• Journal of Drive and Control
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• v.17 no.1
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• pp.13-20
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• 2020

Adaptive neural networks based lateral controller is presented to guarantee path following performance for vehicle lane keeping in the presence of parameter time-varying characteristics of the vehicle lateral dynamics due to the road surface condition, load distribution, tire pressure and so on. The proposed adaptive controller could compensate vehicle lateral dynamics deviated from nominal dynamics resulting from parameter variations by incorporating it with neural networks that have the ability to approximate any given nonlinear function by adjusting weighting matrices. The controller is derived by using Lyapunov-based approach, which provides adaptive update rules for weighting matrices of neural networks. To show the superiority of the presented adaptive neural networks controller, the simulation results are given while comparing with backstepping controller chosen as the baseline controller. According to the simulation results, it is shown that the proposed controller can effectively keep the vehicle tracking the pre-given trajectory in high velocity and curvature with much accuracy under parameter variations.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.3
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• pp.11-18
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• 1996

Liquid droplets of water and argon surrounded by their vapor have been simulated by the milecular dynamics method. To explore the surface phenomena of clusters, each molecule is classified into 'liquid', 'surface', or 'vapor' with respect to the number of neighbor molecules. The contribution of a 'surface' molecule of the water cluster to the far infrared spectrum is almist the same as that of the 'liquid' molecule. Hence, the liquid-vapor interface is viewed as geometrically and temporally varying boundary of 'liquid' molecules with only a single layer of 'surface' molecules that might have different characteristics from the 'liquid' molecules. The time scale of the 'phase change' of each molecule is estimated for the argon cluster by observing the instantancous kinetic and potential energies of each molecule. To compare the feature of clusters with macroscopic droplets, the temperature dependence of the surface tension of the argon cluster is estimated.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.961-972
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• 2008

In this paper, we consider the global exponential stability of cellular neural networks with time-varying delays. Based on the Lyapunov function method and convex optimization approach, a novel delay-dependent criterion of the system is derived in terms of LMI (linear matrix inequality). In order to solve effectively the LMI convex optimization problem, the interior point algorithm is utilized in this work. Two numerical examples are given to show the effectiveness of our results.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.237-254
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• 2019

This paper presents an improved time series based damage detection approach with experimental verifications for detection, localization, and quantification of damage in shear-type structures under varying mass effects using output-only vibration data. The proposed method can be very effective for automated monitoring of buildings to develop proactive maintenance strategies. In this method, Auto-Regressive Moving Average models with eXogenous inputs (ARMAX) are built to represent the dynamic relationship of different sensor clusters. The damage features are extracted based on the relative difference of the ARMAX model coefficients to identify the existence, location and severity of damage of stiffness and mass separately. The results from a laboratory-scale shear type structure show that different damage scenarios are revealed successfully using the approach. At the end of this paper, the methodology limitations are also discussed, especially when simultaneous occurrence of mass and stiffness damage at multiple locations.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.898-906
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• 1999

A control structure is introduced for the purpose of rejecting periodic (or repetitive) disturbances on a tracking system. The objective of the proposed structure is to drive the output of the system to the reference input that will result in perfect following without any changing the inner configuration of the system. The structure includes an adaptation block which learns the dynamics of the periodic disturbance and forces the interferences, caused by disturbances, on the output of the system to be reduced. Since the control structure acquires the dynamics of the disturbance by on-line adaptation, it is possible to generate control signals that reject any slowly varying time-periodic disturbance provided that its amplitude is bounded. The artificial neural network is adopted as the adaptation block. The adaptation is done at an on-line process. For this , the real-time recurrent learning (RTRL) algoritnm is applied to the training of the artificial neural network.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.2
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• pp.152-161
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• 2013

