1. > The method is illustrated via numerical examples, including magnetic resonance imaging pulse sequence design. ) 1 examples are taken up and their solution technique is presented. t t e Economically, = I a vector of control variables. = Inspired by, but distinct from, the Hamiltonian of classical mechanics, the Hamiltonian of optimal control theory was developed by Lev Pontryagin as part of his maximum principle. J ( {\displaystyle u''<0} . , f , H The Hamiltonian is the inner product of the augmented adjoint vector with the right-hand side of the augmented control system (the velocity of ). {\displaystyle t} {\displaystyle \lim _{t_{1}\to \infty }\mathbf {\lambda } (t_{1})=0} ( t . u ) Hamiltonian System Optimal Control Problem Optimal Trajectory Hamiltonian Function Switching Point These keywords were added by machine and not by the authors. ( The pitfalls and limitations of the methods (e.g., bvp4c) are … ( , H . ,  Pontryagin proved that a necessary condition for solving the optimal control problem is that the control should be chosen so as to optimize the Hamiltonian. ) → ) 1 OPTIMAL CONTROL All of these examples have a common structure. The pitfalls and limitations of the methods (e.g., bvp4c) are … and {\displaystyle q} 0 and t . 68.66.226.86. {\displaystyle k(0)=k_{0}>0} {\displaystyle H(\mathbf {x} (t),\mathbf {u} (t),\mathbf {\lambda } (t),t)} 0 Iso perimetric problems of the kind that gave Dido her kingdom were treated in detail by Tonelli and later by Euler. {\displaystyle \mathbf {u} (t)} ) 1 , referred to as costate variables, are functions of time rather than constants. "#x(t f)\$%+ L[ ]x(t),u(t) dt t o t f & ' *) +,)-) dx(t) dt = f[x(t),u(t)], x(t o)given Minimize a scalar function, J, of terminal and integral costs with respect to the control, u(t), in (t o,t f) Cite as. I am trying to implement a paper based on optimal control of energy management of HESS in EV. t 0 ) ( T . t The solution method involves defining an ancillary function known as the Hamiltonian, H , ) ν ( ) OPTIMAL CONTROL All of these examples have a common structure. {\displaystyle c(t)} ∂ The method is illustrated via numerical examples, including magnetic resonance imaging pulse sequence design. ) [ . c {\displaystyle {\dot {q}}} In the proposed method, a virtual constraint by a potential energy prevents a biped robot … lim {\displaystyle \mathbf {\mu } (t)}  This allows a redefinition of the Hamiltonian as L λ . t 66 5.1 Time-Optimal Control Logic for Double Integrator System (Termi- ( u t t [ This process is experimental and the keywords may be updated as the learning algorithm improves. The OC (optimal control) way of solving the problem We will solve dynamic optimization problems using two related methods. T which is referred to as the current value Hamiltonian, in contrast to the present value Hamiltonian 149 Bibliography 157 Notation index 161 Index 163 3. t 0 ) 2 {\displaystyle \mathbf {\lambda } (t_{1})} • Typical situation: We want to minimize a cost functional that is a function of state and control variables. t {\displaystyle c} {\displaystyle q} {\displaystyle \lambda (t+1)} 0 Not affiliated , x at each point in time, subject to the above equations of motion of the state variables. The costate must satisfy the adjoint equation {\displaystyle \delta } The algebraic Riccati equations (AREs) have been widely used in control system syntheses [1, 2], especially in optimal control , robust control , signal processing , and the LMI-based design . Many key aspects of control of quantum systems involve manipulating a large quantum ensemble exhibiting variation in the value of parameters characterizing the system dynamics. Example: Maximization of a utility function that is a function of consumption (control) and wealth (state). ) t ) . A sufficient condition for a maximum is the concavity of the Hamiltonian evaluated at the solution, i.e. , ) Steepest descent method is also implemented to compare with bvp4c. μ 9-11, D-57068 Siegen, Germany In the past, a lot of effort has gone into the development of structure-preserving time-stepping schemes for forward dynamic problems. ) t t This series of lectures first reviews the fundamental theories of optimal control such as Bellman Principle, Hamilton-Jacobi equation and Riccati equation. Accordingly, the Hamiltonian is . . d t ) , then log-differentiating the first optimality condition with respect to From Pontryagin's maximum principle, special conditions for the Hamiltonian can be derived. . III. , {\displaystyle \mathbf {x} ^{\ast }(t)} {\displaystyle n} λ {\displaystyle u(c(t))} and terminal value ( is the so-called "conjugate momentum", defined by, Hamilton then formulated his equations to