Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

A Convex Feasibility Approach to Anytime Model Predictive Control

This paper proposes to decouple performance optimization and enforcement of asymptotic convergence in Model Predictive Control (MPC) so that convergence to a given terminal set is achieved independently of how much performance is optimized at each sampling step. By embedding an explicit decreasing condition in the MPC constraints and thanks to a novel and very easy-to-implement convex feasibility solver proposed in the paper, it is possible to run an outer performance optimization algorithm on top of the feasibility solver and optimize for an amount of time that depends on the available CPU resources within the current sampling step (possibly going open-loop at a given sampling step in the extreme case no resources are available) and still guarantee convergence to the terminal set. While the MPC setup and the solver proposed in the paper can deal with quite general classes of functions, we highlight the synthesis method and show numerical results in case of linear MPC and ellipsoidal and polyhedral terminal sets.Comment: 8 page

Networked and event-triggered control systems

In this thesis, control algorithms are studied that are tailored for platforms with limited computation and communication resources. The interest in such control algorithms is motivated by the fact that nowadays control algorithms are implemented on small and inexpensive embedded microprocessors and that the sensors, actuators and controllers are connected through multipurpose communication networks. To handle the fact that computation power is no longer abundant and that communication networks do not have in finite bandwidth, the control algorithms need to be either robust for the deficiencies induced by these constraints, or they need to optimally utilise the available computation and communication resources. In this thesis, methodologies for the design and analysis of control algorithms with such properties are developed. Networked Control Systems: In the first part of the thesis, so-called networked control systems (NCSs) are studied. The control algorithms studied in this part of the thesis can be seen as conventional sampled-data controllers that need to be robust against the artefacts introduced by using a finite bandwidth communication channel. The network-induced phenomena that are considered in this thesis are time-varying transmission intervals, time-varying delays, packet dropouts and communication constraints. The latter phenomenon causes that not all sensor and actuator data can be transmitted simultaneously and, therefore, a scheduling protocol is needed to orchestrate when to transmit what data over the network. To analyse the stability of the NCSs, a discrete-time modelling framework is presented and, in particular, two cases are considered: in the first case, the transmission intervals and delays are assumed to be upper and lower bounded, and in the second case, they are described by a random process, satisfying a continuous joint probability distribution. Both cases are relevant. The former case requires a less detailed description of the network behaviour than the latter case, while the latter results in a less conservative stability analysis than the former. This allows to make a tradeoff between modelling accuracy (of network-induced effects) and conservatism in the stability analysis. In both cases, linear plants and controllers are considered and the NCS is modelled as a discrete-time switched linear parameter-varying system. To assess the stability of this system, novel polytopic overapproximations are developed, which allows the stability of the NCS to be studied using a finite number of linear matrix inequalities. It will be shown that this approach reduces conservatism significantly with respect to existing results in the literature and allows for studying larger classes of controllers, including discrete-time dynamical output-based controllers. Hence, the main contribution of this part of the thesis is the development of a new and general framework to analyse the stability of NCSs subject to four network-induced phenomena in a hardly conservative manner. Event-Triggered Control Systems: In the second part of the thesis, socalled event-triggered control (ETC) systems are studied. ETC is a control strategy in which the control task is executed after the occurrence of an external event, rather than the elapse of a certain period of time as in conventional periodic control. In this way, ETC can be designed to only provide control updates when needed and, thereby, to optimally utilise the available computation and communication resources. This part of the thesis consists of three main contributions in this appealing area of research. The first contribution is the extension of the existing results on ETC towards dynamical output-based feedback controllers, instead of state-feedback control, as is common in the majority of the literature on ETC. Furthermore, extensions towards decentralised event triggering are presented. These extensions are important for practical implementations of ETC, as in many control applications the full state is hardly ever available for feedback, and sensors and actuators are often physically distributed, which prohibits the use of centralised event-triggering conditions. To study the stability and the L1-performance of this ETC system, a modelling framework based on impulsive systems is developed. Furthermore, for the novel output-based decentralised event-triggering conditions that are proposed, it is shown how nonzero lower bounds on the minimum inter-event times can be guaranteed and how they can be computed. The second contribution is the proposition of the new class of periodic event-triggered control (PETC) algorithms, where the objective is to combine the benefits that, on the one hand, periodic control and, on the other hand, ETC offer. In PETC, the event-triggering condition is monitored periodically and at each sampling instant it is decided whether or not to transmit the data and to use computation resources for the control task. Such an event-triggering condition has several benefits, including the inherent existence of a minimum inter-event time, which can be tuned directly. Furthermore, the fact that the event-triggering condition is only verified at the periodic sampling times, instead of continuously, makes it possible to implement this strategy in standard time-sliced embedded software architectures. To analyse the stability and the L2-performance for these PETC systems, methodologies based on piecewiselinear systems models and impulsive system models will be provided, leading to an effective analysis framework for PETC. Finally, a novel approach to solving the codesign problem of both the feedback control algorithm and the event-triggering condition is presented. In particular, a novel way to solve the minimum attention and anytime attention control problems is proposed. In minimum attention control, the `attention' that a control task requires is minimised, and in anytime attention control, the performance under the `attention' given by a scheduler is maximised. In this context, `attention' is interpreted as the inverse of the time elapsed between two consecutive executions of a control task. The two control problems are solved by formulating them as linear programs, which can be solved efficiently in an online fashion. This offers a new and elegant way to solve both the minimum attention control problem and the anytime attention control problem in one unifying framework. The contributions presented in this thesis can form a basis for future research explorations that can eventually lead to a mature system theory for both NCSs and ETC systems, which are indispensable for the deployment of NCSs and ETC systems in a large variety of practical control applications

