Towards a generic optimal co-design of hardware architecture and control configuration for interacting subsystems

In plants consisting of multiple interacting subsystems, the decision on how to optimally select and place actuators and sensors and the accompanying question on how to control the overall plant is a challenging task. Since there is no theoretical framework describing the impact of sensor and actuator placement on performance, an optimization method exploring the possible configurations is introduced in this paper to find a trade-off between implementation cost and achievable performance. Moreover, a novel model-based procedure is presented to simultaneously co-design the optimal number, type and location of actuators and sensors and to determine the corresponding optimal control architecture and accompanying control parameters. This paper adds the optimization of the control architecture to the current state-of-the-art. As an optimization output, a Pareto front is presented, providing insights on the optimal total plant performance related to the hardware and control design implementation cost. The proposed algorithm is not focused on one particular application or a specific optimization problem, but is instead a generally applicable method and can be applied to a wide range of applications (e.g., mechatronic, electrical, thermal). In this paper, the co-design approach is validated on a mechanical setup

Controller tuning by means of evolutionary multiobjective optimization: current trends and applications

Control engineering problems are generally multi-objective problems; meaning that there are several specifications and requirements that must be fulfilled. A traditional approach for calculating a solution with the desired trade-off is to define an optimisation statement. Multi-objective optimisation techniques deal with this problem from a particular perspective and search for a set of potentially preferable solutions; the designer may then analyse the trade-offs among them, and select the best solution according to his/her preferences. In this paper, this design procedure based on evolutionary multiobjective optimisation (EMO) is presented and significant applications on controller tuning are discussed. Throughout this paper it is noticeable that EMO research has been developing towards different optimisation statements, but these statements are not commonly used in controller tuning. Gaps between EMO research and EMO applications on controller tuning are therefore detected and suggested as potential trends for research.The first author is grateful for the hospitality and availability of the UTC at the University of Sheffield during his academic research stay at 2011; especially to Dr. P.J. Fleming for his valuable comments and insights in the development of this paper. This work was partially supported by Grant FPI-2010/19 and project PAID-2011/2732 from the Universitat Politecnica de Valencia and projects TIN2011-28082 and ENE2011-25900 from the Spanish Ministry of Economy and Competitiveness.Reynoso Meza, G.; Blasco Ferragud, FX.; Sanchís Saez, J.; Martínez Iranzo, MA. (2014). Controller tuning by means of evolutionary multiobjective optimization: current trends and applications. Control Engineering Practice. 28:58-73. https://doi.org/10.1016/j.conengprac.2014.03.003S58732

