Particle Swarm with Domain Partition and Control Assignment for Time-Optimal Maneuvers

2018 ◽  
Vol 41 (4) ◽  
pp. 968-977 ◽  
Author(s):  
Dario Spiller ◽  
Christian Circi ◽  
Fabio Curti
Keyword(s):  
Particle Swarm ◽  
Time Optimal ◽  
Domain Partition ◽  
And Control

scholarly journals Sparse stabilization and control of alignment models

2014 ◽  
Vol 25 (03) ◽  
pp. 521-564 ◽  
Author(s):  
Marco Caponigro ◽  
Massimo Fornasier ◽  
Benedetto Piccoli ◽  
Emmanuel Trélat
Keyword(s):  
Policy Maker ◽  
Research Direction ◽  
Fundamental Question ◽  
Self Organization ◽  
Group Consensus ◽  
Future Developments ◽  
Consensus Dynamics ◽  
Time Optimal ◽  
And Control ◽  
Mathematical Justification

Starting with the seminal papers of Reynolds (1987), Vicsek et al. (1995), Cucker–Smale (2007), there has been a lot of recent works on models of self-alignment and consensus dynamics. Self-organization has so far been the main driving concept of this research direction. However, the evidence that in practice self-organization does not necessarily occur (for instance, the achievement of unanimous consensus in government decisions) leads to the natural question of whether it is possible to externally influence the dynamics in order to promote the formation of certain desired patterns. Once this fundamental question is posed, one is also faced with the issue of defining the best way of obtaining the result, seeking for the most "economical" way to achieve a certain outcome. Our paper precisely addressed the issue of finding the sparsest control strategy in order to lead us optimally towards a given outcome, in this case the achievement of a state where the group will be able by self-organization to reach an alignment consensus. As a consequence, we provide a mathematical justification to the general principle according to which "sparse is better": in order to achieve group consensus, a policy maker not allowed to predict future developments should decide to control with stronger action the fewest possible leaders rather than trying to act on more agents with minor strength. We then establish local and global sparse controllability properties to consensus. Finally, we analyze the sparsity of solutions of the finite time optimal control problem where the minimization criterion is a combination of the distance from consensus and of the ℓ1-norm of the control. Such an optimization models the situation where the policy maker is actually allowed to observe future developments. We show that the lacunarity of sparsity is related to the codimension of certain manifolds in the space of cotangent vectors.

Parameter Settings in Particle Swarm Optimization

2017 ◽  
pp. 101-132
Author(s):  
Snehal Mohan Kamalapur ◽  
Varsha Patil
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Poor Performance ◽  
Parameter Tuning ◽  
Population Based ◽  
Parameter Control ◽  
Swarm Optimization ◽  
Study Parameter ◽  
Optimal Values ◽  
And Control

The issue of parameter setting of an algorithm is one of the most promising areas of research. Particle Swarm Optimization (PSO) is population based method. The performance of PSO is sensitive to the parameter settings. In the literature of evolutionary computation there are two types of parameter settings - parameter tuning and parameter control. Static parameter tuning may lead to poor performance as optimal values of parameters may be different at different stages of run. This leads to parameter control. This chapter has two-fold objectives to provide a comprehensive discussion on parameter settings and on parameter settings of PSO. The objectives are to study parameter tuning and control, to get the insight of PSO and impact of parameters settings for particles of PSO.

Strategies to enhance the biodegradation of toxic compounds using discontinuous processes

2001 ◽  
Vol 43 (3) ◽  
pp. 283-290 ◽  
Author(s):  
G. Buitrón ◽  
G. Soto ◽  
G. Vite ◽  
J. Moreno
Keyword(s):  
Optimal Control ◽  
Activated Carbon ◽  
Dissolved Oxygen ◽  
Batch Reactor ◽  
Biological Degradation ◽  
Optimal Control Strategy ◽  
Measured Variable ◽  
Time Optimal ◽  
High Concentrations ◽  
And Control

This study presents two strategies used to enhance the biological degradation of phenolic wastewaters. In the first one the operation of a sequencing batch biofilter added with granular activated carbon (SBB-AC) was studied. The second strategy presents the results of the automation of a sequencing batch reactor in order to optimize the reaction phase. In this case, the dissolved oxygen was employed to monitor and control the reactor. The results of the SBB-AC system, based on the configuration of the reactor, type and size of activated carbon and size of the packing material, are discussed. The system biodegraded efficiently (total phenol removals as high as 97%) high concentrations (600 mg/l) of a mixture of phenol, 4-chlorophenol, 2,4-dichlorophenol and 2,4,6-trichlorophenol. Maximal eliminated loads of 4.33 kg COD/m3-d were achieved. For the second strategy, the applicability of an optimal control for a SBR using the dissolved oxygen as the measured variable was demonstrated. When the reactor was operated under the time-optimal control strategy, the degradation time of 4-chlorophenol was reduced. A very satisfactory operation of the reactor was observed, since the removal efficiencies were around 99%.

Time and Frequency Analysis of Particle Swarm Trajectories for Cognitive Machines

2011 ◽  
Vol 5 (1) ◽  
pp. 18-42 ◽  
Author(s):  
Dario Schor ◽  
Witold Kinsner
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Space ◽  
Particle Swarm ◽  
Local Area ◽  
Complexity Measures ◽  
Swarm Optimization ◽  
Frequency Domains ◽  
Frequency Domain Techniques ◽  
And Control

This paper examines the inherited persistent behavior of particle swarm optimization and its implications to cognitive machines. The performance of the algorithm is studied through an average particle’s trajectory through the parameter space of the Sphere and Rastrigin function. The trajectories are decomposed into position and velocity along each dimension optimized. A threshold is defined to separate the transient period, where the particle is moving towards a solution using information about the position of its best neighbors, from the steady state reached when the particles explore the local area surrounding the solution to the system. Using a combination of time and frequency domain techniques, the inherited long-term dependencies that drive the algorithm are discerned. Experimental results show the particles balance exploration of the parameter space with the correlated goal oriented trajectory driven by their social interactions. The information learned from this analysis can be used to extract complexity measures to classify the behavior and control of particle swarm optimization, and make proper decisions on what to do next. This novel analysis of a particle trajectory in the time and frequency domains presents clear advantages of particle swarm optimization and inherent properties that make this optimization algorithm a suitable choice for use in cognitive machines.

scholarly journals Discrete-Time Optimal Attitude Control of a Spacecraft with Momentum and Control Constraints

2018 ◽  
Vol 41 (1) ◽  
pp. 199-211 ◽  
Author(s):  
Karmvir Singh Phogat ◽  
Debasish Chatterjee ◽  
Ravi Banavar
Keyword(s):  
Discrete Time ◽  
Attitude Control ◽  
Control Constraints ◽  
Time Optimal ◽  
And Control
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