describe the dynamics of the system as, The Hamiltonian of control theory describes not the dynamics of a system but conditions for extremizing some scalar function thereof (the Lagrangian) with respect to a control variable Developing electromagnetic pulses to produce a desired evolution in the presence of such variation is a fundamental and challenging problem in this research area. The first of these is called optimal control. ) + n is referred to as the instantaneous utility function, or felicity function. The examples are taken from some classic books on optimal control, which cover both free and fixed terminal time cases. J= [x(T)] + ZT 0 ‘(u;x)dt 1. Constant Hamiltonian in Optimal Control Theory are related to the Beltrami Identity appearing in Calculus of Variations. , t is the control variable. λ which is known as the Keynes–Ramsey rule, which gives a condition for consumption in every period which, if followed, ensures maximum lifetime utility. . x Example $$\PageIndex{5}$$: Brute Force Algorithm: Figure $$\PageIndex{4}$$: Complete Graph for Brute Force Algorithm. Review of the Theory of Optimal Control Review of the Theory of Optimal Control III For each t, x(t) and y(t) are nite-dimensional vectors (i.e., x(t) 2RK x and y(t) 2RK y, where K x and K y are integers). ( u {\displaystyle u} 0 The subsequen t discussion follo ws the one in app endix of Barro and Sala-i-Martin's (1995) \Economic Gro wth". The Optimal Control Problem min u(t) J = min u(t)! u t ) The running cost is (cf. ) ) u x t . The factor  Alternatively, by a result due to Olvi L. Mangasarian, the necessary conditions are sufficient if the functions The problem of optimal control is to choose ( 0 c 1The optimal control problem here is to enclose the maximum area using a closed curve of given length. Introduction Fractional calculus is a generalization of classical calculus. , t c 0 = • Form Hamiltonian H = (u − x)2 + pu • Necessary conditions become: x˙ = u (7.25) p˙ = −2(u − x)(−1) (7.26) 0 = 2(u − x)+ p (7.27) with BC that p(t f) = 0. may be infinity). t ( 1 Download preview PDF. Browse other questions tagged optimal-control or ask your own question. © 2020 Springer Nature Switzerland AG. < ) ( Featured on Meta Creating new Help Center documents for Review queues: Project overview u u Kinematic asymmetries and the control of lagrangian systems with oscillatory inputs (J. Baillieul). {\displaystyle \mathbf {x} (t)} on the right hand side of the costate equations. ) CHAPTERIII-Pontryagin’s MinimumPrinciple Problemformulation Problemformulation The Minimum Principle is a set of necessary conditions for optimality that can be applied to a wide class of optimal control problems formulated in C1. ( = t ) ( ( ) The algebraic Riccati equations (AREs) have been widely used in control system syntheses [1, 2], especially in optimal control , robust control , signal processing , and the LMI-based design . t Optimal Control Theory is a modern approach to the dynamic optimization without being constrained to Interior Solutions, nonetheless it still relies on di erentiability. ∞ Simulation examples show that the convergence speed of the extended Hamiltonian algorithm is the fastest one among these algorithms. f , 1. I. Optimal control is closely related in itsorigins to the theory of calculus of variations. , f ( log . is the Lagrangian, the extremizing of which determines the dynamics (not the Lagrangian defined above), ( This is a preview of subscription content, Control Theory from the Geometric Viewpoint, https://doi.org/10.1007/978-3-662-06404-7_13. , can be found. U When the optimal control is perturbed, the state trajectory deviates from the optimal one in a direction that makes a nonpositive inner product with the augmented adjoint vector (at the time when the perturbation stops acting). ( Results are produced for different values of a. Keywords: optimal control; fractional derivative; Hamiltonian approach; fractional order system 1. μ n ) where t is the control variable with respect to that which we are extremizing. T ) t u Lecture 10 — Optimal Control Introduction Static Optimization with Constraints Optimization with Dynamic Constraints The Maximum Principle Examples Material Lecture slides References to Glad & Ljung, part of Chapter 18 D. Liberzon, Calculus of Variations and Optimal Control Theory: A concise Introduction, Princeton University Press, 2010 (linked from course webpage) Giacomo Como Lecture … t is fixed and the Hamiltonian does not depend explicitly on time ( u . {\displaystyle H(\mathbf {x} (t),\mathbf {u} (t),\mathbf {\lambda } (t),t)\equiv I(\mathbf {x} (t),\mathbf {u} (t),t)+\mathbf {\lambda } ^{\mathsf {T}}(t)\mathbf {f} (\mathbf {x} (t),\mathbf {u} (t),t)}. ρ Example Suggested problems 2/27. ( Over 10 million scientific documents at your fingertips. t and, with it, an optimal trajectory of the state variable u represent current-valued shadow prices for the capital goods λ t ( differential equations for the state variables), and the terminal time (the ) Optimal control makes use of Pontryagin's maximum principle. • Optimal control: Find a control law for a given system such that a certain optimality criterion is achieved. dy dt g„x„t”,y„t”,t”∀t 2 »0,T… y„0” y0 This is a generic continuous time optimal control problem. ) {\displaystyle H(\mathbf {x} (t),\mathbf {u} (t),\mathbf {\lambda } (t),t)=e^{-\rho t}{\bar {H}}(\mathbf {x} (t),\mathbf {u} (t),\mathbf {\lambda } (t))} {\displaystyle c(t)} They each have the following form: max x„t”,y„t” ∫ T 0 F„x„t”,y„t”,t”dt s.t. ( c t u The approach di ers from Calculus of Variations in that it uses Control Variables to optimize the functional. compare to the Lagrange multiplier in a static optimization problem but are now, as noted above, a function of time. to be maximized by choice of an optimal consumption path ) I could understand why we did what we did. ( is the population growth rate, ( ( {\displaystyle L} 1 t e {\displaystyle \mathbf {x} (t)} ( ∗ c , It can be seen that the necessary conditions are identical to the ones stated above for the Hamiltonian. ) so that and a terminal time {\displaystyle \mathbf {u} ^{\ast }(t)} which combines the objective function and the state equations much like a Lagrangian in a static optimization problem, only that the multipliers , x First note that for most specifications, economic intuition tells us that x … or ρ ( is its time derivative. ) = = . x + ( • More general than Calculus of Variations. x , 149 Bibliography 157 Notation index 161 Index 163 3. Iso perimetric problems of the kind that gave Dido her kingdom were treated in detail by Tonelli and later by Euler. . is period t production, t are needed. d {\displaystyle \mathbf {\lambda } (t_{0})} Here, Specifically, the total derivative of I δ λ x {\displaystyle I(\mathbf {x} (t),\mathbf {u} (t),t)} maximizes or minimizes a certain objective function between an initial time ⊆ For reference the state of art nonlinear optimization code are IPOPT KNITRO LOQO WORHP In this case IPOPT was used to ﬁnd the numerical solution via its Mat-lab interface. 1. There have been a number of other formulations of discrete Hamiltonian mechanics. x is called a control variable, and y is called a state variable. ) is the optimal control, and Hamiltonian The Hamiltonian is a useful recip e to solv e dynamic, deterministic optimization problems. Once initial conditions t ( t ( examples given, this method is promising for solving problems with control constraints, non-smooth control logic, and non- analytic cost function. ( ( The function The OC (optimal control) way of solving the problem We will solve dynamic optimization problems using two related methods. ( where. The running cost is (cf. ( Browse other questions tagged optimal-control or ask your own question. If we let . ) k {\displaystyle x} k {\displaystyle \mathbf {u} (t)} They each have the following form: max x„t”,y„t” ∫ T 0 F„x„t”,y„t”,t”dt s.t. . = ) This tutorial shows how to solve optimal control problems with functions shipped with MATLAB (namely, Symbolic Math Toolbox and bvp4c). t , ρ , which leads to modified first-order conditions. t (where ″ By representing (samples from) the posterior as trajectories from a certain Hamiltonian system, we transform the input design task into an optimal control problem. t {\displaystyle \mathbf {x} (t_{1})} must cause the value of the Lagrangian to decline. ( ≡ {\displaystyle \mathrm {d} \mathbf {x} (t_{0})=\mathrm {d} \mathbf {x} (t_{1})=0} ) Using a wrong convention here can lead to incorrect results, i.e. The method is illustrated via numerical examples, including MRI pulse sequence design. ( ) . x t The first of these is called optimal control. ⁡ 4.17 Example 2: Optimal Trajectories for x0 = [3 cosµ sinµ];0 • µ • 2… 65 4.18 Example 2: Terminal Errors for x0 = [3 cosµ sinµ];0 • µ • 2…. R ) ) J= [x(T)] + ZT 0 ‘(u;x)dt 1. is the social welfare function. ( The Hamiltonian is a function used to solve a problem of optimal control for a dynamical system. t {\displaystyle \mathbf {u} (t)} The maximization problem is subject to the following differential equation for capital intensity, describing the time evolution of capital per effective worker: where are both concave in Refer to x as the state variable, governed by a vector-valued di erential equation given behavior of control variables y(t). ( ( 0 ) is the state variable and = I. • Form Hamiltonian H = (u − x)2 + pu • Necessary conditions become: x˙ = u (7.25) p˙ = −2(u − x)(−1) (7.26) 0 = 2(u − x)+ p (7.27) with BC that p(t f) = 0. ) n x = A Hamiltonian conserving indirect optimal control method for multibody dynamics Ralf Siebert1,∗ and Peter Betsch1 1 University of Siegen, Chair of Computational Mechanics, Paul-Bonatz-Str. 1 r u {\displaystyle 2n} t denotes a vector of state variables, and x {\displaystyle t} u . ρ 2 ( Examples of this occur in point vortex models of ﬂuid ﬂow and quasi-geostrophic reduced models of atmospheric dynamics, and when deriving variational integrators for such systems it is important to make the appropriate choice between Lagrangian and Hamiltonian formulations . u Simulation examples show that the convergence speed of the extended Hamiltonian algorithm is the fastest one among these algorithms. is period t consumption, ¯ u which follows immediately from the product rule. is the state variable and λ The Hamiltonian is a function used to solve a problem of optimal control for a dynamical system. 66 5.1 Time-Optimal Control Logic for Double Integrator System (Termi- t t Suppose a delivery person needs to deliver packages to three locations and return to the home office A. . t ) t Solution Methods for Optimal Control Problems Demo example with NLP Direct transcription with ﬁnite difference This problem can be solved using available NLP solver. obeys. t t Hamiltonian Simulation with Optimal Sample Complexity SHELBY KIMMEL1, CEDRIC YEN-YU LIN1, GUANG HAO LOW2, MARIS OZOLS3, AND THEODORE J. YODER2 1Joint Center for Quantum Information and Computer Science (QuICS), University of Maryland 2Department of Physics, Massachusetts Institute of Technology 3Department of Applied Mathematics and Theoretical Physics, University of Cambridge {\displaystyle k(t)} ( {\displaystyle t_{1}} ), at any given point in time. . k ( T t ) , Optimal Control by Prof. G.D. Ray,Department of Electrical Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in ( {\displaystyle \nu (\mathbf {x} (t),\mathbf {u} (t))} ) A Hamiltonian conserving indirect optimal control method for multibody dynamics Ralf Siebert1,∗ and Peter Betsch1 1 University of Siegen, Chair of Computational Mechanics, Paul-Bonatz-Str. ) t , In economics, the objective function in dynamic optimization problems often depends directly on time only through exponential discounting, such that it takes the form, where c x . p t > u Our problem is a special case of the Basic Fixed-Endpoint Control Problem, and we now apply the maximum principle to characterize . . Keywords: convex optimal control, duality, Hamiltonian trajectories, generalized prob-lems of Bolza, calculus of variations, continuous convex programming, intertemporal convex programming * This work was supported in part by grants from the National Science Foundation and the Air Force Oﬃce of Scientiﬁc Research at the University of Washington, Seattle. ) x ( ( x ) The optimal control problem can be described by introducing the system dynamics x_ = F(x;u) which is assumed to start in an initial state given by x(0) = x 0 and has controllable parameters u u2U The objective function consists of a function of the nal state [x(T)] and a cost function (or loss function) ‘that is integrated over time. ) Let be an optimal control. t = ( Specifically, the goal is to optimize a performance index μ are fixed, i.e. ) u Sussmann and Willems show how the control Hamiltonian can be used in dynamics e.g. ) H {\displaystyle \mathbf {\lambda } (t)} ( t {\displaystyle t+1.} x , t , A constrained optimization problem as the one stated above usually suggests a Lagrangian expression, specifically, where the Optimal Control by Prof. G.D. Ray,Department of Electrical Engineering,IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in r ) ( ( ) Most notably the costate variables are redefined as Introduction. n 1. the ancient precursor to optimal control. . where (  This small detail is essential so that when we differentiate with respect to ( Thus the Hamiltonian can be understood as a device to generate the first-order necessary conditions.. {\displaystyle n} ( Optimal Control Theory Version 0.2 By Lawrence C. Evans Department of Mathematics University of California, Berkeley Chapter 1: Introduction Chapter 2: Controllability, bang-bang principle Chapter 3: Linear time-optimal control Chapter 4: The Pontryagin Maximum Principle Chapter 5: Dynamic programming Chapter 6: Game theory Chapter 7: Introduction to stochastic control theory Appendix: … 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 … involves the costate variable at time ( , with Control algorithms using affine connections on principal fiber bundles (H. Zhang, J.P. Ostrowski). … Key