An integrated perturbation analysis and Sequential Quadratic Programming approach for Model Predictive Control

Ghaemi, Reza; Sun, Jing; Kolmanovsky, Ilya

doi:10.1016/j.automatica.2009.06.028

Cited by 66 publications

(50 citation statements)

References 16 publications

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“…For the case d = 1, our method coincides with the unconstrained neighboring extremal method found in [3]. The neighboring extremal method with state and input constraints has been considered in [6,7] in the development of fast model predictive control (MPC) laws. In this paper we do not treat state and input constraints.…”

Section: Introductionmentioning

confidence: 88%

“…Such perturbation controllers can be used to increase the speed of model predictive controllers (MPC) [6,7] by providing more accurate initial guesses to nonlinear programming solvers. In the MPC formulation, the terminal cost function φ can be chosen to ensure closedloop stability of the resulting MPC feedback [4].…”

Section: Perturbation Controllers Around a Nominal Optimal Trajectorymentioning

confidence: 99%

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Power series solutions to the time-varying dynamic programming equations

Aguilar

Krener

2011

IEEE Conference on Decision and Control and European Control Conference

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In this paper we construct high-order approximate solutions to the value function and optimal control for a finite-horizon optimal control problem for time-varying discrete-time nonlinear systems. The method consists in expanding the dynamic programming equations (DPE) in a power series, collecting homogeneous polynomial terms and solving for the unknown coefficients from the known and previously computed data. The resulting high-order equations are linear difference equations for the unknown homogeneous terms and are solved backwards in time. The method is applied to construct high-order perturbation controllers around a nominal optimal trajectory.

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Section: Introductionmentioning

confidence: 88%

Section: Perturbation Controllers Around a Nominal Optimal Trajectorymentioning

confidence: 99%

Power series solutions to the time-varying dynamic programming equations

Aguilar

Krener

2011

IEEE Conference on Decision and Control and European Control Conference

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“…The integrated perturbation analysis and sequential quadratic programming (IPA-SQP) algorithm, which has been demonstrated to have advantages in computational efficiency for NMPC, is applied. It combines the complementary features of perturbation analysis (PA) and SQP for solving constrained dynamic optimisation problems (Ghaemi, Sun, & Kolmanovsky, 2009, 2007Xie, Ghaemi, Sun, & Freudenberg, 2012).…”

Section: A Case Study: Ipa-sqp For Real-time Ips Controlmentioning

confidence: 98%

Optimisation-based control for electrified vehicles: challenges and opportunities

Sun

2015

Journal of Control and Decision

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Vehicle electrification is a trend that is transforming the transportation landscape. Automobiles, trains, ships and aircrafts are all being electrified as they are being designed to be more efficient, cleaner and more flexible. Vehicle electrification has brought many multi-faceted challenges and opportunities for control design and system integration. In this paper, we will discuss some of the control problems introduced by vehicle electrification. Case studies will be presented to illustrate the control implications. In particular, optimisation-based energy management solutions will be elucidated as representative examples of new design framework for control design and system integration for hybrid electric vehicles and all electric ships.

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“…w Σw ( 8 ) 020035-3 Thus, if the risk of an investment portfolio is measured using the Value-at-Risk, then it is based on Markowitz's, the investment portfolio optimization problems will be resolved, is shaped [2]: …”

Section: Modeling the Mean-var Portfolio Optimizationmentioning

confidence: 99%

“…either loss or gain in a given accounting period. Currently many developed calculation value risk in investing so that investors can know the value of risks and anticipate it early [16], [8].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Mean-VaR portfolio optimization by risk tolerance when the utility function is quadratic

Sidi¹,

Bon²,

Supian³

2017

AIP Conference Proceedings

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Estimation of value at risk in currency exchange rate portfolio using asymmetric GJR-GARCH Copula AIP Conference Proceedings 1827, 020006 (2017) Abstract. The problems of investing in financial assets are to choose a combination of weighting a portfolio can be maximized return expectations and minimizing the risk. This paper discusses the modeling of Mean-VaR portfolio optimization by risk tolerance, when square-shaped utility functions. It is assumed that the asset return has a certain distribution, and the risk of the portfolio is measured using the Value-at-Risk (VaR). So, the process of optimization of the portfolio is done based on the model of Mean-VaR portfolio optimization model for the Mean-VaR done using matrix algebra approach, and the Lagrange multiplier method, as well as Khun-Tucker. The results of the modeling portfolio optimization is in the form of a weighting vector equations depends on the vector mean return vector assets, identities, and matrix covariance between return of assets, as well as a factor in risk tolerance. As an illustration of numeric, analyzed five shares traded on the stock market in Indonesia. Based on analysis of five stocks return data gained the vector of weight composition and graphics of efficient surface of portfolio. Vector composition weighting weights and efficient surface charts can be used as a guide for investors in decisions to invest.

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An integrated perturbation analysis and Sequential Quadratic Programming approach for Model Predictive Control

Cited by 66 publications

References 16 publications

Power series solutions to the time-varying dynamic programming equations

Power series solutions to the time-varying dynamic programming equations

Optimisation-based control for electrified vehicles: challenges and opportunities

Modeling of Mean-VaR portfolio optimization by risk tolerance when the utility function is quadratic

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