Hypersonic flight vehicle trajectory optimization using pattern search algorithm

Kumar, Gulshan; Ikram, Mohammad; Sarkar, A. K.; Talole, S. E.

doi:10.1007/s11081-017-9367-0

Cited by 15 publications

(2 citation statements)

References 35 publications

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“…Pattern Search (PS) is a derivative-free optimization method that does not rely on gradient information [60], [61]. This is advantageous when dealing with optimization problems where the objective function is not differentiable or the gradients are difficult to compute.…”

Section: ) Pattern Search Algorithmmentioning

confidence: 99%

Robust PID Control of EV Aggregator for C-FCAS Using Sequential Multi-Objective Optimization

Pradana,

Haque,

Krismanto

et al. 2024

IEEE Access

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Integrating Electric Vehicles (EVs) charging into power grids through EV aggregator, offers promising benefits to society for reliable, sustainable, and cost-effective energy solutions. A key aspect of this integration is the possibility of provision of Contingency-Frequency Control Ancillary Services (C-FCAS). While EVs primarily serve owners' transportation needs, they must return to their idle positions with a satisfactory State of Charge (SoC) after providing frequency support following a contingency. However, real-world implementations of grid-connected EV aggregator control for C-FCAS are complex and challenging because several factors, including uncertainties, perturbation magnitude, renewable energy penetration, and the SoC of EVs need to be considered. To implement the C-FCAS considering these factors, this paper presents a robust PID control approach for an EV aggregator. The objective is to effectively manage EV units willing to participate, ensuring frequency regulation support, system stability, and smooth return to their idle position. The strategy exploits a sequential multi-objective optimization approach with two main objectives: mitigating frequency perturbations caused by contingencies and restoring EV units power discharge to zero after a critical time period. Sequential Multi-objective Optimization (SMO) algorithms optimize the control parameters under diverse scenarios and uncertainties. The simulation results validate the effectiveness of the proposed strategy. The control strategy by an EV aggregator successfully handled different market conditions and uncertainties, ensuring reliable frequency regulation. This service opportunity will enhance income of EV owners and their willingness to participate while helping to keep the integrity of system operation.

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Section: ) Pattern Search Algorithmmentioning

confidence: 99%

Robust PID Control of EV Aggregator for C-FCAS Using Sequential Multi-Objective Optimization

Pradana,

Haque,

Krismanto

et al. 2024

IEEE Access

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“…6 In order to achieve these goals, the flight path optimization is essential for hypersonic vehicles. 7…”

Section: Introductionmentioning

confidence: 99%

Numerical algorithm for hypersonic vehicle optimal flight control

Zhang

Ding

2023

Measurement and Control

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We consider a hypersonic vehicle optimal flight control problem. The problem is modeled as an optimal control problem of switched systems (OCPSS), which can become a parameter optimization problem (POP). Following that, to achieve the globally optimal solution of the POP, an improved continuous filled function (CFF) algorithm including one adjusting parameter is proposed based on a penalty function, in which the CFF is differentiable, excludes logarithmic terms or exponential terms, and does not require to minimize the cost function. Numerical results show that the proposed algorithm is effective.

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The role of airspeed variability in fixed-time, fuel-optimal aircraft trajectory planning

et al. 2022

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With the advent of improved aircraft situational awareness and the need for airlines to reduce their fuel consumption and environmental impact whilst adhering to strict timetables, fixed-time, fuel-optimal routing is vital. Here, the aircraft trajectory planning problem is addressed using optimal control theory. Two variants of a finite horizon optimal control formulation for fuel burn minimization are developed, subject to arrival constraints, an aerodynamic fuel-burn model, and a data-driven wind field. In the first variant, the control variable is expressed as a set of position-dependent aircraft headings, with the optimal control problem solved through a reduced gradient approach at a range of fixed airspeeds. The fuel optimal result is taken as the lowest fuel use recorded. In the second variant, both heading angle and airspeed are controlled. Results from three months of simulated flight routes between London and New York show that permitting optimised en-route airspeed variations leads to fuel savings of 0.5% on an average day (and up to 4% on certain days), compared with fixed airspeed flights. We conclude that significant fuel savings are possible if airspeeds are allowed to vary en route to take optimal advantage of the wind field.

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Hypersonic flight vehicle trajectory optimization using pattern search algorithm

Cited by 15 publications

References 35 publications

Robust PID Control of EV Aggregator for C-FCAS Using Sequential Multi-Objective Optimization

Robust PID Control of EV Aggregator for C-FCAS Using Sequential Multi-Objective Optimization

Numerical algorithm for hypersonic vehicle optimal flight control

The role of airspeed variability in fixed-time, fuel-optimal aircraft trajectory planning

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