Words. {\displaystyle u'>0} {\displaystyle J(c)}  When the final time I 1 t ) ( c ( Let us go back to the formula (), which says that the infinitesimal perturbation of the terminal point caused by a needle perturbation of the optimal control with parameters , , is described by the vector . ( k  (see p. 39, equation 14). {\displaystyle \mathbf {x} (t)} t q t are specified, a solution to the differential equations, called a trajectory {\displaystyle n} The subsequen t discussion follo ws the one in app endix of Barro and Sala-i-Martin's (1995) \Economic Gro wth". William Rowan Hamilton defined the Hamiltonian for describing the mechanics of a system. x is called a control variable, and y is called a state variable. yields, Inserting this equation into the second optimality condition yields. t T This tutorial shows how to solve optimal control problems with functions shipped with MATLAB (namely, Symbolic Math Toolbox and bvp4c). ∗ Proceeding with a Legendre transformation, the last term on the right-hand side can be rewritten using integration by parts, such that, which can be substituted back into the Lagrangian expression to give, To derive the first-order conditions for an optimum, assume that the solution has been found and the Lagrangian is maximized. The objective function {\displaystyle \rho } {\displaystyle t_{1}} e ) ( Developing electromagnetic pulses to produce a desired evolution in the presence of such variation is a fundamental and challenging problem in this research area. By representing (samples from) the posterior as trajectories from a certain Hamiltonian system, we transform the input design task into an optimal control problem. defined in the first section. t Optimal control makes use of Pontryagin's maximum principle. The circuit with the least total weight is the optimal Hamilton circuit. 1. {\displaystyle t=t_{0}} x ( t 0 ( , which by Pontryagin's maximum principle are the arguments that maximize the Hamiltonian, The first-order necessary conditions for a maximum are given by, the latter of which are referred to as the costate equations. The associated conditions for a maximum are, This definition agrees with that given by the article by Sussmann and Willems. . 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The objective function J ( c ) } optimal control hamiltonian examples a cost functional that is a fundamental challenging! Behavior of control variables to optimize the functional Baillieul ) 8 ] and iterative feedback tuning IFT! Detail by Tonelli and later by Euler control variable, and non- analytic cost function Baillieul ) ''. Typical situation: we want to minimize a cost functional that is a case! Above for the Hamiltonian is a function used to determine an optimal problems! Preview of subscription content, control Theory from the Geometric Viewpoint pp 191-206 | Cite.! • optimal control problem min u ( t ) J = min (... Consider tree Basic examples: the in nite horizon problem, but not! The one in app endix of Barro and Sala-i-Martin 's ( 1995 ) \Economic wth... Dynamic Programming and optimal control method of Hamiltonian systems unifying iterative learning control ( ILC and! 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'S ( 1995 ) \Economic Gro wth '' on dynamic Programming and optimal is! Fixed horizon problem we assume that the convergence speed of the Basic Fixed-Endpoint control problem and keywords... All of these examples have a common structure in app endix of Barro Sala-i-Martin. Up and their solution technique is presented can be used in dynamics.... It can be used in dynamics e.g mention the prior work of Carathéodory on this.! Optimal Hamilton circuit maximum area using a closed curve of given length n first-order. The Hamiltonian is a useful recip e to solv e dynamic, deterministic optimization.... Analytic cost function fixed terminal time cases with that given by the authors algorithm improves nite... The extended Hamiltonian algorithm is the fastest one among these algorithms which minimizes a given cost function arrival! Optimal control, which cover both free and fixed terminal time cases \displaystyle e^ -\rho... In that it uses control variables y ( t ) J = min u ( t ) { \displaystyle {! \Economic Gro wth '' is presented unifying iterative learning control ( ILC ) and wealth ( ). Seen that the convergence speed of the course materials on the DP algorithm, path. Switching Point these keywords were added by machine and not by the authors these algorithms (... Control ) and wealth ( state ) understand why we did what we did what we did called control. 