In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

Optimization of constrained mathematical and engineering design problems using chaos game optimization

Introduces the auto disturbance rejection controller and its application on a Fast Following Synchronizer of Generator The numerical simulation shows that the controller ensures very good robustness under modeling uncertainty,and it is concluded that the proposed controller produces better dynamic performance such as small overshoot than the classic PID controller

Auto-disturbances-rejection Controller and it′s Application in Fast Following Synchronizer of Generators

The rapid development of battery technology has promoted the deployment of electric vehicles (EVs). To ensure the healthy and sustainable development of EVs, it is urgent to solve the problems of battery safety monitoring, residual value assessment, and predictive maintenance, which heavily depends on the accurate state-of-health (SOH) estimation of batteries. However, many published methods are unsuitable for actual vehicle conditions. To this end, a data-driven method based on the random partial charging process and sparse Gaussian process regression (GPR) is proposed in this article. First, the random capacity increment sequences (△Q) at different voltage segments are extracted from the partial charging process. The average value and standard deviation of △Q are used as features to indicate battery health. Second, correlation analysis is conducted for three types of batteries, and high correlations between the features and battery SOH are verified at different temperatures and discharging current rates. Third, by using the proposed features as inputs, sparse GPR models are constructed to estimate the SOH. Compared with other data-driven methods, the sparse GPR has the highest estimation accuracy, and its average maximum absolute errors are only 2.88%, 2.52%, and 1.51% for three different types of batteries, respectively.

Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data

Prognostics and health management (PHM) is developed to guarantee the safety and reliability of Lithium-ion (Li-ion) battery during operations. Due to the advantages of deep learning on nonlinear modeling and representation learning, it gains considerable attentions in the PHM of Li-ion battery. To provide a comprehensive view of deep learning-based PHM of Li-ion battery, this paper summarizes these applications on the basis of current research. Deep learning-based PHM of Li-ion battery roughly involves three steps, namely data acquisition, deep learning methods and performance evaluation. Firstly, regular data types and public datasets are introduced. Secondly, brief introductions of deep learning methods and their applications to PHM of Li-ion battery are summarized. These deep learning methods include autoencoder, deep neural network, deep belief network, convolutional neural network, recurrent neural network and generative adversarial network. Thirdly, commonly-used evaluation metrics are presented. Finally, the paper draws a conclusion and presents the prospects of PHM of Li-ion battery with deep learning techniques. • This paper summarizes applications of deep learning to the PHM of Li-ion battery. • This paper summarizes the typical data used in the PHM of Li-ion battery. • This paper summarizes the public datasets used in the PHM of Li-ion battery. • Future works of deep learning-based PHM of Li-ion battery are given in seven aspects. 

Prognostics and health management of Lithium-ion battery using deep learning methods: A review

Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries

This article proposes an enhanced active disturbance rejection control (ADRC) method for the attitude deformation system of a self-developed mobile robot in conjunction with evolutionary game theory-based pigeon-inspired optimization (EGPIO). To achieve precise control of the object, EGPIO is employed as an automatic tuning mechanism for ADRC, and simultaneously improves the rapidity and robustness of the controller. In this novel algorithm, individuals in pigeon-inspired optimization (PIO) adjust their strategies dynamically as the evolutionary game proceeds so as to promote the adaptability of the original PIO. Thus, the optimal solutions calculated by EGPIO will be adopted as the main parameters of ADRC to control the mobile robot. The effectiveness of the novel controller is validated by a series of simulations, including cases with normal conditions and situations with disturbances. Experimental results indicate that the scheme we designed possesses better control performances and shows attractive advantages for efficiency and fault tolerance compared with conventional ADRC.

Mobile Robot ADRC With an Automatic Parameter Tuning Mechanism via Modified Pigeon-Inspired Optimization

With the rapid development of technology and increase in performance requirements, the complexity of multi-legged robots has increased in terms of mechanical structures and control algorithms, which has led to challenges in guaranteeing the reliability of the systems. To achieve reliability requirements, stable control under the influence of disturbances and failures is critical for multi-legged mobile robot systems. Researchers have made efforts to promote the reliability and disturbance rejection ability of mobile robot control systems. Intelligent distributed control [3,17,22,34] and fault-tolerant control [8,23-25] are the main solutions. Christensen et al. [2] designed a distributed control strategy for self-reconfigurable modular robots. Cully et al. [5] proposed an intelligent self-adaption control method for a six-legged robot. The researchers attempted to use intelligent distribution methods to address interactions and information crossover between multiple actuators. In the literature [4], a series control scheme based on active disturbance rejection control (ADRC) Cui J, Ren Y, Xu B, YAng D, Zeng S. Reliability analysis of a multi-eSo based control strategy for level adjustment control system of quadruped robot under disturbances and failures. eksploatacja i niezawodnosc – Maintenance and Reliability 2020; 22 (1): 42–51, http:// dx.doi.org/10.17531/ein.2020.1.6.

http://www.ein.org.pl/sites/default/files/2020-01-06.pdf

Reliability analysis of a multi-eso based control strategy for level adjustment control system of quadruped robot under disturbances and failures

The survival signature, a new approach introduced by Coolen, has been used as an effective method to quantify the complex system’s reliability as it requires less computation cost than other algorithms. This paper develops a new method for computing the exact survival signature based on the system reliability block diagram by extending the universal generating function (UGF). And verification example and comparisons with enumerative method are presented for its correctness and efficiency.

A New Method for Computing Survival Signature Based on Extended Universal Generating Function

The invention discloses a double-e-index-model-based lithium battery capacity prediction method for random charging and discharging conditions. The method comprises the following steps: step one, carrying out data acquisition and noise reduction processing on a terminal voltage, a current and a capacity in a continuous charging and discharging process of a battery; step two, calculating equivalentcumulative transfer energy between the battery and equipment according to the collected voltage and current data, and drawing a degradation curve of the battery capacity with the equivalent cumulative transfer energy; step three, carrying out fitting on the degradation curve of the battery capacity with the equivalent cumulative transfer energy by using a double-e index model and acquiring parameters of the double-e index model based on a maximum likelihood estimation method; and step four, substituting the equivalent cumulative transfer energy value into the model based on the double-e indexmodel obtained by fitting in the step three and predicting a future battery capacity. The double-e-index-model-based lithium battery capacity prediction method is suitable for predicting the lithiumbattery capacity attenuation under random charging and discharging conditions. The method is simple and feasible with the high prediction precision and has the great practical application value.

Xu Binghui

Papers

Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries

Mobile Robot ADRC With an Automatic Parameter Tuning Mechanism via Modified Pigeon-Inspired Optimization

Reliability analysis of a multi-eso based control strategy for level adjustment control system of quadruped robot under disturbances and failures

A New Method for Computing Survival Signature Based on Extended Universal Generating Function

Double-e-index-model-based lithium battery capacity prediction method for random charging and discharging conditions