A review of design optimization methods for electrical machines

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines

Design of Outrunner Eectric Machines for Green Energy Applications

Interests in using rare-earth free motors such as switched reluctance motors (SRMs) for electric and hybrid electric vehicles (EV/HEVs) continue to gain popularity, owing to their low cost and robustness. Optimal design of an SRM, to meet specific characteristics for an application, should involve simultaneous optimization of the motor geometry and control in order to achieve the highest performance with the lowest cost. This dissertation firstly presents a constrained multi-objective optimization framework for design and control of a SRM based on a non-dominated sorting genetic algorithm II (NSGA-II). The proposed methodology optimizes SRM operation for high volume traction applications by considering multiple criteria including efficiency, average torque, and torque ripple. Several constraints are defined by the application considered, such as the motor stack length, minimum desired efficiency, etc. The outcome of this optimization includes an optimal geometry, outlining variables such as air gap length, rotor inner diameter, stator pole arc angle, etc as well as optimal turn-on and turn-off firing angles. Then the machine is manufactured according to the obtained optimal specifications. Finite element analysis (FEA) and experimental results are provided to validate the theoretical findings. A solution for exploring optimal firing angles of nonlinear current-controlled SRMs is proposed in order to minimize the torque ripple. Motor torque ripple for a certain electrical load requirement is minimized using a surrogate-based optimization of firing angles by adjusting the motor geometry, reference current, rotor speed and dc bus voltage. Surrogate-based optimization is facilitated via Neural Networks (NN) which are regression tools capable of learning complex multi-variate functions. Flux and torque of the nonlinear SRM is learned as a function of input parameters, and consequently the computation time of design, which is crucial in any micro controller unit, is expedited by replacing the look-up tables of flux and torque with the surrogate NN model. This dissertation then proposes a framework for the design and analysis of a coreless permanent magnet (PM) machine for a 100 kWh shaft-less high strength steel flywheel energy storage system (SHFES). The PM motor/generator is designed to meet the required specs in terms of torque-speed and power-speed characteristics given by the application. The design challenges of a motor/generator for this architecture include: the poor flux paths due to a large scale solid carbon steel rotor and zero-thermal convection of the airgap due to operation of the machine in vacuum. Magnetic flux in this architecture tends to be 3-D rather than constrained due to lack of core in the stator. In order to tackle these challenges, several other parameters such as a proper number of magnets and slots combination, number of turns in each coil, magnets with high saturated flux density and magnets size are carefully considered in the proposed design framework. Magnetic levitation allows the use of a coreless stator that is placed on a supporting structure. The proposed PM motor/generator comprehensive geometry, electromagnetic and mechanical dimensioning are followed by detailed 3-D FEA. The torque, power, and speed determined by the FEA electromagnetic analysis are met by the application design requirements and constraints for both the charging and discharging modes of operation. Finally, the motor/generator static thermal analysis is discussed in order to validate the proposed cooling system functionality

Methodology for Comparison of Algorithms for Real-World Multi-objective Optimization Problems: Space Surveillance Network Design

Space Situational Awareness (SSA) is an activity vital to protecting national and commercial satellites from damage or destruction due to collisions. Recent research has demonstrated a methodology using evolutionary algorithms (EAs) which is intended to develop near-optimal Space Surveillance Network (SSN) architectures in the sense of low cost, low latency, and high resolution. That research is extended here by (1) developing and applying a methodology to compare the performance of two or more algorithms against this problem, and (2) analyzing the effects of using reduced data sets in those searches. Computational experiments are presented in which the performance of five multi-objective search algorithms are compared to one another using four binary comparison methods, each quantifying the relationship between two solution sets in different ways. Relative rankings reveal strengths and weaknesses of evaluated algorithms empowering researchers to select the best algorithm for their specific needs. The use of reduced data sets is shown to be useful for producing relative rankings of algorithms that are representative of rankings produced using the full set

Active Processor Scheduling Using Evolution Algorithms

The allocation of processes to processors has long been of interest to engineers. The processor allocation problem considered here assigns multiple applications onto a computing system. With this algorithm researchers could more efficiently examine real-time sensor data like that used by United States Air Force digital signal processing efforts or real-time aerosol hazard detection as examined by the Department of Homeland Security. Different choices for the design of a load balancing algorithm are examined in both the problem and algorithm domains. Evolutionary algorithms are used to find near-optimal solutions. These algorithms incorporate multiobjective coevolutionary and parallel principles to create an effective and efficient algorithm for real-world allocation problems. Three evolutionary algorithms (EA) are developed. The primary algorithm generates a solution to the processor allocation problem. This allocation EA is capable of evaluating objectives in both an aggregate single objective and a Pareto multiobjective manner. The other two EAs are designed for fine turning returned allocation EA solutions. One coevolutionary algorithm is used to optimize the parameters of the allocation algorithm. This meta-EA is parallelized using a coarse-grain approach to improve performance. Experiments are conducted that validate the improved effectiveness of the parallelized algorithm. Pareto multiobjective approach is used to optimize both effectiveness and efficiency objectives. The other coevolutionary algorithm generates difficult allocation problems for testing the capabilities of the allocation EA. The effectiveness of both coevolutionary algorithms for optimizing the allocation EA is examined quantitatively using standard statistical methods. Also the allocation EAs objective tradeoffs are analyzed and compared

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field