Introduction to model-based reinforcement learning 2. Class Notes 1. 2. discrete time linear optimal control (LQR) 3. linearizing around an operating point 4. linear model predictive control 5. Once the optimal path or value of the control variables is found, the ... namely, the optimal currency float. solving the optimal control problem in Step 1 of Algorithm 1, which is usually done numerically. We want to find optimal control solutions Online in real-time Using adaptive control techniques Without knowing the full dynamics For nonlinear systems and general performance indices Optimal Control and Planning CS 294-112: Deep Reinforcement Learning Sergey Levine. Optimal Control and Planning CS 285: Deep Reinforcement Learning, Decision Making, and Control Sergey Levine. • Optimal control of dynamic systems (ODE, DAE) • Multi-objective optimization (joint work with Filip Logist) • State and parameter estimation • Feedback control (NMPC) and closed loop simulation tools • Robust optimal control • Real-Time MPC and Code Export ACADO Toolkit - Automatic Control and Dynamic Optimization – p. 5/24 Lecture Slides for Robotics and Intelligent Systems. Lecture Slides for Space System Design. Introduction to model-based reinforcement learning 2. … adaptive optimal control algorithm •Great impact on the field of Reinforcement Learning – smaller representation than models – automatically focuses attention to where it is needed i.e., no sweeps through state space – though does not solve the exploration versus exploitation issue Variations on optimal control problem • time varying costs, dynamics, constraints – discounted cost – convergence to nonzero desired state – tracking time-varying desired trajectory • coupled state and input constraints, e.g., (x(t),u(t)) ∈ P ... mpc_slides.dvi Created Date: A 13-lecture course, Arizona State University, 2019 Videos on Approximate Dynamic Programming. Optimal Control Solution • Method #1: Partial Discretization – Divide Trajectory into Segments and Nodes – Numerically integrate node states – Impulsive Control at Nodes (or Constant Thrust Between Nodes) – Numerically integrated gradients – Solve Using Subspace Trust Region Method • Method #2: Transcription and Nonlinear Programming More general optimal control problems Many features left out here for simplicity of presentation: • multiple dynamic stages • differential algebraic equations (DAE) instead of ODE • explicit time dependence • constant design parameters Linear quadratic regulator. Many slides and figures adapted from Stephen Boyd [optional] Boyd and Vandenberghe, Convex Optimization, Chapters 9 – 11 [optional] Betts, Practical Methods for Optimal Control Using Nonlinear Programming TexPoint fonts used in EMF. Motivation. It is emerging as the computational framework of choice for studying the neural control of movement, in much the same way that probabilistic infer- Homework 3 is out! What if we know the dynamics? 3. Optimal control with several targets: the need of a rate-independent memory Fabio Bagagiolo University of Trento –Italy CoSCDS Padova September 25-29 2017. Bellman equation, slides; Feb 18: Linear Quadratic Regulator, Goal: An important special case. Problem Formulation. Essentials of Robust Control These slides will be updated when I have time. •Start early, this one will take a bit longer! In MPC, one often introduces additional terminal conditions, consisting of a ter-minal constraint set X 0 X and a terminal cost F : X 0!R. Classical Numerical Methods to Solve Optimal Control Problems; Linear Quadratic Regulator (LQR) Theory Videos and slides on Reinforcement Learning and Optimal Control. My books: I My two-volume textbook "Dynamic Programming and Optimal Control" was updated in 2017. Optimal Control Lectures 19-20: Direct Solution Methods Benoˆıt Chachuat Department of Chemical Engineering Spring 2009 Benoˆıt Chachuat (McMaster University) Direct Methods Optimal Control 1 / 32 Optimal Control Formulation We are concerned with numerical solution procedures for optimal control Generally not Optimal Optimal Control is off-line, and needs to know the system dynamics to solve design eqs. I For slides and videolecturesfrom 2019 and 2020 ASU courses, see my website. 2 Introduction ... Optimal control Bellman’s Dynamic Programming (1950’s) Pontryagin’s Maximum Principle (1950’s) Linear optimal control (late 1950’s and 1960’s) Optimal Control Theory is a modern approach to the dynamic optimization without being constrained to Interior Solutions, nonetheless it still relies on di erentiability. Homework 3 is out! Necessary Conditions of Optimality - Linear Systems Linear Systems Without and with state constraints. For control inequality constraints, the solution to LQR applies with the resulting control truncated at limit values. 