260 results match your criteria problem control-theory


Reinforcement Learning Control of Robotic Knee With Human-in-the-Loop by Flexible Policy Iteration.

IEEE Trans Neural Netw Learn Syst 2021 May 6;PP. Epub 2021 May 6.

We are motivated by the real challenges presented in a human-robot system to develop new designs that are efficient at data level and with performance guarantees, such as stability and optimality at system level. Existing approximate/adaptive dynamic programming (ADP) results that consider system performance theoretically are not readily providing practically useful learning control algorithms for this problem, and reinforcement learning (RL) algorithms that address the issue of data efficiency usually do not have performance guarantees for the controlled system. This study fills these important voids by introducing innovative features to the policy iteration algorithm. Read More

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Adaptive robust dynamic surface asymptotic tracking for uncertain strict-feedback nonlinear systems with unknown control direction.

ISA Trans 2021 Apr 13. Epub 2021 Apr 13.

Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing 100074, China. Electronic address:

For strict-feedback nonlinear systems (SFNSs) with unknown control direction, this paper synthesizes an asymptotic tracking controller by a combination of the dynamic surface control (DSC) technique, the Nussbaum gain technique (NGT) and fuzzy logic systems (FLSs). The SFNSs under study feature unknown nonlinear uncertainties and external disturbances. By utilizing the DSC technique with nonlinear filters, the issue of 'differential explosion' is obviated, in which the adaptive laws are constructed to conquer the effect of unknown functions. Read More

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Cancer immune control dynamics: a clinical data driven model of systemic immunity in patients with metastatic melanoma.

BMC Bioinformatics 2021 Apr 16;22(1):197. Epub 2021 Apr 16.

Department of Medical Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.

Background: Recent clinical advances in cancer immuno-therapeutics underscore the need for improved understanding of the complex relationship between cancer and the multiple, multi-functional, inter-dependent, cellular and humoral mediators/regulators of the human immune system. This interdisciplinary effort exploits engineering analysis methods utilized to investigate anomalous physical system behaviors to explore immune system behaviors. Cancer Immune Control Dynamics (CICD), a systems analysis approach, attempts to identify differences between systemic immune homeostasis of 27 healthy volunteers versus 14 patients with metastatic malignant melanoma based on daily serial measurements of conventional peripheral blood biomarkers (15 cell subsets, 35 cytokines). Read More

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Solving Bilevel Optimization Problems Using Kriging Approximations.

IEEE Trans Cybern 2021 Mar 22;PP. Epub 2021 Mar 22.

Bilevel optimization involves two levels of optimization, where one optimization problem is nested within the other. The structure of the problem often requires solving a large number of inner optimization problems that make these kinds of optimization problems expensive to solve. The reaction set mapping and the lower level optimal value function mapping are often used to reduce bilevel optimization problems to a single level; however, the mappings are not known a priori, and the need is to be estimated. Read More

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Spectral State Compression of Markov Processes.

IEEE Trans Inf Theory 2020 May 29;66(5):3202-3231. Epub 2019 Nov 29.

Center for Statistics and Machine Learning, Princeton University, Princeton, NJ 08540.

Model reduction of Markov processes is a basic problem in modeling state-transition systems. Motivated by the state aggregation approach rooted in control theory, we study the statistical state compression of a discrete-state Markov chain from empirical trajectories. Through the lens of spectral decomposition, we study the rank and features of Markov processes, as well as properties like representability, aggregability, and lumpability. Read More

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An extended state observer based U-model control of the COVID-19.

ISA Trans 2021 Feb 25. Epub 2021 Feb 25.

Department of Engineering Design and Mathematics, University of the West of England, Bristol, UK.

The coronavirus disease 2019 (COVID-19) is a new, rapidly spreading and evolving pandemic around the world. The COVID-19 has seriously affected people's health or even threaten people's life. In order to contain the spread of the pandemic and minimize its impact on economy, the tried-and-true control theory is utilized. Read More

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February 2021

Optimal Protocols in Quantum Annealing and Quantum Approximate Optimization Algorithm Problems.

Phys Rev Lett 2021 Feb;126(7):070505

Joint Center for Quantum Information and Computer Science, NIST/University of Maryland, College Park, Maryland 20742, USA.

Quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA) are two special cases of the following control problem: apply a combination of two Hamiltonians to minimize the energy of a quantum state. Which is more effective has remained unclear. Here we analytically apply the framework of optimal control theory to show that generically, given a fixed amount of time, the optimal procedure has the pulsed (or "bang-bang") structure of QAOA at the beginning and end but can have a smooth annealing structure in between. Read More

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February 2021

Guaranteed Reachability for Systems with Unknown Dynamics.

