scholarly journals ADAPTIVE DISTRIBUTION OF A SWARM OF HETEROGENEOUS ROBOTS

2016 ◽  
Vol 56 (1) ◽  
pp. 67 ◽  
Author(s):  
Amanda Prorok ◽  
M. Ani Hsieh ◽  
Vijay Kumar
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Optimization Problem ◽  
Optimization Method ◽  
Transition Rates ◽  
Trait Distribution ◽  
Time Optimization ◽  
Heterogeneous Robots ◽  
Real Time Optimization ◽  
Optimal Set

We present a method that distributes a swarm of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, where each species (robot type) is defined by the traits (capabilities) that it owns. Our method is based on a continuous abstraction of the swarm at a macroscopic level as we model robots switching between tasks. We formulate an optimization problem that produces an optimal set of transition rates for each species, so that the desired trait distribution is reached as quickly as possible. Since our method is based on the derivation of an analytical gradient, it is very efficient with respect to state-of-the-art methods. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates. Our approach is well-suited for real-time applications that rely on online redistribution of large-scale robotic systems.

Robust Real-Time Optimization of a Solid Oxide Fuel Cell Stack

2011 ◽  
Vol 8 (5) ◽  
Author(s):  
A. Marchetti ◽  
A. Gopalakrishnan ◽  
B. Chachuat ◽  
D. Bonvin ◽  
L. Tsikonis ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Real Time ◽  
Optimization Problem ◽  
Solid Oxide ◽  
Step Response ◽  
Oxide Fuel ◽  
Power Demand ◽  
Time Optimization ◽  
Real Time Optimization

On-line control and optimization can improve the efficiency of fuel cell systems, whilst simultaneously ensuring that the operation remains within a safe region. Also, fuel cells are subject to frequent variations in their power demand. This paper investigates the real-time optimization (RTO) of a solid oxide fuel cell (SOFC) stack. An optimization problem maximizing the efficiency subject to operating constraints is defined. Due to inevitable model inaccuracies, the open-loop implementation of optimal inputs evaluated off-line may be suboptimal, or worse, infeasible. Infeasibility can be avoided by controlling the constrained quantities. However, the constraints that determine optimal operation might switch with varying power demand, thus requiring a change in the regulator structure. In this paper, a control strategy that can handle plant-model mismatch and changing constraints in the face of varying power demand is presented and illustrated. The strategy consists in the integration of RTO and model predictive control (MPC). A lumped model of the SOFC is utilized at the RTO level. The measurements are not used to re-estimate the parameters of the SOFC model at different operating points, but to simply adapt the constraints in the optimization problem. The optimal solution generated by RTO is implemented using MPC that uses a step-response model in this case. Simulation results show that near-optimality can be obtained, and constraints are respected despite model inaccuracies and large variations in the power demand.

A new modifier adaptation methodology for real-time optimization

2017 ◽  
Vol 40 (4) ◽  
pp. 1320-1327
Author(s):  
Chunhua Chen ◽  
Mingxing Jia ◽  
Fuqiang You ◽  
Fuli Wang ◽  
Wenqi Kou
Keyword(s):  
Real Time ◽  
Flue Gas ◽  
Optimization Problem ◽  
Gradient Estimation ◽  
Search Area ◽  
Time Optimization ◽  
Decision Variables ◽  
Optimal Setting ◽  
Simulation Results ◽  
Real Time Optimization

The traditional modifier adaptation can be used to deal with the optimization problem of mismatched model, and it shows good performance in most cases. However, the method cannot be used directly, when the gradients of the model outputs, with respect to the decision variables, are difficult to calculate directly. Also, the simulation results show that the method cannot achieve the optimum in theory when the gradient estimation is particularly inaccurate. Therefore, a new modifier adaptation methodology for real-time optimization is proposed in this paper. A method similar to Proportion integration differentiation is used to deal with the deviation between the actual gradient and the model gradient and to improve the method of modifier terms computation. In addition, we find that the appropriate relaxation of certain constraints can expand the search area and improve the effectiveness of the optimization. The validation of the method is demonstrated by the solution of an artificial example and the optimal setting problem of the converter entrance temperatures in flue gas acid-making.

On-Board Real-Time Optimization Control for Turbo-Fan Engine Life Extending

2016 ◽  
Vol 0 (0) ◽  
Author(s):  
Qiangang Zheng ◽  
Haibo Zhang ◽  
Lizhen Miao ◽  
Fengyong Sun
Keyword(s):  
Real Time ◽  
Optimization Method ◽  
Support Vector ◽  
Thermal Mechanical Fatigue ◽  
Mechanical Fatigue ◽  
Time Optimization ◽  
Optimization Control ◽  
Conventional Optimization ◽  
Real Time Optimization ◽  
Engine Life

AbstractA real-time optimization control method is proposed to extend turbo-fan engine service life. This real-time optimization control is based on an on-board engine mode, which is devised by a MRR-LSSVR (multi-input multi-output recursive reduced least squares support vector regression method). To solve the optimization problem, a FSQP (feasible sequential quadratic programming) algorithm is utilized. The thermal mechanical fatigue is taken into account during the optimization process. Furthermore, to describe the engine life decaying, a thermal mechanical fatigue model of engine acceleration process is established. The optimization objective function not only contains the sub-item which can get fast response of the engine, but also concludes the sub-item of the total mechanical strain range which has positive relationship to engine fatigue life. Finally, the simulations of the conventional optimization control which just consider engine acceleration performance or the proposed optimization method have been conducted. The simulations demonstrate that the time of the two control methods from idle to 99.5 % of the maximum power are equal. However, the engine life using the proposed optimization method could be surprisingly increased by 36.17 % compared with that using conventional optimization control.

Real-time Optimization Method for Optical Parameters of Ion Implanters

2006 ◽  
Author(s):  
Seiji Ogata ◽  
Tsutomu Nishihashi ◽  
Kazuhiko Tonari ◽  
Hidekazu Yokoo ◽  
Hideo Suzuki ◽  
...  
Keyword(s):  
Real Time ◽  
Optimization Method ◽  
Optical Parameters ◽  
Time Optimization ◽  
Real Time Optimization
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