Due to the feasibility of the gray model for predicting time series with small samples, the gray theory is well investigated since it is presented and is currently evolved in an important manner for forecasting small samples. This study proposes a new gray prediction criterion based on the Neural Ordinary Differential Equation (NODE), which is named the NODGM (Neural Based Ordinary Differential gray Mode). This mode permits the forecasting approximation to be learned by a training process which contains a new whitening equation. It is needed to prepare the structure and time series, compared with other models, according to the regularity of actual specimens in advance, therefore this model of NODGM can provide comprehensive applications as well as learning the properties of distinct data specimens. In order to acquire a better model which has highly predictive efficiency, afterwards, this study trains the model by NODGM meanwhile using the Runge-Kutta method to obtain the prediction sequence and solve the model. The controller establishes an advantageous theoretical foundation in adapting to novel wheels and comprehensive spreads the utilize extent of Mechanical Elastic Vehicle Wheel (MEVW). 

Neural ordinary differential gray algorithm to forecasting nonlinear systems

To ensure the asymptotic stability and improve the ride comfort of vehicles, this paper develops a fuzzy neural network (NN) based evolved bat algorithm (EBA) to design adaptive backstepping controllers with grey signal predictors. The Lyapunov theory and backstepping method were employed to evaluate the nonlinearity of controlled systems and derive the evolved control law for the signal tracking. The discrete grey model DGM (2,1) is utilized to obtain future movement of the nonlinear system, so that the command controller can prove the Lyapunov stability and feasibility of the entire scheme through the Lyapunov-like lemma. The controller design criterion is demonstrated in the range of mechanical elastic wheel (MEW) and lays a feasible mathematical framework for the matching of new wheels.

Grey signal predictor and FNN evolved control for practical nonlinear systems


Purpose
To prove the effectiveness of the proposed design method, this study aims to propose the Fisher equation and temperature cooling fins that control high-speed aerospace vehicles.


Design/methodology/approach
A new approach whereby the control of aerospace vehicles can be achieved by fuzzy controller and appropriate Navier–Stokes equations in this article. The design of the controller based on models of Navier–Stokes equations simplified complex mathematical simulations and approximations.


Findings
If the fuzzy controller cannot stabilize the system, the Navier–Stokes fuzzy function is injected into the system as a controller tool, and the system is asymptotically stabilized by adjusting the fuzzy parameters.


Originality/value
The simulation results show that if the tuning frequency is high enough, the fuzzy controller and fuzzy observer can create chaotic movements by adjusting the dither amplitude appropriately. The demonstration of the Fisher equation and the temperature-cooled fin control problem for high-speed aerospace vehicles has displayed the benefits of combining fuzzy control with the Navier–Stokes equation.


Systematic fuzzy Navier–Stokes equations for aerospace vehicles

In this paper, a novel two-step nonlinear ground acceleration feedback mechanism is proposed for vibration mitigation of building structures considering soil-structure interaction. Hence, an observer based receding horizon control (RHC) scheme is presented for active control of the structures. Afterward, a nonlinear two-step auto-regressive framework is appended to the RHC formulations to consider the adaptive seismic term in the resulted control law. Soil-structure interaction (SSI) is also considered in the controller structure regarding soft, medium, and dense soil conditions. A data-driven approach is then introduced for lessening the complexity of the proposed framework for practical applications. A forty-story building equipped with active mass damper (AMD) subjected to earthquake excitations is selected as the numerical study. The superiority of the proposed method is evaluated compared to other control strategies including conventional RHC, ideal RHC, and linear version of the proposed controllers. The outcomes indicate the remarkable accuracy of the proposed method against mentioned control approaches. 

Nonlinear ground acceleration tracking mechanism for seismic control of high-rise buildings considering soil-structure interaction

This works aims to develop a new and improved GWO (Grey Wolf Optimizer), the so-called Robotic GWO (RGWO). First, to improve GWO's update formula position with an optimal learning strategy, we adapt the algorithm to real mobile environments, including robots, so that tracking robots can move prey toward targets. Then, the nonlinear active suspension (AS) control system is linearized by a neural network (NN) based linear differential inclusion (LDI) using feedback and feedforward linearization. In theory, it is found that the general SM (Sliding Mode) optimal control cannot provide sudden optimal results for the active linearized suspension system, so a method is proposed to improve the shortcomings of the active linearized suspension system. By constructing an extended SM-optimal manifold function, an improved SM-optimal controller is designed, which incorporates information on the entire structure and the expected performance of the suspension. For comparison purposes, the performance of three kinds of controls: SM optimal refinement control, logic-fuzzy SM control, and PS (passive suspension), shows the proposed controller's advantages . Finally, our improved SM optimal control for nonlinear AS systems, in general, can achieve the actual nominal optimal suspension performance, as confirmed by the simulation results. The results also show that the improved SM optimal control method provides better robustness even when the operating conditions or parameters of the structure vary.

/pdf/a-novel-robotic-gwo-ldi-modeling-and-control-for-nonlinear-3dvlzia6.pdf

A Novel Robotic GWO LDI Modeling and Control for Nonlinear Systems

In recent years, with the maximization of control design and efficiency, and the improvement of economy and energy efficiency, building technology and control in the theory have attracted the attention of lots researchers. By trying various control theorems, many numerical methods have been investigated in the literature to achieve this target, but all these numerical methods are difficult to work out the problem correctly. This paper puts forward a potentially feasible evolutionary bat algorithm (EB) method for active control of earthquake-induced vibration in building structures. Based disturbance observer based control and S surface combined with the robust adaptive control scheme for solving optimization problems proposed, an important contribution in the control law is what the configuration control in the present study should not require known uncertainty limits and the disturbance is eliminated. A simulation case study was proposed to illustrate the possibility of implementing an apparent learning method in ANN to effectively control structural vibration under the influence of systematic motion under earthquake citations. The proposed learning numerical methods does not need to develop a mathematical model of structural dynamics or train another neural network to approximate the actual structural response to be performed.

Smart structural control and analysis for earthquake excited building with evolutionary design

Grey Signal Predictor and Evolved Control for Practical Nonlinear Mechanical Systems

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

ZY Chen

Author Tools

Chat about Author

Papers

Smart structural control and analysis for earthquake excited building with evolutionary design

Grey Signal Predictor and Evolved Control for Practical Nonlinear Mechanical Systems

Grey signal predictor and FNN evolved control for practical nonlinear systems