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 review on analysis and synthesis of nonlinear stochastic systems with randomly occurring incomplete information

Copyright q 2012 Hongli Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out. © 2012 Hongli Dong et al.This work was supported in part by the National Natural Science Foundation of China under Grants 61273156, 61134009, 61273201, 61021002, and 61004067, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, the National Science Foundation of the USA under Grant No. HRD-1137732, and the Alexander von Humboldt Foundation of German

Output feedback stable stochastic predictive control with hard control constraints

We present a stochastic predictive controller for discrete time linear time invariant systems under incomplete state information. Our approach is based on a suitable choice of control policies, stability constraints, and employment of a Kalman filter to estimate the states of the system from incomplete and corrupt observations. We demonstrate that this approach yields a computationally tractable problem that should be solved online periodically, and that the resulting closed loop system is mean-square bounded for any positive bound on the control actions. Our results allow one to tackle the largest class of linear time invariant systems known to be amenable to stochastic stabilization under bounded control actions via output feedback stochastic predictive control

Externalities, communication and the allocation of decision rights

This paper views authority as the right to undertake decisions that impose externalities on other members of the organization. When only decision rights can be contractually assigned to one of the organization's stakeholders, the optimal assignment minimizes the resulting inefficiencies by giving control rights to the party with the highest stake in the organization's decisions. Under asymmetric information, the efficient allocation of authority depends on the communication of private information. In the case of multiple decision areas, divided control rights may enhance organizational efficiency unless there exist complementarities between different decisions. --Authority,Decision Rights,Externalities,Incomplete Contracts,Imperfect Information,Theory of the Firm

Robust expectations and uncertain models – A robust control approach with application to the New Keynesian economy

This paper extends Svensson and Woodford’s (2003) partial information framework by allowing the private agents to achieve robustness against incomplete information about the structure of the economy by distorting their expectations in a particular direction. It shows how a linear rational expectations equilibrium under concern for robustness can be solved by exploiting the recursive structure of the problem and appropriately modifying the Bellman equations in their framework. The standard Kalman filter is then used for information updating under imperfect measurement of the state variables. The standard New Keynesian model is used for illustrating how concern for modelling errors interacts with imperfect information. Agents achieve robustness by simultaneously over-estimating the persistence of exogenous shocks, but under-estimating the policy response to the output gap. This under- estimation, combined with imperfect measurement, leads to larger and more persistent responses of private consumption to government expenditure shocks under robust expectations.expectations, robust control, model uncertainty, monetary policy, imperfect information

Robust expectations and uncertain models – A robust control approach with application to the New Keynesian economy

This paper extends Svensson and Woodford’s (2003) partial information framework by allowing the private agents to achieve robustness against incomplete information about the structure of the economy by distorting their expectations in a particular direction. It shows how a linear rational expectations equilibrium under concern for robustness can be solved by exploiting the recursive structure of the problem and appropriately modifying the Bellman equations in their framework. The standard Kalman filter is then used for information updating under imperfect measurement of the state variables. The standard New Keynesian model is used for illustrating how concern for modelling errors interacts with imperfect information. Agents achieve robustness by simultaneously over-estimating the persistence of exogenous shocks, but under-estimating the policy response to the output gap. This under-estimation, combined with imperfect measurement, leads to larger and more persistent responses of private consumption to government expenditure shocks under robust expectations.expectations; robust control; model uncertainty; monetary policy; imperfect information

Reinforcement learning for Order Acceptance on a shared resource

Order acceptance (OA) is one of the main functions in business control. Basically, OA involves for each order a reject/accept decision. Always accepting an order when capacity is available could disable the system to accept more convenient orders in the future with opportunity losses as a consequence. Another important aspect is the availability of information to the decision-maker. We use the stochastic modeling approach, Markov decision theory and learning methods from artificial intelligence to find decision policies, even under uncertain information. Reinforcement learning (RL) is a quite new approach in OA. It is capable of learning both the decision policy and incomplete information, simultaneously. It is shown here that RL works well compared with heuristics. Finding good heuristics in a complex situation is a delicate art. It is demonstrated that a RL trained agent can be used to support the detection of good heuristics

Method for Detecting Anomalous States of a Control Object in Information Systems Based on the Analysis of Temporal Data and Knowledge

The problem of finding the anomalous states of the control object in the management information system under conditions of uncertainty caused by the incompleteness of knowledge about this object is considered. The method of classifying the current state of the control object in real time, allowing to identify the current anomalous state. The method uses temporal data and knowledge. Data is represented by sequences of events with timestamps. Knowledge is represented as weighted temporal rules and constraints. The method includes the following key phases: the formation of sequences of logical facts; selection of temporal rules and constraints; classification based on a comparison of rules and constraints. Logical facts are represented as predicates on event attributes and reflect the state of the control object. Logical rules define valid sequences of logical facts. Performing a classification by successive comparisons of constraints and weights of the rules makes it possible to more effectively identify the anomalous state since the comparison of the constraints reduces the subset of facts comparing to the current state. The method creates conditions for improving management efficiency in the context of incomplete information on the state of a complex object by using logical inference in knowledge bases for anomalous states of such control objects

Where do we stand in the theory of finance? : a selective overview with reference to Erich Gutenberg

For the past 20 years, financial markets research has concerned itself with issues related to the evaluation and management of financial securities in efficient capital markets and with issues of management control in incomplete markets. The following selective overview focuses on key aspects of the theory and empirical experience of management control under conditions of asymmetric information. The objective is examine the validity of the recently advanced hypothesis on the myths of corporate control. The present overview is based on Gutenberg's position that there exists a discrete corporate interest, as distinct from and separate from the interests of the shareholders or other stakeholders. In the third volume of Grundlagen der BWL: Die Finanzen, published in 1969, this position of Gutenberg's is coupled with an appeal for a so-called financial equilibrium to be maintained. Not until recently have models grounded in capital market theory been developed which also allow for a firm's management to exercise autonomy vis-à-vis its stakeholder. This paper was prepared for the Erich Gutenberg centenary conference on December 12 and 13, 1997 in Cologne