OPTIMAL CONTROL SOLUTION OF THE AUTOMOTIVE EMISSION-CONSTRAINED MINIMUM FUEL PROBLEM

Developing an efficient online predictive modeling system (PMS) is a major issue in the field of electrified vehicles as it can help reduce fuel consumption, greenhouse gasses (GHG) emission, but also the aging of power-train components, such as the battery. For this manuscript, a model predictive control (MPC) has been considered as PMS. This control design has been defined as an optimization problem that uses the projected system behaviors over a finite prediction horizon to determine the optimal control solution for the current time instant. In this manuscript, the MPC controller intents to diminish simultaneously the battery aging and the equivalent fuel consumption. The main contribution of this manuscript is to evaluate numerically the impacts of the vehicle battery model on the MPC optimal control solution when the plug hybrid electric vehicle (PHEV) is in the battery charge sustaining mode. Results show that the higher fidelity model improves the capability of accurately predicting the battery aging.

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Optimal feedback control of twin rotor MIMO system with a prescribed degree of stability

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-07-2016-0005 ◽

2016 ◽

Vol 4 (4) ◽

pp. 226-238 ◽

Cited By ~ 5

Author(s):

Santosh Kumar Choudhary

Keyword(s):

Optimal Control ◽

Tracking Control ◽

Mimo System ◽

Control Solution ◽

Content Type ◽

Degree Of Stability ◽

Twin Rotor Mimo System ◽

Posture Stabilization ◽

Twin Rotor ◽

Stability Concept

Purpose The purpose of this paper is to investigate an optimal control solution with prescribed degree of stability for the position and tracking control problem of the twin rotor multiple input-multiple output (MIMO) system (TRMS). The twin rotor MIMO system is a benchmark aerodynamical laboratory model having strongly non-linear characteristics and unstable coupling dynamics which make the control of such system for either posture stabilization or trajectory tracking a challenging task. Design/methodology/approach This paper first describes the dynamical model of twin rotor MIMO system (TRMS) and then it adopts linear-quadratic regulator (LQR)-based optimal control technique with prescribed degree of stability to achieve the desired trajectory or posture stabilization of TRMS. Findings The simulation results show that the investigated controller has both static and dynamic performance; therefore, the stability and the quick control effect can be obtained simultaneously for the twin rotor MIMO system. Originality/value The articles on LQR optimal controllers for TRMS can also be found in many literatures, but the prescribed degree of stability concept was not discussed in any of the paper. In this work, new LQR with the prescribed degree of stability concept is applied to provide an optimal control solution for the position and tracking control problem of TRMS.

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A First Optimal Control Solution for a Complex, Nonlinear, Tendon Driven Neuromuscular Finger Model

ASME 2010 Summer Bioengineering Conference, Parts A and B ◽

10.1115/sbc2010-19628 ◽

2010 ◽

Cited By ~ 1

Author(s):

Evangelos A. Theodorou ◽

Emo Todorov ◽

Francisco J. Valero-Cuevas

Keyword(s):

Optimal Control ◽

Muscle Force ◽

Index Finger ◽

Feedback Controller ◽

Extensor Mechanism ◽

High Dimensionality ◽

Control Solution ◽

Force Velocity ◽

Finger Model ◽

Robust To Noise

In this work we present the first constrained stochastic optimal feedback controller applied to a fully nonlinear, tendon driven index finger model. Our model also takes into account an extensor mechanism, and muscle force-length and force-velocity properties. We show this feedback controller is robust to noise and perturbations to the dynamics, while successfully handling the nonlinearities and high dimensionality of the system. By extending prior methods, we are able to approximate physiological realism by ensuring positivity of neural commands and tendon tensions at all times.

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