An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

http://www.cs.unibo.it/~montesi/CBD/Articoli/SurvyBigData2.pdf

Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

Fuzzy logic control was originally introduced and developed as a model free control design approach. However, it unfortunately suffers from criticism of lacking of systematic stability analysis and controller design though it has a great success in industry applications. In the past ten years or so, prevailing research efforts on fuzzy logic control have been devoted to model-based fuzzy control systems that guarantee not only stability but also performance of closed-loop fuzzy control systems. This paper presents a survey on recent developments (or state of the art) of analysis and design of model based fuzzy control systems. Attention will be focused on stability analysis and controller design based on the so-called Takagi-Sugeno fuzzy models or fuzzy dynamic models. Perspectives of model based fuzzy control in future are also discussed

/pdf/a-survey-on-analysis-and-design-of-model-based-fuzzy-control-3hrjlrel0k.pdf

A Survey on Analysis and Design of Model-Based Fuzzy Control Systems

Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

Computational Analysis of Microarray Data

Nonsmooth nonlinear systems can model many practical processes with discontinuous property and are difficult to be stabilized by classical control methods like smooth nonlinear systems. This article considers the output-feedback adaptive neural network (NN) control problem for nonsmooth nonlinear systems with input deadzone and saturation. First, the nonsmooth input deadzone and saturation is converted to a smooth function of affine form with bounded estimation error by means of the mean-value theorem. Second, with the help of approximation theorem and Filippov's differential inclusion theory, the given nonsmooth system is converted to an equivalent smooth system model. Then, by introducing a proper logarithmic barrier Lyapunov function (BLF), an output-feedback adaptive NN strategy is set up by constructing an appropriate observer and adopting the adaptive backstepping technique. A new stability criterion is established to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, comparative simulations through Chua's oscillator are offered to verify the effectiveness of the proposed control algorithm.

Output-Feedback Adaptive Neural Network Control for Uncertain Nonsmooth Nonlinear Systems With Input Deadzone and Saturation.

In this paper, the simultaneous static output feedback stabilization problem for a collection of discrete‐time interval systems is considered. A sufficient condition for the existence of static output feedback simultaneously stabilizing controllers is obtained. It is shown that this problem is solvable if a matrix's spectral norm assignment problem is solvable. We proved that the admissible solution set of the matrix's spectral norm assignment problem is convex. Then, the matrix's spectral norm assignment problem is shown to be equivalent to a linear matrix inequality (LMI) feasibility problem. Finally, an example is provided to illustrate the proposed methodology.

Lmi approach to simultaneous output-feedback stabilization for discrete-time interval systems

It is well-known that Self-constructing neural fuzzy inference network (SONFIN) is a nice fuzzy inference system that has been equipped with structure learning capability. For the learning mechanisms, SONFIN can either be employed with the adaptive learning algorithm from neural network, which is often called backpropagation (BP) learning algorithm or use the Recursive Least Squares (RLS) algorithm in finding the parameters in the consequence part. In this paper, we reported the analysis on the use of RLS algorithm for neural fuzzy systems under the structure of SONFIN. Such a RLS algorithm is originally proposed to learn parameters in the consequence part only for TSK fuzzy systems. RLS has been demonstrated to be capable of providing great learning performance to neural fuzzy systems. From our previous work, it can be observed that the advantages of using RLS over BP and various issues, such as forgetting factor or reset operation are also investigated. All the above studies are based on the use of the full covariance matrixin the RLS algorithm. However, such an approach may result in heavy computational burden especially when the rule number is large. An alternative approach is to neglect the correlation among rules. In this study, we will report our analyses on the effects of correlation among rules. In order to have a clear demonstration on those effects, some special designs for the system are considered. From our study, it is clearly evidence that such neglect may results in large errors.

Learning analysis for correlation of fuzzy rules in applying RLS for neural fuzzy systems

Adaptive quantized control for uncertain nonlinear systems with unknown control directions

The uncertainty due to road fluctuations and vision system dynamics represents a big challenge to adjusting the steering angle of autonomous vehicles (AVs). Furthermore, AVs require fast action to follow the target lane to overcome lateral deviation with minor errors. In this regard, this paper introduces a fast model predictive controller formulated based on the discrete-time Laguerre function (DTLF) to overcome the high computational burden of the traditional MPC. To improve the hybrid DTLF-MPC performance, a modern and effective dandelion optimizer (DO) strategy is used in this work, which resulted in obtaining the optimal DTLF-MPC parameters and achieving satisfactory results. Furthermore, the proposed hybrid DTLF-MPC is designed based on different algorithms in the literature to evaluate the performance of the DO. Several scenarios are discussed in this paper to confirm the effectiveness and efficiency of the proposed control strategy system against the vision system uncertainty and road fluctuations. The results emphasize that the proposed DTLF-MPC based on the DO can achieve the best damping performance for the lateral deviations with less overshoot; around 0.4533, and a settling time of around 0.01979 s compared with other algorithms.

https://www.mdpi.com/2227-7390/10/23/4539/pdf?version=1669879343

Shun-Feng Su

Papers

Output-Feedback Adaptive Neural Network Control for Uncertain Nonsmooth Nonlinear Systems With Input Deadzone and Saturation.

Lmi approach to simultaneous output-feedback stabilization for discrete-time interval systems

Learning analysis for correlation of fuzzy rules in applying RLS for neural fuzzy systems

Adaptive quantized control for uncertain nonlinear systems with unknown control directions

Model Predictive Paradigm with Low Computational Burden Based on Dandelion Optimizer for Autonomous Vehicle Considering Vision System Uncertainty