Visible to the public Control Theory

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Control Theory

According to Wikipedia, "Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behavior of dynamical systems with inputs, and how their behavior is modified by feedback." In cyber security, control theory offers methods and approaches to potentially solve hard problems. The articles cited here look at both theory and applications and were presented in the first half of 2014.

  • Spyridopoulos, Theodoros; Maraslis, Konstantinos; Tryfonas, Theo; Oikonomou, George; Li, Shancang, "Managing Cyber Security Risks In Industrial Control Systems With Game Theory And Viable System Modelling," System of Systems Engineering (SOSE), 2014 9th International Conference on, pp.266,271, 9-13 June 2014. doi: 10.1109/SYSOSE .2014.6892499 Cyber security risk management in Industrial Control Systems has been a challenging problem for both practitioners and the research community. Their proprietary nature along with the complexity of those systems renders traditional approaches rather insufficient and creating the need for the adoption of a holistic point of view. This paper draws upon the principles of the Viable System Model and Game Theory in order to present a novel systemic approach towards cyber security management in this field, taking into account the complex inter-dependencies and providing cost-efficient defence solutions.
    Keywords: Airports; Computer security; Game theory; Games; Industrial control; Risk management; asset evaluation; game theory; industrial control systems; risk management; viable system model (ID#:14-2745)
  • Kumar, P.; Singh, AK.; Kummari, N.K., "P-Q Theory Based Modified Control Algorithm For Load Compensating Using DSTATCOM," Harmonics and Quality of Power (ICHQP), 2014 IEEE 16th International Conference on, pp.591,595, 25-28 May 2014. doi: 10.1109/ICHQP.2014.6842810 This paper proposes a control algorithm for DSTATCOM (Distributed STATic COMpensator) to compensate the source current harmonics in a non-sinusoidal voltage source environment. A 3-leg VSC (voltage source converter) based DSTATCOM is used for the load compensation, on a system consisting balanced 5th harmonic PCC voltages, in 3-phase, 4-wire distribution system. Simulations are performed in MATLAB(r) environment for two load conditions, i.e., (i) a 3-phase non-linear load (NLL), and (ii) a NLL with reactive load. The results show that the proposed modification in the p-q theory control algorithm allows successful harmonic compensation at load side.
    Keywords: compensation; power convertors; static VAr compensators; 3-leg VSC; 3-phase 4-wire distribution system; 3-phase nonlinear load; Matlab environment; NLL; balanced 5th harmonic PCC voltages; control algorithm; distributed static compensator; load compensation; nonsinusoidal voltage source environment ;p-q theory based modified control algorithm; point of common coupling; reactive load; source current harmonic compensation; voltage source converter based DSTATCOM; Harmonic analysis; Power harmonic filters; Reactive power; Rectifiers; Vectors; Voltage control; DSTATCOM; Harmonics; p-q theory (ID#:14-2746)
  • Veremey, Evgeny I, "Computer Technologies Based On Optimization Approach In Control Theory," Computer Technologies in Physical and Engineering Applications (ICCTPEA), 2014 International Conference on, pp.200,201, June 30 2014-July 4 2014. doi: 10.1109/ICCTPEA.2014.6893359 Report is devoted to basic conceptions of computer technologies and systems application in the wide area of control systems and processes investigation and design. A special attention is focused on the ideology of optimization approach connected with the problems of control systems modeling, analysis, and synthesis. Some questions of digital control laws real-time implementation are discussed. Computational algorithms are proposed for optimization problems with no formalized performance indices. The main positions are illustrated by correspondent numerical examples.
    Keywords: (not provided) (ID#:14-2747)
  • Fatemi, M.; Haykin, S., "Cognitive Control: Theory and Application," Access, IEEE, vol.2, pp.698, 710, 2014. doi: 10.1109/ACCESS.2014.2332333 From an engineering point-of-view, cognitive control is inspired by the prefrontal cortex of the human brain; cognitive control may therefore be viewed as the overarching function of a cognitive dynamic system. In this paper, we describe a new way of thinking about cognitive control that embodies two basic components: learning and planning, both of which are based on two notions: 1) two-state model of the environment and the perceptor and 2) perception-action cycle, which is a distinctive characteristic of the cognitive dynamic system. Most importantly, it is shown that the cognitive control learning algorithm is a special form of Bellman's dynamic programming. Distinctive properties of the new algorithm include the following: 1) optimality of performance; 2) algorithmic convergence to optimal policy; and 3) linear law of complexity measured in terms of the number of actions taken by the cognitive controller on the environment. To validate these intrinsic properties of the algorithm, a computational experiment is presented, which involves a cognitive tracking radar that is known to closely mimic the visual brain. The experiment illustrates two different scenarios: 1) the impact of planning on learning curves of the new cognitive controller and 2) comparison of the learning curves of three different controllers, based on dynamic optimization, traditional (Q) -learning, and the new algorithm. The latter two algorithms are based on the two-state model, and they both involve the use of planning.
