Sliding Mode Control for Servo Motors Based on the Differential Evolution Algorithm

Zhonggang Yin,Lei Gong,Chao Du,Jing Liu,Yanru Zhong 전력전자학회 2018 JOURNAL OF POWER ELECTRONICS Vol.18 No.1

A sliding mode control (SMC) for servo motors based on the differential evolution (DE) algorithm, called DE-SMC, is proposed in this study. The parameters of SMC should be designed exactly to improve the robustness, realize the precision positioning, and reduce the steady-state speed error of the servo drive. The main parameters of SMC are optimized using the DE algorithm according to the speed feedback information of the servo motor. The most significant influence factor of the DE algorithm is optimization iteration. A suitable iteration can be achieved by the tested optimization process profile of the main parameters of SMC. Once the parameters of SMC are optimized under a convergent iteration, the system realizes the given performance indices within the shortest time. The experiment indicates that the robustness of the system is improved, and the dynamic and steady performance achieves the given performance indices under a convergent iteration when motor parameters mismatch and load disturbance is added. Moreover, the suitable iteration effectively mitigates the low-speed crawling phenomenon in the system. The correctness and effectiveness of DE-SMC are verified through the experiment.

Sliding Mode Control for Servo Motors Based on the Differential Evolution Algorithm

Yin, Zhonggang,Gong, Lei,Du, Chao,Liu, Jing,Zhong, Yanru The Korean Institute of Power Electronics 2018 JOURNAL OF POWER ELECTRONICS Vol.18 No.1

A sliding mode control (SMC) for servo motors based on the differential evolution (DE) algorithm, called DE-SMC, is proposed in this study. The parameters of SMC should be designed exactly to improve the robustness, realize the precision positioning, and reduce the steady-state speed error of the servo drive. The main parameters of SMC are optimized using the DE algorithm according to the speed feedback information of the servo motor. The most significant influence factor of the DE algorithm is optimization iteration. A suitable iteration can be achieved by the tested optimization process profile of the main parameters of SMC. Once the parameters of SMC are optimized under a convergent iteration, the system realizes the given performance indices within the shortest time. The experiment indicates that the robustness of the system is improved, and the dynamic and steady performance achieves the given performance indices under a convergent iteration when motor parameters mismatch and load disturbance is added. Moreover, the suitable iteration effectively mitigates the low-speed crawling phenomenon in the system. The correctness and effectiveness of DE-SMC are verified through the experiment.

Field development optimization in mature oil reservoirs using a hybrid algorithm

Yang, Hyungjun,Kim, Junyi,Choe, Jonggeun Elsevier 2017 Journal of petroleum science & engineering Vol.156 No.-

<P><B>Abstract</B></P> <P>Many optimization schemes have been proposed to simultaneously optimize various variables such as well locations, well operation schedules, well types, and the number of wells. However, most of these approaches often focused on fixed well type without considering conversion of existing wells.</P> <P>This paper proposes a new optimization for mature oil field development. Since converting from producers to injectors is a common practice in mature oil field, we have to optimize simultaneously type conversion schedules of all existing producers and infill wells as well as the number of infill wells, their locations, and operation schedules. We propose a new hybrid algorithm, which combines differential evolution (DE) algorithm and mesh adaptive direct search algorithm (MADS) to solve our optimization task.</P> <P>By considering well type conversion, it will increase the complexity of searching space but provide more realistic and optimal development plan. We demonstrate it in 2D synthetic and 3D PUNQ-S3 reservoirs for optimal field development. The proposed optimization considering well type conversion provides higher net present value than the fixed well type optimization in the both cases. The hybrid algorithm also shows better search performances than DE and MADS algorithms. Thus, we conclude that consideration of well conversion schedules is necessary for economical field development scenarios in mature oil fields.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We proposea new field development optimization formature oil reservoirs including well type conversion schedules. </LI> <LI> The proposedhybrid algorithm combines differential evolution (DE) algorithm and mesh adaptive direct search algorithm (MADS). </LI> <LI> The hybrid algorithm showsgoodperformancesin both 2D and 3D PUNQ-S3 mature oil reservoircasesby considering well type conversion. </LI> </UL> </P>

Research on LADRC strategy of PMSM for road-sensing simulation based on differential evolution algorithm

Zhang, Hui,Wang, Yuyuan,Zhang, Guowang,Tang, Conghui The Korean Institute of Power Electronics 2020 JOURNAL OF POWER ELECTRONICS Vol.20 No.4

