Brief Review on Formation Control of Swarm Robot

This paper presented review formation control ofswarm robot. Recently the problems formation control of swarmrobots has attracted much attention, and several formationcontrol schemes were proposed based on various strategies. Theformation control strategies to solved these problem on swarmrobots, with considering regulation concept in control theory.Swarm intelligence algorithms takes the full of advantages of thefeature of swarm robotics, and provides a great solution forproblem formation control on swarm robots

Solving the potential field local minimum problem using internal agent states

We propose a new, extended artificial potential field method, which uses dynamic internal agent states. The internal states are modelled as a dynamical system of coupled first order differential equations that manipulate the potential field in which the agent is situated. The internal state dynamics are forced by the interaction of the agent with the external environment. Local equilibria in the potential field are then manipulated by the internal states and transformed from stable equilibria to unstable equilibria, allowiong escape from local minima in the potential field. This new methodology successfully solves reactive path planning problems, such as a complex maze with multiple local minima, which cannot be solved using conventional static potential fields

A review of optimization approaches for controlling water-cooled central cooling systems

Buildings consume a large amount of energy across all sectors of society, and a large proportion of building energy is used by HVAC systems to provide a comfortable and healthy indoor environment. In medium and large-size buildings, the central cooling system accounts for a major share of the energy consumption of the HVAC system. Improving the cooling system efficiency has gained much attention as the reduction of cooling system energy use can effectively contribute to environmental sustainability. The control and operation play an important role in central cooling system energy efficiency under dynamic working conditions. It has been proven that optimization of the control of the central cooling system can notably reduce the energy consumption of the system and mitigate greenhouse gas emissions. In recent years, numerous studies focus on this topic to improve the performance of optimal control in different aspects (e.g., energy efficiency, stability, robustness, and computation efficiency). This paper provides an up-to-date overview of the research and development of optimization approaches for controlling water-cooled central cooling systems, helping readers to understand the new significant trends and achievements in this area. The optimization approaches have been classified as system-model-based and data-based. In this paper, the optimization methodology is introduced first by summarizing the key decision variables, objective function, constraints, and optimization algorithms. The principle and performance of various optimization approaches are then summarized and compared according to their classification. Finally, the challenges and development trends for optimal control of water-cooled central cooling systems are discussed

Reactive Particle Swarm Control Architecture and Application for Scalar Field Adaptive Navigation

Adaptive navigation is a subcategory of navigation techniques that attempts to identify goal locations that satisfy specific criteria in an unknown area. In 2D scalar field adaptive navigation (SFAN), primitives navigate to or along features of interest in an unknown, possibly time-varying, planar scalar field. Features include extrema, contours, and fronts. This work solves the 2D SFAN problem using swarm robotic techniques. Robotic swarms are a subset of multi-robot systems that use decentralized control of simple interchangeable robots to perform collective actions. A subgroup of swarms is the Reactive Particle Swarm (RPS), characterized based on its simplicity, reactivity to its current environment, and flexibility of applications. Previous work in RPS lacks a unified implementation for RPS behaviors making cross-comparison and reuse challenging. This work presents a novel 1) RPS control architecture that streamlines the development of novel RPS behaviors, 2) elliptical aggregation algorithm that meets the four tenets of elliptical aggregation, and 3) series of 2D RPS SFAN primitives, and verifies all RPS base and composite behaviors using simulated and hardware-in-the-loop case studies. The architecture unifies the development of new RPS behaviors. The weighted summation of simple base behaviors and external command inputs form complex composite behaviors. This plug-and-play design concept allows for the rapid development of novel combinations of base behaviors, and emphasizes the topdown design of composite behaviors. A series of simulated and on-hardware case studies demonstrate the utility and flexibility of the architecture while establishing a library of verified RPS base behaviors. The four tenets of elliptical aggregation are 1) guidelines for swarm and ellipse parameter selection to ensure successful aggregation, 2) commandable ellipse parameters, 3) simplicity for scaling in the number of robots, and 4) adaptive sizing. The elliptical attraction behavior can be leveraged for SFAN to orient the swarm to improve feature sensing and size to overcome noise thresholds. The elliptical attraction behavior and adaptive sizing variant were verified using simulated and experimental trials. For 2D RPS SFAN primitives, the extremum seeking, contour following, and front identification behaviors and their adaptive sizing variants are verified using simulations incorporating both artificial and interpolated real-world scalar fields and hardware-in-the-loop trials. The ridge descent, trench ascent, and saddle point identification behaviors are presented in a preliminary form and are verified through simulation. Overall this work has four main contributions, 1) a novel RPS control architecture that unifies the implementation and streamlines the development of novel RPS behaviors, 2) a novel elliptical attraction behavior, 3) novel SFAN primitives, and 4) verification of all RPS behaviors through simulation and hardware-in-theloop trials

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation

Photovoltaic (PV) system is one of the reliable alternative sources of energy and its contribution in energy sector is growing rapidly. The performance of PV system depends upon the solar insolation, which will be varying throughout the day, season and year. The biggest challenge is to obtain the maximum power from PV array at varying insolation levels. The maximum power point tracking (MPPT) controller, in association with tracking algorithm will act as a principal element in driving the PV system at maximum power point (MPP). In this paper, the simulation model has been developed and the results were compared for perturb and observe, incremental conductance, extremum seeking control and fuzzy logic controller based MPPT algorithms at different irradiation levels on a 10 KW PV array. The results obtained were analysed in terms of convergence rate and their efficiency to track the MPP.Keywords: Photovoltaic system, MPPT algorithms, perturb and observe, incremental conductance, scalar gradient extremum seeking control, fuzzy logic controller.Article History: Received 3rd Oct 2016; Received in revised form 6th January 2017; Accepted 10th February 2017; Available onlineHow to Cite This Article: Naick, B. K., Chatterjee, T. K. & Chatterjee, K. (2017) Performance Analysis of Maximum Power Point Tracking Algorithms Under Varying Irradiation. Int Journal of Renewable Energy Development, 6(1), 65-74.http://dx.doi.org/10.14710/ijred.6.1.65-7