• Title/Summary/Keyword: Evolved Bat Algorithm

Search Result 6, Processing Time 0.02 seconds

Structural system simulation and control via NN based fuzzy model

  • Tsai, Pei-Wei;Hayat, T.;Ahmad, B.;Chen, Cheng-Wu
    • Structural Engineering and Mechanics
    • /
    • v.56 no.3
    • /
    • pp.385-407
    • /
    • 2015
  • This paper deals with the problem of the global stabilization for a class of tension leg platform (TLP) nonlinear control systems. It is well known that, in general, the global asymptotic stability of the TLP subsystems does not imply the global asymptotic stability of the composite closed-loop system. Finding system parameters for stabilizing the control system is also an issue need to be concerned. In this paper, we give additional sufficient conditions for the global stabilization of a TLP nonlinear system. In particular, we consider a class of NN based Takagi-Sugeno (TS) fuzzy TLP systems. Using the so-called parallel distributed compensation (PDC) controller, we prove that this class of systems can be globally asymptotically stable. The proper design of system parameters are found by a swarm intelligence algorithm called Evolved Bat Algorithm (EBA). An illustrative example is given to show the applicability of the main result.

LDI NN auxiliary modeling and control design for nonlinear systems

  • Chen, Z.Y.;Wang, Ruei-Yuan;Jiang, Rong;Chen, Timothy
    • Smart Structures and Systems
    • /
    • v.29 no.5
    • /
    • pp.693-703
    • /
    • 2022
  • This study investigates an effective approach to stabilize nonlinear systems. To ensure the asymptotic nonlinear stability in nonlinear discrete-time systems, the present study presents controller for an EBA (Evolved Bat Algorithm) NN (fuzzy neural network) in the algorithm. In fuzzy evolved NN modeling, the auxiliary circuit with high frequency LDI (linear differential inclusions) and NN model representation is developed for the nonlinear arbitrary dynamics. An example is utilized to demonstrate the system more robust compared with traditional control systems.

Composite components damage tracking and dynamic structural behaviour with AI algorithm

  • Chen, Z.Y.;Peng, Sheng-Hsiang;Meng, Yahui;Wang, Ruei-Yuan;Fu, Qiuli;Chen, Timothy
    • Steel and Composite Structures
    • /
    • v.42 no.2
    • /
    • pp.151-159
    • /
    • 2022
  • This study discusses a hypothetical method for tracking the propagation damage of Carbon Reinforced Fiber Plastic (CRFP) components underneath vibration fatigue. The High Cycle Fatigue (HCF) behavior of composite materials was generally not as severe as this of admixture alloys. Each fissure initiation in metal alloys may quickly lead to the opposite. The HCF behavior of composite materials is usually an extended state of continuous degradation between resin and fibers. The increase is that any layer-to-layer contact conditions during delamination opening will cause a dynamic complex response, which may be non-linear and dependent on temperature. Usually resulted from major deformations, it could be properly surveyed by a non-contact investigation system. Here, this article discusses the scanning laser application of that vibrometer to track the propagation damage of CRFP components underneath fatigue vibration loading. Thus, the study purpose is to demonstrate that the investigation method can implement systematically a series of hypothetical means and dynamic characteristics. The application of the relaxation method based on numerical simulation in the Artificial Intelligence (AI) Evolved Bat (EB) strategy to reduce the dynamic response is proved by numerical simulation. Thermal imaging cameras are also measurement parts of the chain and provide information in qualitative about the temperature location of the evolution and hot spots of damage.

System simulation and synchronization for optimal evolutionary design of nonlinear controlled systems

  • Chen, C.Y.J.;Kuo, D.;Hsieh, Chia-Yen;Chen, Tim
    • Smart Structures and Systems
    • /
    • v.26 no.6
    • /
    • pp.797-807
    • /
    • 2020
  • Due to the influence of nonlinearity and time-variation, it is difficult to establish an accurate model of concrete frame structures that adopt active controllers. Fuzzy theory is a relatively appropriate method but susceptible to human subjective experience to decrease the performance. This paper proposes a novel artificial intelligence based EBA (Evolved Bat Algorithm) controller with machine learning matched membership functions in the complex nonlinear system. The proposed affine transformed membership functions are adopted and stabilization and performance criterion of the closed-loop fuzzy systems are obtained through a new parametrized linear matrix inequality which is rearranged by machine learning affine matched membership functions. The trajectory of the closed-loop dithered system and that of the closed-loop fuzzy relaxed system can be made as close as desired. This enables us to get a rigorous prediction of stability of the closed-loop dithered system by establishing that of the closed-loop fuzzy relaxed system.

Adaptive backstepping control with grey theory for offshore platforms

  • Hung, C.C.;Nguyen, T.
    • Ocean Systems Engineering
    • /
    • v.12 no.2
    • /
    • pp.159-172
    • /
    • 2022
  • To ensure stable performance, adaptive regulators with new theories are designed for steel-covered offshore platforms to withstand anomalous wave loads. This model shows how to control the vibration of the ocean panel as a solution using new results from Lyapunov's stability criteria, an evolutionary bat algorithm that simplifies computational complexity and utilities. Used to reduce the storage space required for the method. The results show that the proposed operator can effectively compensate for random delays. The results show that the proposed controller can effectively compensate for delays and random anomalies. The improved prediction method means that the vibration of the offshore structure can be significantly reduced. While maintaining the required controllability within the ideal narrow range.

Fuzzy neural network controller of interconnected method for civil structures

  • Chen, Z.Y.;Meng, Yahui;Wang, Ruei-yuan;Chen, Timothy
    • Advances in concrete construction
    • /
    • v.13 no.5
    • /
    • pp.385-394
    • /
    • 2022
  • Recently, an increasing number of cutting-edged studies have shown that designing a smart active control for real-time implementation requires piles of hard-work criteria in the design process, including performance controllers to reduce the tracking errors and tolerance to external interference and measure system disturbed perturbations. This article proposes an effective artificial-intelligence method using these rigorous criteria, which can be translated into general control plants for the management of civil engineering installations. To facilitate the calculation, an efficient solution process based on linear matrix (LMI) inequality has been introduced to verify the relevance of the proposed method, and extensive simulators have been carried out for the numerical constructive model in the seismic stimulation of the active rigidity. Additionally, a fuzzy model of the neural network based system (NN) is developed using an interconnected method for LDI (linear differential) representation determined for arbitrary dynamics. This expression is constructed with a nonlinear sector which converts the nonlinear model into a multiple linear deformation of the linear model and a new state sufficient to guarantee the asymptomatic stability of the Lyapunov function of the linear matrix inequality. In the control design, we incorporated H Infinity optimized development algorithm and performance analysis stability. Finally, there is a numerical practical example with simulations to show the results. The implication results in the RMS response with as well as without tuned mass damper (TMD) of the benchmark building under the external excitation, the El-Centro Earthquake, in which it also showed the simulation using evolved bat algorithmic LMI fuzzy controllers in term of RMS in acceleration and displacement of the building.