A linear active disturbance rejection control (LADRC) strategy for permanent magnet synchronous motor (PMSM) for road-sensing based on the differential evolution (DE) algorithm is proposed in this paper, called DE-LADRC, to obtain the better dynamic and steady-state responses of the road-sensing simulation in electric vehicle (EV) steering-by-wire (SBW) systems. Since the novel control method ignores the time delay modules in digital motor control, the controlled object is regarded as a first-order inertial link to design a first-order LADRC controller. Then aiming to solve the problem where the values of the controller parameters are changed and difficult to tune due to ignoring the time delay modules in the first-order LADRC controller, the differential evolution (DE) algorithm is designed to find the optimal controller parameters by self-tuning. Experiment results indicate the effectiveness and convergence of the DE-LADRC, as well as the correctness of the road-sensing planning. In addition, the DE-LADRC can provide the smoother feel and real-time road-sensing for driver due to experiment of a Hardware in the Loop (HIL) platform under convergent iteration.

Adaptive Differential Evolution-based Receding Horizon Control Design for Multi-UAV Formation Reconfiguration

Boyang Zhang,Xiuxia Sun,Shuguang Liu,Xiongfeng Deng 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.12

The complicated and constrained reconfiguration optimisation for unmanned aerial vehicles (UAVs) is a challenge, particularly when multi-mission requirements are taken into account. In this study, we evaluate the use of the adaptive differential evolution-based centralised receding horizon control approach to achieve the formation reconfiguration along a given formation group trajectory for multiple unmanned aerial vehicles in a three-dimensional (3D) environment. A rolling optimisation approach which combines the receding horizon control method with the adaptive differential evolution algorithm is proposed, where the receding horizon control method divides the global control problem into a series of local optimisations and each local optimisation problem is solved by an adaptive differential evolution algorithm. Furthermore, a novel quadratic reconfiguration cost function with the topology information of UAVs is presented, and the asymptotic convergence of the rolling optimisation is analysed. Finally, simulation examples are provided to illustrate the validity of the proposed control structure.

최적 pRBFNNs 패턴분류기 기반 2차원 영상과 ASM 알고리즘을 이용한 얼굴인식 알고리즘 설계

오성권(Sung-Kwun Oh),마창민(Chang-Min Ma),유성훈(Sung-Hoon Yoo) 한국지능시스템학회 2011 한국지능시스템학회논문지 Vol.21 No.6

본 연구에서는 최적 pRBFNNs 패턴분류기 기반 2차원 영상과 ASM 알고리즘을 이용한 얼굴인식 시스템을 설계하고자 한다. 기존의 2차원 영상 기반 얼굴 인식 기법들은 인식하고자 하는 객체의 영상내의 위치, 크기 및 배경의 존재 유무에 따라 인식률이 영향을 받는 단점이 있으며, 본 연구에서는 이를 보완하기 위하여 관심 영역 내에서의 얼굴 영역 추출 및 특징 추출기법을 이용한 얼굴인식 방법을 소개한다. 본 연구에서는 CCD 카메라를 이용하여 영상을 획득하고 히스토그램 평활화를 이용하여 조명으로 왜곡된 영상정보를 개선한다. AdaBoost 알고리즘을 이용하여 얼굴영역을 검출하고 ASM을 통하여 얼굴 윤곽선 및 형상을 추출하여 개인 프로필을 구성한 후 PCA 알고리즘을 사용하여 고차원 얼굴데이터의 차원을 축소한다. 그리고 인식 모듈로서 pRBFNNs 패턴분류기를 제안한다. 제안된 다항식 기반 RBFNNs은 조건부, 결론부, 추론부 세 가지의 기능적 모듈로 구성되어 있고 조건부는 퍼지 클러스터링을 사용하여 입력 공간을 분할하고, 결론부는 분할된 로컬 영역을 다항식 함수로 표현한다. 또한 차분진화 알고리즘을 이용하여 제안된 분류기의 파라미터, 즉, 학습률, 모멘텀 계수, 퍼지 클러스터링의 퍼지화 계수를 최적화한다. 제안된 다항식 기반 RBFNNs는 얼굴 인식을 위한 패턴분류기로서 직접 CCD 카메라로부터 입력받은 데이터를 영상 보정, 얼굴 검출 및 특징 추출 등과 같은 데이터 전 처리 과정을 포함하여 고차원 데이터로 이루어진 얼굴 영상에 대한 인식 성능을 확인한다. In this study, we propose the design of optimized pRBFNNs-based face recognition system using two-dimensional Image and ASM algorithm. usually the existing 2 dimensional face recognition methods have the effects of the scale change of the image, position variation or the backgrounds of an image. In this paper, the face region information obtained from the detected face region is used for the compensation of these defects. In this paper, we use a CCD camera to obtain a picture frame directly. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. AdaBoost algorithm is used for the detection of face image between face and non-face image area. We can butt up personal profile by extracting the both face contour and shape using ASM(Active Shape Model) and then reduce dimension of image data using PCA. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of RBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to real-time face image database and then demonstrated from viewpoint of the output performance and recognition rate.