This paper presents a control effectiveness analysis of the hawkmoth Manduca sexta. A multibody dynamic model of the insect that considers the time-varying inertia of two flapping wings is established, based on measurement data from the real hawkmoth. A six-degree-of-freedom (6-DOF) multibody flight dynamics simulation environment is used to analyze the effectiveness of the control variables defined in a wing kinematics function. The aerodynamics from complex wing flapping motions is estimated by a blade element approach, including translational and rotational force coefficients derived from relevant experimental studies. Control characteristics of flight dynamics with respect to the changes of three angular degrees of freedom (stroke positional, feathering, and deviation angle) of the wing kinematics are investigated. Results show that the symmetric (asymmetric) wing kinematics change of each wing only affects the longitudinal (lateral) flight forces and moments, which implies that the longitudinal and lateral flight controls are decoupled. However, there are coupling effects within each plane of motion. In the longitudinal plane, pitch and forward/backward motion controls are coupled; in the lateral plane, roll and side-translation motion controls are coupled.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.663-668
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• 1990

This paper deals with the application of a Generalized Predictive Control (CPC) to a Pressurized Water Reactor (P.W.R) Nuclear Power Plant. Generalized Predictive Control is a sort of Explicit Self-Tuning Control. Current self-tuning algorithms lack robustness to prior choices of either dead-time (input time delay of a plant) or model order. GPC is shown by simulation studies to be superior to accepted self-tuning techniques such as minimum variance and pole-placement from the viewpoint that it is robust to prior choices of dead-time or model order. In this paper a GPC controller is designed to control the P.W.R. nuclear power rlant with varying dead-time and through the designing procedure the designer is free from the constraint of knowing the exact dead-time. The controller is constructed based on the 2nd order linear model approximated in the vicinity of operating point. To ensure that this low-order model describes the complex real dynamics well enough for control purposes, model parameters are updated on-line with a Recursive Least Squares algorithm. Simulation results are successful and show the possibilities of the GPC control application to actual plants with varying or unknown dead-time.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.195-205
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• 2013

The analysis of electroencephalographic signal associated with the attention is essential for the understanding of human cognition. In this paper, the characteristic differences between the attention and inattention status in the brain were inspected by nonlinear analysis. The recurrence quantification analysis was applied to the relatively small number of samples of evoked potential having time varying characteristics, where the recurrence plot (RP), the color recurrence plot (CRP), and mean and time-sequential trend parameters were extracted. The dimension and the time delay in phase transformation can be determined by the paired set of extracted parameters. It is observed from RP, CRP, and parameters that the brain dynamics in attention is more complex than that in the inattention, as well as the synchronized brain response is stable in the mean sense but locally time varying. It is feasible that the non-linear analysis method can be useful for the analysis of complex brain dynamics associated during visual attentional task.

• Journal of Drive and Control
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• v.15 no.3
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• pp.21-28
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• 2018

We propose a tension controller based on a time-delay estimation (TDE) technique for a winding machine. Firstly, we perform the necessary calculations to derive a mathematical model of the winding machine. In this sense, it is revealed that the roll radius of the winding machine is characteristically seen to be increasing or decreasing during the winding process. That being said, it is noted that the parameters of the winding machine are coupled and constantly changing during this process. Understandably then, it is noted that the model is shown to be nonlinear and time-varying. Secondly, we propose the way to apply the TDE based controller which is the so-called Time-delay Control (TDC). The TDC utilizes the time-delayed information intentionally to compensate the nonlinear and time-varying characteristics. As we have seen, the proposed controller consists of two parts: one is a TDE component, and the other is an error dynamics component which is defined by a user. In a computer simulation based on the Matlab/Simulink program, the proposed controller is compared with a conventional PID controller, which is widely used in the tension control of the winding machine. The proposed controller reduces the incidence of overshoot and steady-state error in the tension control, as compared to the conventional PID controller.

• Journal of Agricultural Extension & Community Development
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• v.24 no.2
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• pp.99-115
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• 2017

The purpose of this study is to analyze potential adoption rates and reusing patterns of the new rice direct seeding technology. The model constructed and employed in this study is a system dynamics model of farmer adoption and reusing patterns for this new technology over time. The model incorporates a causal loop diagram that explains interactions among rice cultivation subsystems with feedback loops and further attempts to build a causal loop model with stock-flow diagram for computer simulation. As one example of how the model can be used to provide insight to rice farmers, simulations are run over varying levels on the cultivation process of rice. The major finding is to demonstrate the utility of system dynamics simulation methodology in aiding the rice wet direct seeding farmers' decision making.