Results 1  10
of
34
Time and Fuel Optimal Power Response of a DieselElectric Powertrain
"... Abstract: Optimal control policies for a dieselelectric powertrain in transient operation are studied. In order to fully utilize the extra degree of freedom available in a dieselelectric powertrain, compared to a conventional powertrain, the enginespeed is allowed to vary freely. The considered t ..."
Abstract

Cited by 8 (6 self)
 Add to MetaCart
Abstract: Optimal control policies for a dieselelectric powertrain in transient operation are studied. In order to fully utilize the extra degree of freedom available in a dieselelectric powertrain, compared to a conventional powertrain, the enginespeed is allowed to vary freely. The considered transients are steps from idle to target power. A nonlinear four statethree input mean value engine model, incorporating the important turbocharger dynamics, is used for this study. The study is conducted for two different criteria, fuel optimal control and time optimal control. The results from the optimization show that the optimal controls for each criteria can be divided into two categories, one for high requested powers and one for low requested powers. For high power transients the controls for both criteria follow a similar structure, a structure given by the maximum torque line and the smokelimiter. The main difference between the criteria is the end point and how it is approached. The fuel optimal control builds more kinetic energy in the turbocharger, reducing the necessary amount of kinetic energy in the system to produce the requested power. For low power transients the optimal controls deal with the turbocharger dynamics in a fundamentally different way. Keywords: Optimal Control, DieselElectric Powertrain 1.
MinimumTime Speed Optimization Over a Fixed Path
, 2013
"... In this paper we investigate the problem of optimizing the speed of a vehicle over a fixed path for minimum time traversal. We utilize a change of variables that has been known since the 1980s, although the resulting convexity of the problem was not noted until recently. The contributions of this pa ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
In this paper we investigate the problem of optimizing the speed of a vehicle over a fixed path for minimum time traversal. We utilize a change of variables that has been known since the 1980s, although the resulting convexity of the problem was not noted until recently. The contributions of this paper are three fold. First, we extend the convexification of the problem to a more general framework. Second, we identify a widerangeofvehiclemodelswhichcanbeincludedinthisexpandedframework. Third, we develop and implement an algorithm that allows these problems to be solved in real time, on embedded systems, with a high degree of accuracy.
Representing Movement Primitives as Implicit Dynamical Systems learned from Multiple Demonstrations
"... Abstract — This work deals with the problem of parameter estimation of dynamical systems intended to model demonstrated motion profiles for a system of interest. The regression problem is formulated as a constrained nonlinear least squares problem. We present an approach that extends the concept of ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Abstract — This work deals with the problem of parameter estimation of dynamical systems intended to model demonstrated motion profiles for a system of interest. The regression problem is formulated as a constrained nonlinear least squares problem. We present an approach that extends the concept of dynamical movement primitives to account for multiple demonstrations of a motion. We maintain an implicit dynamical system that resembles the demonstrated trajectories in a locally optimal way. This is achieved by solving a quadratic program (that encodes our notion of resemblance) at each sampling time step. Our method guarantees predictable state evolution even in regions of the state space not covered by the demonstrations. I.
Numerical Optimal Control
, 2011
"... Optimal control regards the optimization of dynamic systems. Thus, it bridges two large and active research communities of applied mathematics, each with their own journals and conferences. A scholar of numerical optimal control has to acquire basic numerical knowledge within both fields, i.e. numer ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Optimal control regards the optimization of dynamic systems. Thus, it bridges two large and active research communities of applied mathematics, each with their own journals and conferences. A scholar of numerical optimal control has to acquire basic numerical knowledge within both fields, i.e. numerical optimization on the one hand, and system theory and numerical simulation on the other hand. Within this text, we start by rehearsing basic concepts from both fields. Hereby, we give numerical optimization the larger weight, as dynamic system simulation is often covered ratherwellinengineeringandappliedmathematicscurricula,andbasicoptimizationconceptssuch as convexity or optimality conditions and Lagrange multipliers play a crucial role in numerical methods for optimal control. The course is intended for students of engineering and the exact sciences as well as for interested PhD students and besides the abovementioned fields requires only knowledge of linear algebra and numerical analysis. The course should be accompanied by computer exercises, and its aim is to give an introduction into numerical methods for solution of optimal control problems, in order to preparethe students for using and developing these methods themselves for specific applications in science and engineering. The course is divided into four major parts [for each of which we also give a few references for
Parallel MultipleShooting and Collocation Optimization with OpenModelica
"... Nonlinear model predictive control (NMPC) has become increasingly important for today’s control engineers during the last decade. In order to apply NMPC a nonlinear optimal control problem (NOCP) must be solved which needs a high computational effort. Stateoftheart solution algorithms are based on ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Nonlinear model predictive control (NMPC) has become increasingly important for today’s control engineers during the last decade. In order to apply NMPC a nonlinear optimal control problem (NOCP) must be solved which needs a high computational effort. Stateoftheart solution algorithms are based on multiple shooting and/or collocation algorithms, which are needed to solve the underlying dynamic model description. This paper concentrates on parallelizing these timeconsuming algorithms, which finally lead to a very fast solution of the underlying NOCP suitable for online application. The modeling and problem description is done in Optimica and Modelica. The simulation is performed using OpenModelica. Speedup curves for parallel execution are presented. To our knowledge this is one of the first parallel multiple shooting and collocation algorithms with corresponding implementations.
