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Optimal control information


Optimal control problem benchmark (Luus) with an integral objective, inequality, and differential constraint

Optimal control theory is a branch of control theory that deals with finding a control for a dynamical system over a period of time such that an objective function is optimized.[1] It has numerous applications in science, engineering and operations research. For example, the dynamical system might be a spacecraft with controls corresponding to rocket thrusters, and the objective might be to reach the Moon with minimum fuel expenditure.[2] Or the dynamical system could be a nation's economy, with the objective to minimize unemployment; the controls in this case could be fiscal and monetary policy.[3] A dynamical system may also be introduced to embed operations research problems within the framework of optimal control theory.[4][5]

Optimal control is an extension of the calculus of variations, and is a mathematical optimization method for deriving control policies.[6] The method is largely due to the work of Lev Pontryagin and Richard Bellman in the 1950s, after contributions to calculus of variations by Edward J. McShane.[7] Optimal control can be seen as a control strategy in control theory.[1]

  1. ^ a b Ross, Isaac (2015). A primer on Pontryagin's principle in optimal control. San Francisco: Collegiate Publishers. ISBN 978-0-9843571-0-9. OCLC 625106088.
  2. ^ Luenberger, David G. (1979). "Optimal Control". Introduction to Dynamic Systems. New York: John Wiley & Sons. pp. 393–435. ISBN 0-471-02594-1.
  3. ^ Kamien, Morton I. (2013). Dynamic Optimization: the Calculus of Variations and Optimal Control in Economics and Management. Dover Publications. ISBN 978-1-306-39299-0. OCLC 869522905.
  4. ^ Ross, I. M.; Proulx, R. J.; Karpenko, M. (6 May 2020). "An Optimal Control Theory for the Traveling Salesman Problem and Its Variants". arXiv:2005.03186 [math.OC].
  5. ^ Ross, Isaac M.; Karpenko, Mark; Proulx, Ronald J. (1 January 2016). "A Nonsmooth Calculus for Solving Some Graph-Theoretic Control Problems**This research was sponsored by the U.S. Navy". IFAC-PapersOnLine. 10th IFAC Symposium on Nonlinear Control Systems NOLCOS 2016. 49 (18): 462–467. doi:10.1016/j.ifacol.2016.10.208. ISSN 2405-8963.
  6. ^ Sargent, R. W. H. (2000). "Optimal Control". Journal of Computational and Applied Mathematics. 124 (1–2): 361–371. Bibcode:2000JCoAM.124..361S. doi:10.1016/S0377-0427(00)00418-0.
  7. ^ Bryson, A. E. (1996). "Optimal Control—1950 to 1985". IEEE Control Systems Magazine. 16 (3): 26–33. doi:10.1109/37.506395.

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Optimal control

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Optimal control theory is a branch of control theory that deals with finding a control for a dynamical system over a period of time such that an objective...

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Pseudospectral optimal control

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optimal control is a joint theoretical-computational method for solving optimal control problems. It combines pseudospectral (PS) theory with optimal...

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Model predictive control

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for this reason MPC is also called receding horizon control. Although this approach is not optimal, in practice it has given very good results. Much academic...

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Bellman equation

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the optimal policy in the last time period is specified in advance as a function of the state variable's value at that time, and the resulting optimal value...

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unscented optimal control combines the notion of the unscented transform with deterministic optimal control to address a class of uncertain optimal control problems...

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Control theory

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Fractional-order control H-infinity loop-shaping Hierarchical control system Model predictive control Optimal control Process control Robust control Servomechanism...

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Reinforcement learning

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learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a...

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PROPT

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The PROPT MATLAB Optimal Control Software is a new generation platform for solving applied optimal control (with ODE or DAE formulation) and parameters...

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Mathematical optimization

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a cost function where a minimum implies a set of possibly optimal parameters with an optimal (lowest) error. Typically, A is some subset of the Euclidean...

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Machine learning control

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Machine learning control (MLC) is a subfield of machine learning, intelligent control and control theory which solves optimal control problems with methods...

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Stochastic control

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Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations...

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Degrees of freedom problem

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the natural outcome of an adaptive optimal control process. Optimal control is a way of understanding motor control and the motor equivalence problem,...

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Value function

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on the parameters of the problem. In a controlled dynamical system, the value function represents the optimal payoff of the system over the interval [t...

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same precision as an optimal design. In practical terms, optimal experiments can reduce the costs of experimentation. The optimality of a design depends...

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Transversality condition

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variables. They are one of the necessary conditions for optimality infinite-horizon optimal control problems without an endpoint constraint on the state...

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Control

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regulations on trade Internal control, a process to help achieve specific goals typically related to managing risk Control (optimal control theory), a variable...

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Trajectory optimization

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optimization is a technique for computing an open-loop solution to an optimal control problem. It is often used for systems where computing the full closed-loop...

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Free energy principle

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problem – to movement trajectories. Active inference is related to optimal control by replacing value or cost-to-go functions with prior beliefs about...

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Monte Carlo method

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"Estimation and nonlinear optimal control: Particle resolution in filtering and estimation". Studies on: Filtering, optimal control, and maximum likelihood...

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Dynamic programming

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solved optimally by breaking it into sub-problems and then recursively finding the optimal solutions to the sub-problems, then it is said to have optimal substructure...

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CasADi

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and open source symbolic framework for automatic differentiation and optimal control. Automatic differentiation JModelica.org "Optimization in Engineering...

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