    Keywords: cognition; computational complexity; dynamic programming; Bellman dynamic programming; Q-learning; algorithmic convergence; cognitive control learning algorithm; cognitive dynamic system; cognitive tracking radar; dynamic optimization; human brain; learning curves; linear complexity law; perception-action cycle; performance optimality; prefrontal cortex; two-state model; visual brain; Brain modeling; Cognition; Complexity theory; Control systems; Dynamic programming; Heuristic algorithms; Perception; Radar tracking; Bayesian filtering; Cognitive dynamic systems; Shannon's entropy; cognitive control; dynamic programming; entropic state; explore/exploit tradeoff ;learning; planning; two-state model (ID#:14-2748)
  • Yin-Lam Chow; Pavone, M., "A Framework For Time-Consistent, Risk-Averse Model Predictive Control: Theory And Algorithms," American Control Conference (ACC), 2014, pp.4204,4211, 4-6 June 2014. doi: 10.1109/ACC.2014.6859437 In this paper we present a framework for risk-averse model predictive control (MPC) of linear systems affected by multiplicative uncertainty. Our key innovation is to consider time-consistent, dynamic risk metrics as objective functions to be minimized. This framework is axiomatically justified in terms of time-consistency of risk preferences, is amenable to dynamic optimization, and is unifying in the sense that it captures a full range of risk assessments from risk-neutral to worst case. Within this framework, we propose and analyze an online risk-averse MPC algorithm that is provably stabilizing. Furthermore, by exploiting the dual representation of time-consistent, dynamic risk metrics, we cast the computation of the MPC control law as a convex optimization problem amenable to implementation on embedded systems. Simulation results are presented and discussed.
    Keywords: convex programming; linear systems; predictive control; risk analysis; stability; uncertain systems; MPC control law; convex optimization problem; dynamic optimization; dynamic risk metrics; linear systems; multiplicative uncertainty; risk preference; risk-averse model predictive control; stability; time-consistent model predictive control; Equations; Markov processes; Mathematical model; Measurement; Predictive control; Random variables; Stability analysis; LMIs; Predictive control for linear systems; Stochastic systems (ID#:14-2749)
  • Ueyama, Y., "Feedback Gain Indicates The Preferred Direction In Optimal Feedback Control Theory," Advanced Motion Control (AMC), 2014 IEEE 13th International Workshop on, pp.651,656, 14-16 March 2014. doi: 10.1109/AMC.2014.6823358 We investigated the role of feedback gain in optimal feedback control (OFC) theory using a neuromotor system. Neural studies have shown that directional tuning, known as the "preferred direction" (PD), is a basic functional property of cell activity in the primary motor cortex (M1). However, it is not clear which directions the M1 codes for, because neural activities can correlate with several directional parameters, such as joint torque and end-point motion. Thus, to examine the computational mechanism in the M1, we modeled the isometric motor task of a musculoskeletal system required to generate the desired joint torque. Then, we computed the optimal feedback gain according to OFC. The feedback gain indicated directional tunings of the joint torque and end-point motion in Cartesian space that were similar to the M1 neuron PDs observed in previous studies. Thus, we suggest that the M1 acts as a feedback gain in OFC.
