Simultaneous dynamic optimization strategies: Recent advances and challenges

Kameswaran, Shivakumar; Biegler, Lorenz T.

doi:10.1016/j.compchemeng.2006.05.034

Cited by 201 publications

(149 citation statements)

References 50 publications

Supporting

Mentioning

142

Contrasting

Order By: Relevance

“…To deal with control and optimization of systems modeled by DAEs, several strategies have been proposed, such as simultaneous strategies [6,9,21], multiple shooting strategies [11,12], and direct search methods [43]. The particular structure of power network models (see Section 3.3.4) can especially be used advantageously to set up tractable models for model-based control approaches, such as model predictive control (MPC) [31].…”

Section: Power Network Modeling and Controlmentioning

confidence: 99%

Prevention of Emergency Voltage Collapses in Electric Power Networks using Hybrid Predictive Control

Leirens

Negenborn

2009

Intelligent Infrastructures

View full text Add to dashboard Cite

Section: Power Network Modeling and Controlmentioning

confidence: 99%

Prevention of Emergency Voltage Collapses in Electric Power Networks using Hybrid Predictive Control

Leirens

Negenborn

2009

Intelligent Infrastructures

View full text Add to dashboard Cite

“…The direct shooting method, also sometimes known as the sequential method [49], the control vector is parameterized by piecewise polynomials and optimization is done with respect to these variables only. Given the initial conditions xO, the optimal control problem DAE (Differential Algebraic Equations) are solved using explicit integration methods in a loop driven by the NLP solver as shown in Figure 5.6.…”

Section: A Description Of the Algorithmmentioning

confidence: 99%

Dynamics and control of electromagnetic satellite formations

Ahsun

Miller

2006

2006 American Control Conference

View full text Add to dashboard Cite

Satellite formation flying is an enabling technology for many space missions, especially for space-based telescopes. Usually there is a tight formation-keeping requirement that may need constant expenditure of fuel or at least fuel is required for formation reconfiguration. Electromagnetic Formation Flying (EMFF) is a novel concept that uses superconducting electromagnetic coils to provide forces and torques between different satellites in a formation which enables the control of all the relative degrees of freedom.With EMFF, the life-span of the mission becomes independent of the fuel available on board. Also the contamination of optics or sensitive formation instruments, due to thruster plumes, is avoided. This comes at the cost of coupled and nonlinear dynamics of the formation and makes the control problem a challenging one. In this thesis, the dynamics for a general N-satellite electromagnetic formation will be derived for both deep space missions and Low Earth Orbit (LEO) formations. Nonlinear control laws using adaptive techniques will be derived for general formations in LEO. Angular momentum management in LEO is a problem for EMFF due to interaction of the magnetic dipoles with the Earth's magnetic field. A solution of this problem for general Electromagnetic (EM) formations will be presented in the form of a dipole polarity switching control law. For EMFF, the formation reconfiguration problem is a nonlinear and constrained optimal time control problem as fuel cost for EMFF is zero. Two 3 different methods of trajectory generation, namely feedback motion planning using the Artificial Potential Function Method (APFM) and optimal trajectory generation using the Legendre Pseudospectral method, will be derived for general EM Formations. The results of these methods are compared for random EM Formations. This comparison shows that the artificial potential function method is a promising technique for solving the real-time motion planning problem of nonlinear and constrained systems, such as EMFF, with low computational cost. Specifically it is the purpose of this thesis to show that a fullyactuated N-satellite EM formation can be stabilized and controlled under fairly general assumptions, therefore showing the viability of this novel approach for satellite formation flying from a dynamics and controls perspective.

show abstract

“…Additionally, the optimizer used in real-time estimation and control algorithms can be similarly refined to satisfy the requirements for use in the microcontroller. 35 Future work will be to design new solvers or parse down existing solvers to further reduce the overhead required to perform simulations in a microcontroller environment. For comparing solvers in a desktop computer environment, DASKR 67 /IDA 69 can be used in full form, but they are not optimized for implementation in a microcontroller.…”

Section: Future Directionsmentioning

confidence: 99%

“…Optimal simulation techniques [31][32][33][34] with model predictive control for optimization 35 can improve performance of batteries in high demand applications. Using reformulated models and improved simulation techniques, [31][32][33][34] advanced control schemes can be developed leading to better utilization of any battery chemistry.…”

mentioning

confidence: 99%

Efficient Simulation and Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation

Northrop

Suthar

Ramadesigan

et al. 2014

J. Electrochem. Soc.

View full text Add to dashboard Cite

Improving the efficiency and utilization of battery systems can increase the viability and cost-effectiveness of existing technologies for electric vehicles (EVs). Developing smarter battery management systems and advanced sensing technologies can circumvent problems arising due to capacity fade and safety concerns. This paper describes how efficient simulation techniques and improved algorithms can alleviate some of these problems to help electrify the transportation industry by improving the range of variables that are predictable and controllable in a battery in real-time within an electric vehicle. The use of battery models in a battery management system (BMS) is reviewed. The effect of different simulation techniques on computational cost and accuracy are also compared, and the validity of implementation in a microcontroller environment for model predictive control (MPC) is addressed. Using mathematical techniques to add more physics without losing efficiency is also discussed. Behavioral predictions can be made using mathematical models without the need to directly observe the states using expensive and time consuming physical experiments. Such predictions allow for more intelligent design of new systems, which is generally limited by the mathematical techniques used and the computational resources available. An improved modeling and simulation approach can achieve the following goals when applied to engineering systems: r More accurate predictions by using more meaningful models r Faster simulation with fewer computational resources r Optimization of design parameters r Better control, allowing aggressive performance while maintaining safetyHere we focus on the application of such principles to the use of physics-based battery models in battery management systems in electric vehicles.In recent years, battery electric vehicles (BEV) have increased in popularity to reduce the dependence on fossil fuels. Lithium-ion batteries are a popular choice as an energy storage medium for high demand applications due to their large energy density but are not utilized to their full capacity in BEV applications; operating a Li-ion battery too aggressively can lead to reduced cycle life and unpredictable thermal runaway reactions. These challenges reduce the functional capacity of the battery available for propulsion.The consumer expects the vehicle's performance and capabilities to remain uniform regardless of the state of charge or age of the battery, as they have become accustomed to internal combustion engines. When the battery is nearly depleted, it is difficult or impossible to satisfy high power demand, which is aggravated as the battery ages. To avoid these difficulties, the BMS shuts off the battery with a large amount of energy unused, so that Li-ion batteries for EVs are greatly overdesigned and carry extra weight and volume, reducing efficiency and increasing cost.1 Research is underway to better understand the internal limitations of Li-ion batteries including SEI layer growth, side * Electrochemical Society ...

show abstract

Simultaneous dynamic optimization strategies: Recent advances and challenges

Cited by 201 publications

References 50 publications

Prevention of Emergency Voltage Collapses in Electric Power Networks using Hybrid Predictive Control

Prevention of Emergency Voltage Collapses in Electric Power Networks using Hybrid Predictive Control

Dynamics and control of electromagnetic satellite formations

Efficient Simulation and Reformulation of Lithium-Ion Battery Models for Enabling Electric Transportation

Contact Info

Product

Resources

About