Incremental Sampling-based Algorithms for Optimal Motion Planning

During the last decade, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs) have been shown to work well in practice and to possess theoretical guarantees such as probabilistic completeness. However, no theoretical bounds on the quality of the solution obtained by these algorithms have been established so far. The first contribution of this paper is a negative result: it is proven that, under mild technical conditions, the cost of the best path in the RRT converges almost surely to a non-optimal value. Second, a new algorithm is considered, called the Rapidly-exploring Random Graph (RRG), and it is shown that the cost of the best path in the RRG converges to the optimum almost surely. Third, a tree version of RRG is introduced, called the RRT$^*$ algorithm, which preserves the asymptotic optimality of RRG while maintaining a tree structure like RRT. The analysis of the new algorithms hinges on novel connections between sampling-based motion planning algorithms and the theory of random geometric graphs. In terms of computational complexity, it is shown that the number of simple operations required by both the RRG and RRT$^*$ algorithms is asymptotically within a constant factor of that required by RRT.Comment: 20 pages, 10 figures, this manuscript is submitted to the International Journal of Robotics Research, a short version is to appear at the 2010 Robotics: Science and Systems Conference

Motion Planning for Optimal Information Gathering in Opportunistic Navigation Systems

Motion planning for optimal information gathering in an opportunistic navigation (OpNav) environment is considered. An OpNav environment can be thought of as a radio frequency signal landscape within which a receiver locates itself in space and time by extracting information from ambient signals of opportunity (SOPs). The receiver is assumed to draw only pseudorange-type observations from the SOPs, and such observations are fused through an estimator to produce an estimate of the receiver’s own states. Since not all SOP states in the OpNav environment may be known a priori, the receiver must estimate the unknown SOP states of interest simultaneously with its own states. In this work, the following problem is studied. A receiver with no a priori knowledge about its own states is dropped in an unknown, yet observable, OpNav environment. Assuming that the receiver can prescribe its own trajectory, what motion planning strategy should the receiver adopt in order to build a high-fidelity map of the OpNav signal landscape, while simultaneously localizing itself within this map in space and time? To answer this question, first, the minimum conditions under which the OpNav environment is fully observable are established, and the need for receiver maneuvering to achieve full observability is highlighted. Then, motivated by the fact that not all trajectories a receiver may take in the environment are equally beneficial from an information gathering point of view, a strategy for planning the motion of the receiver is proposed. The strategy is formulated in a coupled estimation and optimal control framework of a gradually identified system, where optimality is defined through various information-theoretic measures. Simulation results are presented to illustrate the improvements gained from adopting the proposed strategy over random and pre-defined receiver trajectories.Aerospace Engineering and Engineering Mechanic