A Novel Hybrid Bat Algorithm with Differential Evolution Strategy for Constrained Optimization

Xianbing Meng,X. Z. Gao,Yu Liu 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.1

A novel hybrid Bat Algorithm (BA) with the Differential Evolution (DE) strategy using the feasibility-based rules, namely BADE is proposed to deal with the constrained optimization problems. The sound interferences induced by other things are inevitable for the bats which rely on the echolocation to detect and localize the things. Through integration of the DE strategy with BA, the insects’ interferences for the bats can be effectively mimicked by BADE. Moreover, the bats swarm’ mean velocity is simulated as the other bats’ effects on each bat. Having considered the living environments the bats inhabit, the virtual bats can be lifelike. Experiments on some benchmark problems and engineering designs demonstrate that BADE performs more efficient, accurate, and robust than the original BA, DE, and some other optimization methods.

A modified differential evolution algorithm for tensegrity structures

Do, D.T.T.,Lee, S.,Lee, J. Applied Science Publishers ; Elsevier 2016 COMPOSITE STRUCTURES -BARKING THEN OXFORD- Vol.158 No.-

In this paper, a novel modified differential evolution (mDE) algorithm for advanced form-finding of tensegrity structures is proposed to define an appropriate candidate for strut members. The form-finding process only requires topology and member type of a tensegrity based on the force density method. The proposed algorithm improved from original differential evolution (DE) is performed to reduce significant computational cost. In the mDE, scale factor F and crossover rate c are adjusted as well as the mutation and selection phases of the original DE are also replaced by the best individual-based mutation and elitist selection techniques. The objective function of the product of α and β related to eigenvalues and force densities is minimized. Since force density values are considered as continuous design variables, optimal solutions obtained by mDE are more accurate than those solved from discrete design variables of GA. Several benchmark numerical examples of two- and three-dimensional tensegrity structures are investigated to verify the effectiveness and robustness of the proposed algorithm by comparing obtained results with those of other methods in the literature.

An Evolutionary Optimized Algorithm Approach to Compensate the Non-linearity in Linear Variable Displacement Transducer Characteristics

Murugan, S.,Umayal, S.P. The Korean Institute of Electrical Engineers 2014 Journal of Electrical Engineering & Technology Vol.9 No.6

Linearization of transducer characteristic plays a vital role in electronic instrumentation because all transducers have outputs nonlinearly related to the physical variables they sense. If the transducer output is nonlinear, it will produce a whole assortment of problems. Transducers rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. Attempts have been made by many researchers to increase the range of linearity of transducers. This paper presents a method to compensate nonlinearity of Linear Variable Displacement Transducer (LVDT) based on Extreme Learning Machine (ELM) method, Differential Evolution (DE) algorithm and Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Because of the mechanism structure, LVDT often exhibit inherent nonlinear input-output characteristics. The best approximation capability of optimized ANN technique is beneficial to this. The use of this proposed method is demonstrated through computer simulation with the experimental data of two different LVDTs. The results reveal that the proposed method compensated the presence of nonlinearity in the displacement transducer with very low training time, lowest Mean Square Error (MSE) value and better linearity. This research work involves less computational complexity and it behaves a good performance for nonlinearity compensation for LVDT and has good application prospect.

An Evolutionary Optimized Algorithm Approach to Compensate the Non-linearity in Linear Variable Displacement Transducer Characteristics

S. Murugan,SP. Umayal 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.6

Linearization of transducer characteristic plays a vital role in electronic instrumentation because all transducers have outputs nonlinearly related to the physical variables they sense. If the transducer output is nonlinear, it will produce a whole assortment of problems. Transducers rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. Attempts have been made by many researchers to increase the range of linearity of transducers. This paper presents a method to compensate nonlinearity of Linear Variable Displacement Transducer (LVDT) based on Extreme Learning Machine (ELM) method, Differential Evolution (DE) algorithm and Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Because of the mechanism structure, LVDT often exhibit inherent nonlinear input-output characteristics. The best approximation capability of optimized ANN technique is beneficial to this. The use of this proposed method is demonstrated through computer simulation with the experimental data of two different LVDTs. The results reveal that the proposed method compensated the presence of nonlinearity in the displacement transducer with very low training time, lowest Mean Square Error (MSE) value and better linearity. This research work involves less computational complexity and it behaves a good performance for nonlinearity compensation for LVDT and has good application prospect.