Authors:
Melkior Ornik

Proc IEEE Conf Decis Control 2021 Jan;2020

Department of Aerospace Engineering and the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.

The problem of computing the reachable set for a given system is a quintessential question in nonlinear control theory. Motivated by prior work on safety-critical online planning, this paper considers an environment where the only available information about system dynamics is that of dynamics at a single point. Limited to such knowledge, we study the problem of describing the set of all states that are guaranteed to be reachable regardless of the unknown true dynamics. Read More

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January 2021

Distributed Group Coordination of Multiagent Systems in Cloud Computing Systems Using a Model-Free Adaptive Predictive Control Strategy.

IEEE Trans Neural Netw Learn Syst 2021 Feb 2;PP. Epub 2021 Feb 2.

This article studies the group coordinated control problem for distributed nonlinear multiagent systems (MASs) with unknown dynamics. Cloud computing systems are employed to divide agents into groups and establish networked distributed multigroup-agent systems (ND-MGASs). To achieve the coordination of all agents and actively compensate for communication network delays, a novel networked model-free adaptive predictive control (NMFAPC) strategy combining networked predictive control theory with model-free adaptive control method is proposed. Read More

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February 2021

A Brief Survey of Telerobotic Time Delay Mitigation.

Front Robot AI 2020 15;7:578805. Epub 2020 Dec 15.

Electrical and Computer Engineering, Wayne State University, Detroit, MI, United States.

There is a substantial number of telerobotics and teleoperation applications ranging from space operations, ground/aerial robotics, drive-by-wire systems to medical interventions. Major obstacles for such applications include latency, channel corruptions, and bandwidth which limit teleoperation efficacy. This survey reviews the time delay problem in teleoperation systems. Read More

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December 2020

The promise of dawn: Microalgae photoacclimation as an optimal control problem of resource allocation.

J Theor Biol 2021 04 18;515:110597. Epub 2021 Jan 18.

Avignon Université, Laboratoire de Mathématiques d'Avignon (EA 2151), F-84018, France. Electronic address:

Photosynthetic microorganisms are known to adjust their photosynthetic capacity according to light intensity. This so-called photoacclimation process is thought to maximize growth at equilibrium, but its dynamics under varying conditions remains less understood. To tackle this problem, microalgae growth and photoacclimation are represented by a (coarse-grained) resource allocation model. Read More

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Multi-Fading Factor and Updated Monitoring Strategy Adaptive Kalman Filter-Based Variational Bayesian.

Sensors (Basel) 2020 Dec 30;21(1). Epub 2020 Dec 30.

College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.

Aiming at the problem that the performance of adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of multi-fading factor and an updated monitoring strategy adaptive Kalman filter-based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model and the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the updated monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. Read More

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December 2020

The 4Ds of Dealing With Distress - Distract, Dilute, Develop, and Discover: An Ultra-Brief Intervention for Occupational and Academic Stress.

Front Psychol 2020 16;11:611156. Epub 2020 Dec 16.

Beyond Psychology, Rochdale, United Kingdom.

The Covid-19 crisis has clarified the demand for an ultra-brief single-session, online, theory-led, empirically supported, psychological intervention for managing stress and improving well-being, especially for people within organizational settings. We designed and delivered "4Ds for Dealing with Distress" during the crisis to address this need. 4Ds unifies a spectrum of familiar emotion regulation strategies, resilience exercises, and problem-solving approaches using perceptual control theory and distils them into a simple four-component rubric (Distract-Dilute-Develop-Discover). Read More

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December 2020

A 6-point subdivision scheme and its applications for the solution of 2nd order nonlinear singularly perturbed boundary value problems.

Math Biosci Eng 2020 09;17(6):6659-6677

Department of Law, Economics and Human Sciences & Decisions Lab, University Mediterranea of Reggio Calabria, Reggio Calabria, Italy.

In this paper, we first present a 6-point binary interpolating subdivision scheme (BISS) which produces a continuous curve and 4th order of approximation. Then as an application of the scheme, we develop an iterative algorithm for the solution of 2nd order nonlinear singularly per-turbed boundary value problems (NSPBVP). The convergence of an iterative algorithm has also been presented. Read More

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September 2020

Output-Based Event-Triggered Cooperative Robust Regulation for Constrained Heterogeneous Multiagent Systems.

IEEE Trans Cybern 2020 Dec 30;PP. Epub 2020 Dec 30.

The output-based event-triggered cooperative output regulation problem is addressed for constrained linear heterogeneous multiagent system in this article. In light of the robust control theory, H∞ leader-following consensus with respect to exogenous signals, including both disturbance to be rejected and reference state of leader to be tracked, is guaranteed. Meanwhile, the system performance alleviates degradation through a model recovery anti-windup technique while encountering input saturation. Read More

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December 2020

Efficient Markets and Contingent Claims Valuation: An Information Theoretic Approach.