A Validated Integration Algorithm for Nonlinear ODEs using Taylor Models and
 Ellipsoidal Calculus, in 52nd IEEE Conference on Decision and Control
"... AbstractThis paper presents a novel algorithm for bounding the reachable set of parametric nonlinear differential equations. This algorithm is based on a firstdiscretizethenbound approach to enclose the reachable set via propagation of a Taylor model with ellipsoidal remainder, and it accounts f ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
AbstractThis paper presents a novel algorithm for bounding the reachable set of parametric nonlinear differential equations. This algorithm is based on a firstdiscretizethenbound approach to enclose the reachable set via propagation of a Taylor model with ellipsoidal remainder, and it accounts for truncation errors that are inherent to the discretization. In contrast to existing algorithms that proceed in two phasesan a priori enclosure phase, followed by a tightening phasethe proposed algorithm first predicts a continuoustime enclosure and then seeks a maximal stepsize for which validity of the predicted enclosure can be established. It is shown that this reversed approach leads to a natural stepsize control mechanism, which no longer relies on the availability of an a priori enclosure. Also described in the paper is an opensource implementation of the algorithm in ACADO Toolkit. A simple numerical case study is presented to illustrate the performance and stability of the algorithm.
Regulation of Differential Drive Robots using Continuous Time MPC without Stabilizing Constraints or Costs
 In Proceedings of the 5th IFAC Conference on Nonlinear Model Predictive Control (NPMC’15
, 2015
"... Abstract: In this paper, model predictive control (MPC) of differential drive robots is considered. Here, we solve the set point stabilization problem without incorporating stabilizing constraints and/or costs in the MPC scheme. In particular, we extend recent results obtained in a discrete time set ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Abstract: In this paper, model predictive control (MPC) of differential drive robots is considered. Here, we solve the set point stabilization problem without incorporating stabilizing constraints and/or costs in the MPC scheme. In particular, we extend recent results obtained in a discrete time setting to the continuous time domain. To this end, so called swaps and replacements are introduced in order to validate a growth condition on the value function and, thus, to rigorously prove asymptotic stability of the MPC closed loop for nonholonomic robots.
Convex optimization in Julia
 in Proceedings of the 1st First Workshop for High Performance Technical Computing in Dynamic Languages, HPTCDL ’14, Piscataway
"... ABSTRACT This paper describes Convex 1 , a convex optimization modeling framework in Julia. Convex translates problems from a userfriendly functional language into an abstract syntax tree describing the problem. This concise representation of the global structure of the problem allows Convex to in ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
ABSTRACT This paper describes Convex 1 , a convex optimization modeling framework in Julia. Convex translates problems from a userfriendly functional language into an abstract syntax tree describing the problem. This concise representation of the global structure of the problem allows Convex to infer whether the problem complies with the rules of disciplined convex programming (DCP), and to pass the problem to a suitable solver. These operations are carried out in Julia using multiple dispatch, which dramatically reduces the time required to verify DCP compliance and to parse a problem into conic form. Convex then automatically chooses an appropriate backend solver to solve the conic form problem.
A Framework for Nonlinear Model Predictive Control in JModelica.org
"... Nonlinear Model Predictive Control (NMPC) is a control strategy based on repeatedly solving an optimal control problem. In this paper we present a new MPC framework for the JModelica.org platform, developed specifically for use in NMPC schemes. The new framework utilizes the fact that the optimal ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Nonlinear Model Predictive Control (NMPC) is a control strategy based on repeatedly solving an optimal control problem. In this paper we present a new MPC framework for the JModelica.org platform, developed specifically for use in NMPC schemes. The new framework utilizes the fact that the optimal control problem to be solved does not change between solutions, thus decreasing the computation time needed to solve it. The new framework is compared to the old optimization framework in JModelica.org in regards to computation time and solution obtained through a benchmark on a combined cycle power plant. The results show that the new framework obtains the same solution as the old framework, but in less than half the time.