, and we show that its optimal observability problem can be formulated as an average cost Markov decision process (MDP) scheduling problem. In this problem, a policy is a rule for selecting sensors or adjusting the measuring device based on the measurement history. Given a policy, we can evaluate the estimation entropy for the joint state-measurement processes which inversely measures the observability of state process for that policy. Considering estimation entropy as the cost of a policy, we show that the problem of finding optimal policy is equivalent to an average cost MDP scheduling problem where the cost function is the entropy function over the belief space. This allows the application of the policy iteration algorithm for finding the policy achieving minimum estimation entropy, thus optimum observability.Comment: 5 pages, submitted to 2007 IEEE PerCom/PerSeNS conferenc

Saliency Based Control in Random Feature Networks

The ability to rapidly focus attention and react to salient environmental features enables animals to move agiley through their habitats. To replicate this kind of high-performance control of movement in synthetic systems, we propose a new approach to feedback control that bases control actions on randomly perceived features. Connections will be made with recent work incorporating communication protocols into networked control systems. The concepts of {\em random channel controllability} and {\em random channel observability} for LTI control systems are introduced and studied.Comment: 9 pages, 2 figure

Multiple input control strategies for robust and adaptive climate engineering in a low order 3-box model

A low-order 3-box energy balance model for the climate system is employed with a multivariable control scheme for the evaluation of new robust and adaptive climate engineering strategies using solar radiation management. The climate engineering measures are deployed in three boxes thus representing northern, southern and central bands. It is shown that, through heat transport between the boxes, it is possible to effect a degree of latitudinal control through the reduction of insolation. The approach employed consists of a closed-loop system with an adaptive controller, where the required control intervention is estimated under the RCP4.5 radiative scenario. Through the online estimation of the controller parameters, adaptive control can overcome key issues related to uncertainties of the climate model, the external radiative forcing and the dynamics of the actuator used. In fact, the use of adaptive control offers a robust means of dealing with unforeseeable abrupt perturbations, as well as the parametrization of the model considered, to counteract the RCP4.5 scenario, while still providing bounds on stability and control performance. Moreover, applying multivariable control theory also allows the formal controllability and observability of the system to be investigated in order to identify all feasible control strategies

Pilot modeling, modal analysis, and control of large flexible aircraft

The issues to be addressed are threefold. The first deals with the question of whether dynamic aeroelastic effects can significantly impact piloted flight dynamics. For example, if one were to explore this problem experimentally, what mathematical model would be appropriate to use in the simulation? What modes, for example, should be included in the simulation, or what linear model should be used in the control synthesis? The second question deals with the appropriate design criteria or design objectives. In the case of active control, for example, what would be the design objectives for the control synthesis if aeroelastic effects are a problem? The outline of the topics includes a description of a model analysis methodology aimed at answering the question of the significance of higher order dynamics. Secondly, a pilot vehicle analysis of some experimental data addresses the question of ""What's important in the task?'' The experimental data will be presented briefly, followed by the results of an open-loop modal analysis of the generic vehicle configurations in question. Finally, one of the vehicles will be augmented via active control and the results presented

Model reduction results for flexible space structures

This paper describes the novel subsystem balancing technique for obtaining reduced-order models of flexible structures, and investigates its properties fully. This method can be regarded as a combination of the best features of modal truncation (efficiency) and internal balancing (accuracy); it is particularly well suited to the typical practical case of structures which possess clusters of close modes. Numerical results are then presented demonstrating the results obtained by applying subsystem balancing to the Air Force Phillips Laboratory ASTREX testbed, the Jet Propulsion Laboratory antenna facility, and the NASA Marshall Space Flight Center ACES structure