• Title/Summary/Keyword: Continuous Parameter Genetic Algorithm

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LQG modeling and GA control of structures subjected to earthquakes

  • Chen, ZY;Jiang, Rong;Wang, Ruei-Yuan;Chen, Timothy
    • Earthquakes and Structures
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    • v.22 no.4
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    • pp.421-430
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    • 2022
  • This paper addresses the stochastic control problem of robots within the framework of parameter uncertainty and uncertain noise covariance. First of all, an open circle deterministic trajectory optimization issue is explained without knowing the unequivocal type of the dynamical framework. Then, a Linear Quadratic Gaussian (LQG) controller is intended for the ostensible trajectory-dependent linearized framework, to such an extent that robust hereditary NN robotic controller made out of the Kalman filter and the fuzzy controller is blended to ensure the asymptotic stability of the non-continuous controlled frameworks. Applicability and performance of the proposed algorithm shown through simulation results in the complex systems which are demonstrate the feasible to improve the performance by the proposed approach.

Temperature Control of a CSTR using a Nonlinear PID Controller (비선형 PID 제어기를 사용한 CSTR의 온도 제어)

  • Lee, Joo-Yeon;So, Gun-Baek;Lee, Yun-Hyung;So, Myung-Ok;Jin, Gang-Gyoo
    • Journal of Institute of Control, Robotics and Systems
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    • v.21 no.5
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    • pp.482-489
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    • 2015
  • CSTR (Continuous Stirred Tank Reactor) which plays a key role in the chemical plants exhibits highly nonlinear behavior as well as time-varying behavior during operation. The control of CSTRs in the whole operating range has been a challenging problem to control engineers. So, a variety of feedback control forms and their tuning methods have been implemented to guarantee the satisfactory performance. This paper presents a scheme of designing a nonlinear PID controller incorporating with a GA (Genetic Algorithm) for the temperature control of a CSTR. The gains of the NPID controller are composed of easily implementable nonlinear functions based on the error and/or the error rate and its parameters are tuned using a GA by minimizing the ITAE (Integral of Absolute Error). Simulation works for reference tracking and disturbance rejecting performances and robustness to parameter changes show the feasibility of the proposed method.

A Design on Robust Servo Controller Using ${\delta}$ - Operator (${\delta}$ - 연산자를 이용한 강인한 서보 제어기의 설계)

  • Hwang, Hyun-Joon;Kim, Jeong-Tek
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.2602-2604
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    • 2000
  • In this paper, we study robust linear optimal model following servo system in the presence of disturbances and parameter perturbations. A technique to directly design the generalized differential operator based unified control system that covers both differential operator based continuous time and delta operator based discrete time case is presented. The quadratic criterion function for a linear system is used to design the robust unified servo control system. This servo control system is designed by applying a simple genetic algorithm to follow the output of the reference model optimally. The characteristics of the proposed servo system are analysed and simulated to verify the robustness.

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Chemical Oxygen Demand (COD) Model for the Assessment of Water Quality in the Han River, Korea (한강수질 평가를 위한 COD (화학적 산소 요구량) 모델 평가)

  • Kim, Jae Hyoun;Jo, Jinnam
    • Journal of Environmental Health Sciences
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    • v.42 no.4
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    • pp.280-292
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    • 2016
  • Objectives: The objective of this study was to build COD regression models for the Han River and evaluate water quality. Methods: Water quality data sets for the dry season (as of January) during a four-year period (2012-2015) were collected from the database of the Han River automatic water quality monitoring stations. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR) were used to build five-descriptor COD models. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) are useful tools for extracting meaningful information. Results: The $r^2$ of the best COD models provided significant high values (> 0.8) between 2012 and 2015. Total organic carbon (TOC) was a surrogate indicator for COD (as COD/TOC) with high reliability ($r^2=0.63$ in 2012, $r^2=0.75$ for 2013, $r^2=0.79$ for 2014 and $r^2=0.85$ for 2015). The ratios of COD/TOC were calculated as 2.08 in 2012, 1.79 in 2013, 1.52 and 1.45 in 2015, indicating that biodegradability in the water body of the Han River was being sustained, thereby further improving water quality. The BOD/COD ratio supported these findings. The cluster analysis revealed higher annual levels of microorganisms and phosphorous at stations along the Hangang-Seoul and Hantangang areas. Nevertheless, the overall water quality over the last four years showed an observable trend toward continuous improvement. These findings also suggest that non-point pollution control strategies should consider the influence of upstreams and downstreams to protect water quality in the Han River. Conclusion: This data analysis procedure provided an efficient and comprehensive tool to interpret complex water quality data matrices. Results from a trend analysis provided much important information about sources and parameters for Han River water quality management.

Structural system identification by measurement error-minimization observability method using multiple static loading cases

  • Lei, Jun;Lozano-Galant, Jose Antonio;Xu, Dong;Zhang, Feng-Liang;Turmo, Jose
    • Smart Structures and Systems
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    • v.30 no.4
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    • pp.339-351
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    • 2022
  • Evaluating the current condition of existing structures is of primary importance for economic and safety reasons. This can be addressed by Structural System Identification (SSI). A reliable static SSI depends on well-designed sensor configuration and loading cases, as well as efficient parameter estimation algorithms. Static SSI by the Measurement Error-Minimizing Observability Method (MEMOM) is a model-based deterministic static SSI method that could estimate structural parameters from static responses. In the current state of the art, this method is only applicable when structures are subjected to one loading case. This might lead to lack of information in some local regions of the structure (such as the null curvatures zones). To address this issue, the SSI by MEMOM using multiple loading cases is proposed in this work. Observability equations obtained from different loading cases are concatenated simultaneously and an optimization procedure is introduced to obtain the estimations by minimizing the discrepancy between the predicted response and the measured one. In addition, a Genetic-Algorithm (GA)-based Optimal Sensor Placement (OSP) method is proposed to tackle the OSP problem under multiple static loading cases for the very first time. In this approach, the Fisher Information Matrix (FIM)'s determinant is used as the metric of the goodness of sensor configurations. The numerical examples of a 3-span continuous bridge and a 13-story frame, are analyzed to validate the applicability of the extended SSI by MEMOM and the GA-based OSP method.