    Keywords: biocontrol; feedback; neurophysiology; optimal control; biological motor system; central nervous system; directional tuning; end-point motion; isometric motor task; joint torque; musculoskeletal system; neuromotor system; optimal feedback control theory; optimal feedback gain; preferred direction; primary motor cortex; Elbow; Force; Joints; Kalman filters; Muscles; Shoulder; Torque; isometric task; motor control; motor cortex; musculoskeletal systems; population coding (ID#:14-2750)
  • Xiaoliang Zhang; Junqiang Bai, "Aerodynamic Optimization Utilizing Control Theory," Control and Decision Conference (2014 CCDC), The 26th Chinese, pp.1293, 1298, May 31 2014-June 2 2014. doi: 10.1109/CCDC.2014.6852366 This paper presents the method of aerodynamic optimization utilizing control theory, which is also called the adjoint method. The discrete adjoint equations are obtained from an unstructured cell-vortex finite-volume Navier-Stokes solver. The developed adjoint equations solver is verified by comparison of objective sensitivities with finite differences. An aerodynamic optimization system is developed combining the flow solver, adjoint solver, mesh deformation and a gradient-based optimizer. The surface geometry is parameterized using Free Form Deformation (FFD) method and a linear elasticity method is employed for the volume mesh deformation during optimization process. This optimization system is successfully applied to a design case of ONERA M6 transonic wing design.
    Keywords: Navier-Stokes equations; aerodynamics; aerospace components; computational fluid dynamics; design engineering; elasticity; finite difference methods; finite volume methods; gradient methods; mechanical engineering computing; mesh generation; optimisation; transonic flow; vortices; FFD method; ONERA M6 transonic wing design; adjoint equations solver; adjoint method; aerodynamic optimization; computational fluid dynamics; control theory; discrete adjoint equations; finite difference; flow solver; free form deformation method; gradient-based optimizer; linear elasticity method; surface geometry; unstructured cell-vortex finite-volume Navier-Stokes solver; volume mesh deformation; Aerodynamics; Equations; Geometry; Mathematical model; Optimization; Sensitivity; Vectors; Aerodynamic and Adjoint method; Control theory; Optimization (ID#:14-2751)
  • Khanum, S.; Islam, M.M., "An Enhanced Model Of Vertical Handoff Decision Based On Fuzzy Control Theory & User Preference," Electrical Information and Communication Technology (EICT), 2013 International Conference on , vol., no., pp.1,6, 13-15 Feb. 2014. doi: 10.1109/EICT.2014.6777873 With the development of wireless communication technology, various wireless networks will exist with different features in same premises. Heterogeneous networks will be dominant in the next generation wireless networks. In such networks choose the most suitable network for mobile user is one of the key issues. Vertical handoff decision making is one of the most important topics in wireless heterogeneous networks architecture. Here the most significant parameters are considered in vertical handoff decision. The proposed method considered Received signal strength (RSS), Monetary Cost(C), Bandwidth (BW), Battery consumption (BC), Security (S) and Reliability (R). Handoff decision making is divided in two sections. First section calculates system obtained value (SOV) considering RSS, C, BW and BC. SOV is calculated using fuzzy logic theory. Today's mobile user are very intelligent in deciding there desired type of services. User preferred network is choose from user priority list is called User obtained value (UOV). Then handoff decisions are made based on SOV & UOV to select the most appropriate network for the mobile nodes (MNs). Simulation results show that fuzzy control theory & user preference based vertical handoff decision algorithm (VHDA) is able to make accurate handoff decisions, reduce unnecessary handoffs decrease handoff calculation time and decrease the probability of call blocking and dropping.
    Keywords: decision making; fuzzy control; fuzzy set theory; mobile computing; mobility management (mobile radio);probability; telecommunication network reliability; telecommunication security; MC; RSS; SOV; VHDA; bandwidth; battery consumption; decrease call blocking probability; decrease call dropping probability; decrease handoff calculation time; fuzzy control theory; fuzzy logic theory; mobile nodes; monetary cost; next generation wireless networks; received signal strength; reliability; security; system obtained value calculation; unnecessary handoff reduction; user obtained value; user preference; user priority list; vertical handoff decision enhancement model; vertical handoff decision making; wireless communication technology; wireless heterogeneous networks architecture; Bandwidth; Batteries; Communication system security Mobile communication; Vectors; Wireless networks; Bandwidth; Cost; Fuzzy control theory; Heterogeneous networks; Received signal strength; Security and user preference; Vertical handoff (ID#:14-2752)


Articles listed on these pages have been found on publicly available internet pages and are cited with links to those pages. Some of the information included herein has been reprinted with permission from the authors or data repositories. Direct any requests via Email to SoS.Project (at) for removal of the links or modifications to specific citations. Please include the ID# of the specific citation in your correspondence.