3 Units. The following slides are supplied to aid control educators in the preparation and presentation of course material. Riccati Equation, Differential Dynamic Programming; Feb 20: Ways to reduce the curse of dimensionality Goal: Tricks of the trade. Linear Optimal Control *Slides based in part on Dr. Mike Stilman’sslides 11/04/2014 2 Linear Quadratic Regulator (LQR) • Remember Gains: K p and K d • LQR is an automated method for choosing OPTIMAL gains • Optimal with respect to what? Examples and applications from digital filters, circuits, signal processing, and control systems. Other Course Slide Sets Lecture Slides for Aircraft Flight Dynamics. Optimal control and dynamic programming; linear quadratic regulator. The slides are closely related to the text, aiding the educator in producing carefully integrated course material. Optimal Control Theory Emanuel Todorov University of California San Diego Optimal control theory is a mature mathematical discipline with numerous applications in both science and engineering. Classes of optimal control systems •Linear motion, Quadratic reward, Gaussian noise: •Solved exactly and in closed form over all state space by “Linear Quadratic Regulator” (LQR). Today’s Lecture 1. Time-varying and periodic systems. control and states) and how to approximate the continuous time dynamics. Read the TexPoint manual before you delete this box. EE392m - Spring 2005 Gorinevsky Control Engineering 14-13 The approach di ers from Calculus of Variations in that it uses Control Variables to optimize the functional. •Start early, this one will take a bit longer! slides chapter 10 fixed exchange rates, taxes, and capital controls. Through the use of inverters they can aid in the compensation of reactive power when needed, lowering their power factor. I My mathematically oriented research monograph “Stochastic Optimal Control" (with S. Optimal Control through Calculus of Variation. Examples are countries that ... of whether optimal capital control policy is macroprudential in the General considerations. The principal reference is Stengel, R., Optimal Control and Estimation, Dover Publications, NY, 1994. Contents •The need of rate-independent memory –Continuous memory/hysteresis •Dynamic programming with hysteresis - Some(quadratic) function of state (e.g. Lyapunov theory and methods. AN INTRODUCTION TO OPTIMAL CONTROL 23 Definition 5 (Lie Algebra of F) Let F be a family of smooth vector fields on a smooth manifold Mand denote by ˜(M)the set of all C1 vector fields on M. The Lie algebra Lie(F) generated by F is the smallest Lie subalgebra of ˜(M) containing The original optimal control problem is discretized and transcribed to a Non Linear Programming (NLP). Issues in optimal control theory 2. A simple system k b m Force exerted by the spring: Force exerted by the damper: Introduction to Optimal Control Organization 1. Last updated on August 28, 2000. Control slides are prepared using human tissue that has been collected, tracked, maintained and processed with the highest standards. The tissue is embedded in paraffin blocks, cut at an optimal thickness, and placed on an unbaked SuperFrost® Plus Slide. Review of Calculus of Variations – I; Review of Calculus of Variations – II; Optimal Control Formulation Using Calculus of Variations; Classical Numerical Techniques for Optimal Control. : AAAAAAAAAAAA. slides The NLP is solved using well-established optimization methods. Dealing with state- or state-control (mixed) constraints is more difficult, and the resulting conditions of optimality are very complex. Optimal Control --Approaches shooting collocation Return open-loop controls u 0, u 1, …, u H Return feedback policy (e.g. linear or neural net) n Roll-out u 0, u 1, …, u Hor OR: n Model-Predictive Control (MPC) n Just take the first action u 0or then resolve the optimization Allow 7-10 business days for delivery. Seminar Slides for From the Earth to the Moon. •Non-linear motion, Quadratic reward, Gaussian noise: Today’s Lecture 1. We investigate optimal control of linear port-Hamiltonian systems with control constraints, in which one aims to perform a state transition with minimal energy supply. References Quite a fewExact DPbooks (1950s-present starting with Bellman). 2. To this end, the opti-mization objective J See Applied optimal control… Remember project proposals next Wednesday! LQR variants 6. model predictive control for non-linear systems. But some countries lack the ability to conduct exchange-rate policy. • Optimal control trajectories converge to (0,0) • If N is large, the part of the problem for t > N can be neglected • Infinite-horizon optimal control ≈ horizon-N optimal control x1 x2 t > N Optimal control trajectories . Reinforcement Learning turns out to be the key to this! Class Notes 1. MAE 546, Optimal Control and Estimation Alternatively for the individual reader, the slides provide a summary of key control concepts presented in the text. Optimal Control: Linear Quadratic Regulator (LQR) System Performance Index Leibniz’s formula‐ Optimal Control is SVFB Algebraic Riccati equation dV dHx u Ax Bu Px xQx uRu(, , ) 2( ) 0 TT T du x du Stationarity Condition 20Ru B Px T ()() ()TT T T T T T T d V x … Realization theory. • Assuming already know the optimal path from each new terminal point (xj k+1), can establish optimal path to take from xi k using J (x k i,t k) = min ΔJ(x ki,x j +1)+ J (xj) xj k+1 – Then for each x ki, output is: iBest x k+1 to pick, because it gives lowest cost Control input required to … Introduction. Goal: Use of value function is what makes optimal control special. Minimum time. Optimal Reactive Power Control in Renewable Energy Sources: Comparing a metaheuristic versus a deterministic method Renewable energy sources such as photovoltaics and wind turbines are increasingly penetrating electricity grids. One of the two big algorithms in control (along with EKF). Minimize distance to goal) How can we make decisions? Methods differs for the variables to be discretized (i.e. Linear estimation and the Kalman filter. Classes of problems. Contribute to mail-ecnu/Reinforcement-Learning-and-Optimal-Control development by creating an account on GitHub.