Authors:
Jussi Lindgren

Entropy (Basel) 2020 Nov 12;22(11). Epub 2020 Nov 12.

Department of Mathematics and Systems Analysis, Aalto University, 02150 Espoo, Finland.

This research article shows how the pricing of derivative securities can be seen from the context of stochastic optimal control theory and information theory. The financial market is seen as an information processing system, which optimizes an information functional. An optimization problem is constructed, for which the linearized Hamilton-Jacobi-Bellman equation is the Black-Scholes pricing equation for financial derivatives. Read More

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November 2020

Coupling the modeling of phage-bacteria interaction and cholera epidemiological model with and without optimal control.

J Theor Biol 2021 03 13;512:110537. Epub 2020 Nov 13.

Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria 0002, South Africa.

In this work, we assess the impact of the phage-bacteria infection and optimal control on the indirectly transmitted cholera disease. The phage-bacteria interactions are described by predator-prey system using the Smith functional response, which takes into account the number of bacteria binding sites. The study is done in two steps, namely the model without control and the model with control. Read More

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Nearest-neighbor NMR spectroscopy: categorizing spectral peaks by their adjacent nuclei.

Nat Commun 2020 11 3;11(1):5547. Epub 2020 Nov 3.

Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, 02115, USA.

Methyl-NMR enables atomic-resolution studies of structure and dynamics of large proteins in solution. However, resonance assignment remains challenging. The problem is to combine existing structural informational with sparse distance restraints and search for the most compatible assignment among the permutations. Read More

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November 2020

Design of personalized cancer treatments by use of optimal control problems: The case of chronic myeloid leukemia.

Math Biosci Eng 2020 07;17(5):4773-4800

The Irma H. Russo, MD Breast Cancer Research Laboratory, Fox Chase Cancer Center, Philadelphia, USA.

The advances in the mathematical explanation of the dynamics underlying treated cancer has opened the door to the mathematical design of optimal therapies. In parallel, the improvements and cost reductions in experimentation and data analysis techniques have made the formulation of personalized therapies possible. However, the design of cancer therapies making use of optimal control theory has not fully considered this possibility in detail. Read More

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Design, Validation and Comparison of Path Following Controllers for Autonomous Vehicles.

Sensors (Basel) 2020 Oct 24;20(21). Epub 2020 Oct 24.

School of Automotive Studies, Tongji University, Shanghai 201804, China.

As one of the core issues of autonomous vehicles, vehicle motion control directly affects vehicle safety and user experience. Therefore, it is expected to design a simple, reliable, and robust path following the controller that can handle complex situations. To deal with the longitudinal motion control problem, a speed tracking controller based on sliding mode control with nonlinear conditional integrator is proposed, and its stability is proved by the Lyapunov theory. Read More

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October 2020

Finite-Time Synchronization for Delayed Complex Dynamical Networks With Synchronizing or Desynchronizing Impulses.

IEEE Trans Neural Netw Learn Syst 2020 Oct 20;PP. Epub 2020 Oct 20.

In this article, the finite-time synchronization problem of delayed complex dynamical networks (CDNs) with impulses is studied, where two types of impulses, namely, synchronizing impulses and desynchronizing impulses, are fully considered, respectively. Since the existence of impulses makes the discontinuity of the states, which means that the classical result for finite-time stability is inapplicable in such a case, the key challenge is how to guarantee the finite-time stability and estimate the settling time in impulse sense. We apply impulsive control theory and finite-time stability theory to CDNs and establish some sufficient conditions for finite-time synchronization, where two kinds of memory controllers are designed for synchronizing impulses and desynchronizing impulses, respectively. Read More

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October 2020

Semigroup applications everywhere.

Philos Trans A Math Phys Eng Sci 2020 Nov 19;378(2185):20190610. Epub 2020 Oct 19.

Dipartimento di Ingegneria dell'Informazione, Ingegneria Elettrica e Matematica Applicata, Università degli Studi di Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy.

Most dynamical systems arise from partial differential equations (PDEs) that can be represented as an abstract evolution equation on a suitable state space complemented by an initial or final condition. Thus, the system can be written as a Cauchy problem on an abstract function space with appropriate topological structures. To study the qualitative and quantitative properties of the solutions, the theory of one-parameter operator semigroups is a most powerful tool. Read More

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November 2020

On the effects of memory and topology on the controllability of complex dynamical networks.

Sci Rep 2020 10 15;10(1):17346. Epub 2020 Oct 15.

Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90007, USA.

Recent advances in network science, control theory, and fractional calculus provide us with mathematical tools necessary for modeling and controlling complex dynamical networks (CDNs) that exhibit long-term memory. Selecting the minimum number of driven nodes such that the network is steered to a prescribed state is a key problem to guarantee that complex networks have a desirable behavior. Therefore, in this paper, we study the effects of long-term memory and of the topological properties on the minimum number of driven nodes and the required control energy. Read More

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October 2020

Pareto-Optimal Strategy for Linear Mean-Field Stochastic Systems With H∞ Constraint.

IEEE Trans Cybern 2020 Oct 15;PP. Epub 2020 Oct 15.

This article presents results on designing the Pareto-optimal strategy under H∞ constraint for the linear mean-field stochastic systems disturbed by external disturbances. First, combining the stochastic H∞ control theory with the stochastic mean-field theory, we derive the stochastic bounded real lemma (SBRL) of our considered linear mean-field stochastic systems with the stochastic initial condition. Second, we use the mean-field forward-backward stochastic differential equation to solve the mean-field linear quadratic Pareto-optimal problem with indefinite cost functionals. Read More

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October 2020

Enzyme activities predicted by metabolite concentrations and solvent capacity in the cell.

J R Soc Interface 2020 10 14;17(171):20200656. Epub 2020 Oct 14.

Department of Mathematics, University of California Riverside, Riverside, CA 92505, USA.

Experimental measurements or computational model predictions of the post-translational regulation of enzymes needed in a metabolic pathway is a difficult problem. Consequently, regulation is mostly known only for well-studied reactions of central metabolism in various model organisms. In this study, we use two approaches to predict enzyme regulation policies and investigate the hypothesis that regulation is driven by the need to maintain the solvent capacity in the cell. Read More

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October 2020

Estimating Quality of Reaching Movement Using a Wrist-Worn Inertial Sensor.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:3719-3722

Stroke is a major cause of long-term disability. Because patients recovering from stroke often perform differently in clinical settings than in their naturalistic environments, remote monitoring of motor performance is needed to evaluate the true impact of prescribed therapies. Wearable sensors have been considered as a technical solution to this problem, but most existing systems focus on measuring the amount of movement without considering the quality of movement. Read More

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Mathematical analysis of spread and control of the novel corona virus (COVID-19) in China.

Chaos Solitons Fractals 2020 Dec 23;141:110286. Epub 2020 Sep 23.

Department of Mathematics, University of Malakand, Chakdara, Pakistan.

Number of well-known contagious diseases exist around the world that mainly include HIV, Hepatitis B, influenzas etc., among these, a recently contested coronavirus (COVID-19) is a serious class of such transmissible syndromes. Abundant scientific evidence the wild animals are believed to be the primary hosts of the virus. Read More

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December 2020

Deep Reinforcement Learning Control of Quantum Cartpoles.

Phys Rev Lett 2020 Sep;125(10):100401

Department of Physics and Institute for Physics of Intelligence, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan.

We generalize a standard benchmark of reinforcement learning, the classical cartpole balancing problem, to the quantum regime by stabilizing a particle in an unstable potential through measurement and feedback. We use state-of-the-art deep reinforcement learning to stabilize a quantum cartpole and find that our deep learning approach performs comparably to or better than other strategies in standard control theory. Our approach also applies to measurement-feedback cooling of quantum oscillators, showing the applicability of deep learning to general continuous-space quantum control. Read More

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September 2020

Stability Analysis and Optimal Control for Yellow Fever Model with Vertical Transmission.

Int J Appl Comput Math 2020 6;6(4):105. Epub 2020 Jul 6.

Department of Mathematics and Applied Mathematics, University of Pretoria, Pretoria, 0002 South Africa.

In this study, a deterministic model for the transmission dynamics of yellow fever (YF) in a human-mosquito setting in the presence of control measures is constructed and rigorously analyzed. In addition to horizontal transmissions, vertical transmission within mosquito population is incorporated. Analysis of the mosquito-only component of the model shows that the reduced model has a mosquito-extinction equilibrium, which is globally-asymptotically stable whenever the basic offspring number is less than unity. Read More

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Achieving stable dynamics in neural circuits.

PLoS Comput Biol 2020 08 7;16(8):e1007659. Epub 2020 Aug 7.

The Picower Institute for Learning & Memory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, United States of America.

The brain consists of many interconnected networks with time-varying, partially autonomous activity. There are multiple sources of noise and variation yet activity has to eventually converge to a stable, reproducible state (or sequence of states) for its computations to make sense. We approached this problem from a control-theory perspective by applying contraction analysis to recurrent neural networks. Read More

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