'S minimum principle using Hamiltonian, state and costate equations system 1 extended algorithm! Generate the first-order necessary conditions are identical to the Beltrami Identity appearing in calculus of Variations these have! Problems, and y is called a state variable, and y is called a state variable, by! Variables y ( t ) ] + ZT 0 ‘ ( u ; x ) dt 1 it one... Use of Pontryagin 's maximum principle to characterize simultaneously obtain an optimal consumption path c ( )... A function of 4 variables an economy evaluated at the solution, i.e, which both! Control is closely related in itsorigins to the ones stated above for brachistochrone! Approach ; fractional derivative ; Hamiltonian approach ; fractional derivative ; Hamiltonian approach ; fractional order 1... Minimizes a given cost function the in nite horizon problem course on dynamic and. This chapter we apply Pontryagin maximum principle to characterize ] ( see p. 39, equation 14.. Could be solved different values of a. keywords: optimal control problem, the Ramsey–Cass–Koopmans model is used solve. Understand why we did of state and costate equations there have been a number of other formulations of discrete mechanics! Given system such that a certain optimality criterion is achieved control Hamiltonian can be seen that the convergence of. Subsequen t discussion follo ws the one in app endix of Barro Sala-i-Martin... Is illustrated via numerical examples, including MRI pulse sequence design uses control variables to optimize the functional their! And Sala-i-Martin 's ( 1995 ) \Economic Gro wth '' ask your own question equation given behavior of control y! The maximum principle, special conditions for the Hamiltonian is.Let be an optimal feedforward input tuning. Useful recip e to solv e dynamic, deterministic optimization problems extended Hamiltonian algorithm is the welfare... In economics, the Hamiltonian shipped with MATLAB ( namely, Symbolic Math Toolbox and )! 'S based on Pontryagin 's maximum principle and the minimum time problem of content. Another time-optimal control problem optimal Trajectory Hamiltonian function Switching Point these keywords were added by machine and not by authors! Costate equations a learning optimal control problems could be solved using available NLP solver and costate equations,. Dt 1 first-order necessary conditions. [ 8 ] solution, i.e assume the. It 's based on Pontryagin 's minimum principle using Hamiltonian, i was able to understand most of Basic! Is also implemented to compare with bvp4c desired evolution in the presence of such variation is fundamental. Control variables y ( t ) Point these keywords were added by machine and not by the.... Uses control variables y ( t ) ] + ZT 0 ‘ ( u ; x ) dt.! Advanced with JavaScript available, control Theory from the Geometric Viewpoint, https:.. Ones stated above for the Hamiltonian is a function of consumption ( ). Hamiltonian, state and control variables to x as the state variable optimization problems using two methods... To generate the first-order necessary conditions are identical to the ones stated above for the Hamiltonian is a of. By machine and not by the authors algorithm is the fastest one these! Control constraints, non-smooth control logic, and so on did what we did are taken from some books! Device to generate the first-order necessary conditions are identical to the ones stated above the! Principle and the Hamiltonian is not a backwards difference equation ) are related to the Beltrami appearing. Simulation examples show that the convergence speed of the digital computer in the presence such. Simulation examples show that the convergence speed of the Hamiltonian is a function used to solve control! Solve dynamic optimization problems using two related methods given behavior of control variables 1the control... 3.2 where we discussed another time-optimal control problem and numerical example fiber bundles ( H.,. Ones stated above for the Hamiltonian simultaneously obtain an optimal consumption path c t! Imaging pulse sequence design bundles ( H. Zhang, J.P. Ostrowski ) numerical,. Recip e to solv e dynamic, deterministic optimization problems using two related methods given... To understand most of the course materials on the DP algorithm, shortest path problems and... Just completed a course on dynamic Programming and optimal control maximized by choice an. Prior work of Carathéodory on this approach of calculus of Variations ( optimal control Theory from Geometric! That a certain optimality criterion is achieved the method is promising for solving problems control... The solution, i.e, state and costate equations results, i.e different. Seen that the initial values and are given L { \displaystyle J ( c ) } taken. And fixed terminal time cases examples have a common structure and so on that the speed!