• Geomechanics and Engineering
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• v.31 no.4
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• pp.339-352
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• 2022

Studying slope stability is an important branch of civil engineering. In this way, engineers have employed machine learning models, due to their high efficiency in complex calculations. This paper examines the robustness of various novel optimization schemes, namely equilibrium optimizer (EO), Harris hawks optimization (HHO), water cycle algorithm (WCA), biogeography-based optimization (BBO), dragonfly algorithm (DA), grey wolf optimization (GWO), and teaching learning-based optimization (TLBO) for enhancing the performance of adaptive neuro-fuzzy inference system (ANFIS) in slope stability prediction. The hybrid models estimate the factor of safety (F_S) of a cohesive soil-footing system. The role of these algorithms lies in finding the optimal parameters of the membership function in the fuzzy system. By examining the convergence proceeding of the proposed hybrids, the best population sizes are selected, and the corresponding results are compared to the typical ANFIS. Accuracy assessments via root mean square error, mean absolute error, mean absolute percentage error, and Pearson correlation coefficient showed that all models can reliably understand and reproduce the F_S behavior. Moreover, applying the WCA, EO, GWO, and TLBO resulted in reducing both learning and prediction error of the ANFIS. Also, an efficiency comparison demonstrated the WCA-ANFIS as the most accurate hybrid, while the GWO-ANFIS was the fastest promising model. Overall, the findings of this research professed the suitability of improved intelligent models for practical slope stability evaluations.

• Smart Structures and Systems
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• v.26 no.3
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• pp.263-275
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• 2020

This research is dedicated to slope stability analysis using novel intelligent models. By coupling a neural network with spotted hyena optimizer (SHO), salp swarm algorithm (SSA), shuffled frog leaping algorithm (SFLA), and league champion optimization algorithm (LCA) metaheuristic algorithms, four predictive ensembles are built for predicting the factor of safety (FOS) of a single-layer cohesive soil slope. The data used to develop the ensembles are provided from a vast finite element analysis. After creating the proposed models, it was observed that the best population size for the SHO, SSA, SFLA, and LCA is 300, 400, 400, and 200, respectively. Evaluation of the results showed that the combination of metaheuristic and neural approaches offers capable tools for estimating the FOS. However, the SSA (error = 0.3532 and correlation = 0.9937), emerged as the most reliable optimizer, followed by LCA (error = 0.5430 and correlation = 0.9843), SFLA (error = 0.8176 and correlation = 0.9645), and SHO (error = 2.0887 and correlation = 0.8614). Due to the high accuracy of the SSA in properly adjusting the computational parameters of the neural network, the corresponding FOS predictive formula is presented to be used as a fast yet accurate substitution for traditional methods.

• The Journal of Natural Sciences
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• v.8 no.1
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• pp.5-15
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• 1995

The equation error formulation in the adaptive IIR filtering provides convergence to a global minimum regardless a local minimum with a large stability margin. However, the equation error formulation suffers from the bias in the coefficient estimates. In this paper, a new algorithm, which does not require a prespecification of the noise variance, is proposed for the equation error formulation. This algorithm is based on the equation error smoothing and provides an unbiased parameter estimate in the presence of white noise. Through simulations, it is demonstrated that the algorithm eliminates the bias in the parameter estimate while retaining good properties of the equation error formulation such as fast convergence speed and the large stability margin.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.780-785
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• 2004

The work presented here deals with controller design by graphical method based on proportional control ramp response. The design aims at the improvement of transient response, disturbance rejection capability, steady-state error reduction with stability preservation. The first step is to generate tracking-error curve with proportional control only and decide the added error signal shape on the error curve. The effectiveness of the proposed controller is confirmed through the simulation and experiment.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1981-1986
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• 2004

In this study, the basic motor control system had been investigated. The controller for this study consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such a tracking perfect, an adaptive law based on Feedback Error Learning (FEL) is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The theory in $H^{\infty}$-Control is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.689-701
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• 2008

In this paper, a fully adaptive feedforward feedback synchronized tracking control approach is developed for precision tracking control of 6 degree of freedom (6DOF) Stewart Platform. The proposed controller is designed in decentralized form for implementation simplicity. Interconnections among different subsystems and gravity effect are eliminated by the feedforward control action. Feedback control action guarantees the stability of the system. The gains of the proposed controller can be updated on line without requiring any prior knowledge of Stewart Platform manipulator. Thus the control approach is claimed to be fully adaptive. By employing cross-coupling error technology, the proposed approach can guarantee both of position error and synchronization error converge to zero asymptotically. Because the actuators work in synchronous manner, the tracking performances are improved. The corresponding stability analysis is also presented in this paper. Finally, simulation is demonstrated to verify the effectiveness of the proposed approach.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.4
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• pp.530-536
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• 1993

In adaptive antenna, recently Gooch suggested a new adaptive system using equation error method, but the system demands inverse model about the pole part and thus does not guarantee stability. In this paper, algorithm is proposed that has a basis on Popov's extra-stability theory. And system is developed of output error method. In addition, the result obtained by applying proposed algorithm to system of output error method is compared with that of Gooch model.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.5
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• pp.72-78
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• 2006

The paper proposes reference model error feedback control scheme for motion control system with hard non-linear components as like saturation and dead-zone in plant input part. Additionally, the plant has the system uncertainty effected by plant model parameter deviation and disturbance. The control algorithm uses the reference model to apply additional feedback loop with the error between reference model output and actual output effected by disturbance and non-linear components. And the stability evaluation based on Popov stability and controller design method are formulated to be performed. The effectiveness of the proposed scheme is examined by simulations. The results are proven by reasonable performances following reference model responses with good disturbance rejection performance without over-tuning of controller.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2E
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• pp.56-62
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• 2002

This paper describes a new method of stability evaluation for an estimated impulse response of a plant. It is difficult for the conventional stability evaluation equation to be used in an adaptive feedforward control system which uses an immeasurable acoustic transfer system of a real plant, because the equation requires an exact true impulse response of the plant. Therefore, the usefulness of the conventional equation is limited in a computer simulation. The proposed method is applicable to not only a computer simulation but also a real feedforward adaptive control system. It is found that the system is stable when the value of misadjustment is below -10 dB through computer simulations and experiments. And also, it is proved that the error signal is stable through the verification using filtered reference and filtered error LMS methods.

• Journal of Ocean Engineering and Technology
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• v.13 no.3B
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• pp.99-105
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• 1999

In order to reject the steady-state tracking error, it is common to introduce integral compensators in servosystems for constant reference signals. However, if the mathematical model of the plant is exact and no disturbance input exists, the integral compensation is not necessary. From this point of view, a two-degree-of-freedom(2DOF) servosystem has been proposed, in which the integral compensation is effective only when there is a modeling error or a disturbance input. The present paper considers a robust stability of this 2DOF servosystem with nonlinear type uncertainty in the system, and a robust stability condition for the servosystem is introduced. This result guarantees that if the plant uncertainty is in the permissible set defined by the condition, gain tuning can be carried out to suppress the influence of the plant uncertainties and disturbance inputs.