• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.382-385
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• 2003

In this paper, in order to improve the control performance of piezoelectric actuator, an integrated control structure is proposed. The control structure consists of inverse hysteresis model , to compensate the hysteresis nonlinearty problem, and feedforward - feedback controller to give a good tracking performance. The inverse hysteresis model and neural network are used as feed-forward controller, and PID controller is used as a feedback controller. From diverse experiments it is concluded that the proposed control scheme gives good tracking performance than the classical control does.

• International Journal of Automotive Technology
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• v.7 no.6
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• pp.687-695
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• 2006

This paper presents a network-based traction control system(TCS), where several electric control units(ECUs) are connected by a controller area network(CAN) communication system. The control system consists of four ECUs: the electric throttle controller, the transmission controller, the engine controller and the traction controller. In order to validate the traction control algorithm of the network-based TCS and evaluate its performance, a Hardware-In-the-Loop Simulation(HILS) environment was developed. Herein we propose a new concept of the HILS environment called the network-based HILS(Net-HILS) for the development and validation of network-based control systems which include smart sensors or actuators. In this study, we report that we have designed a network-based TCS, validated its algorithm and evaluated its performance using Net-HILS.

• International Journal of Automotive Technology
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• v.8 no.2
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• pp.211-217
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• 2007

The wheel slip control systems are able to control the braking force more accurately and can be adapted to different vehicles more easily than conventional ABS (Anti-lock Brake System) systems. In realizing the wheel slip control systems, real-time information such as the tire braking force at each wheel is required. In addition, the optimal target slip values need to be determined depending on the braking objectives such as minimum braking distance and stability enhancement. In this paper, a robust wheel slip controller is developed based on the adaptive sliding mode control method and an optimal target slip assignment algorithm is proposed for maximizing the braking force. An adaptive law is formulated to estimate the braking force in real-time. The wheel slip controller is designed based on the Lyapunov stability theory considering the error bounds in estimating the braking force and the brake disk-pad friction coefficient. The target slip assignment algorithm searches for the optimal target slip value based on the estimated braking force. The performance of the proposed wheel slip control system is verified in HILS (Hardware-In-the-Loop Simulator) experiments and demonstrates the effectiveness of the wheel slip control in various road conditions.

• International Journal of Automotive Technology
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• v.8 no.3
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• pp.351-359
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• 2007

In this study, a hierarchical switching control scheme based on robust control theory is proposed for tracking control of vehicle longitudinal acceleration in the presence of large uncertainties. A model set consisting of four multiplicative-uncertainty models is set up, and its corresponding controller set is designed by the LMI approach, which can ensures the robust performance of the closed loop system under arbitray switching. Based on the model set and the controller set, a switching index function by estimating the system gain of the uncertainties between the plant and the nominal model is designed to determine when and which controller should be switched into the closed loop. After theoretical analyses, experiments have also been carried out to validate the proposed control algorithm. The results show that the control system has good performance of robust stability and tracking ability in the presence of large uncertainties. The response time is smaller than 1.5s and the max tracking error is about $0.05\;m/S^2$ with the step input.

• Journal of Drive and Control
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• v.11 no.4
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• pp.1-7
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• 2014

This paper presents the development of a compact position generator to be used for industrial purposes based on a pump controlled Electro-Hydraulic Actuator (EHA), which is closed-loop controlled by an embedded based Iterative PID controller. The controller is designed by combining the PID controller and the iterative learning scheme to perform tracking control for periodically desired references. Control algorithm is implemented on an embedded computer (AD 7011-EVA) which makes the implementation and application in industrial environments easier.

• International Journal of Automotive Technology
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• v.5 no.4
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• pp.269-276
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• 2004

There has been considerable activity in recent years in developing timing analysis algorithms for distributed real-time control systems. However, it is difficult for control engineers to analyze the timing behavior of distributed real-time control systems because the algorithms was developed in a software engineer's position and the calculation of the algorithm is very complex. Therefore, there is a need to develop a timing analysis tool, which can handle the calculation complexity of the timing analysis algorithms in order to help control engineers easily analyze or develop the distributed real-time control systems. In this paper, an interactive timing analysis tool, called RAT (Response-time Analysis Tool), is introduced. RAT can perform the schedulability analysis for development of distributed real-time control systems. The schedulability analysis can verify whether all real-time tasks and messages in a system will be completed by their deadlines in the system design phase. Furthermore, from the viewpoint of end-to-end scheduling, RAT can perform the schedulability analysis for series of tasks and messages in a precedence relationship.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.6
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• pp.623-632
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• 2015

This study proposes a control strategy of the common rail pressure with a fuel injection limitation algorithm to reduce particulate matter (PM) emissions under transient states. The proposed control strategy consists of two parts: injection quantity limitation and rail pressure adaptation. The injection limitation algorithm determines the maximum allowable fuel injection quantity to avoid rich combustion under transient states. The fuel injection quantity is limited by predicting the burned gas rate after combustion; however, the reduced injection quantity leads to deterioration of engine torque. The common rail pressure adaptation strategy is designed to compensate for the reduced engine torque. An increase of the rail pressure under transient states contributes to enhancement of the engine torque as well as reduction of PM emissions by promoting atomization of the injected fuel. The proposed control strategy is validated through engine experiments. The rail pressure adaptation reduced the PM emission by 5-10% and enhanced the engine torque up to 2.5%.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.5
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• pp.14-21
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• 2011

In diesel engines, accurate EGR control is important due to its effect on nitrogen oxide and particulate matter emissions. Conventional EGR control system comprises a PI feedback controller for tracking target air mass flow and a feedforward controller for fast response. Physically, the EGR flow is affected by EGR valve lift and thermodynamic properties of the EGR path, such as pressures and temperatures. However, the conventional feedforward control output is indirectly derived from engine operating conditions, such as engine rotational speed and fuel injection quantity. Accordingly, the conventional feedforward control action counteracts the feedback controller in certain operating conditions. In order to improve this disadvantage, in this study, we proposed feedforward EGR control algorithm based on a physical model of the EGR system. The proposed EGR control strategy was validated with a 3.0 liter common rail direct injection diesel engine equipped with a DC motor type EGR valve.

• Transactions of the Korean Society of Automotive Engineers
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• v.19 no.4
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• pp.84-90
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• 2011

In active noise control filter, LMS algorithms which used for control filter, assure the convergence property, and computational burden of these algorithms are proportionate to the filter taps. The convergence speed of LMS algorithms is mainly determined by value of the convergence coefficient, so optimal selection of the value of convergence coefficient is very important. In this paper, We proposed novel adaptive fuzzy logic LMS algorithms with FIR filter structure which has better convergence speed and less computational burden than conventional LMS algorithms, for single channel active noise control with ill conditioned signal case. Computer simulations were performed to show the effectiveness of a proposed algorithms.

• International Journal of Automotive Technology
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• v.6 no.5
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• pp.529-536
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• 2005

This paper presents a novel design of a fuzzy control strategy (FCS) based on torque distribution for parallel hybrid electric vehicles (HEVs). An empirical load-regulating vehicle operation strategy is developed on the basis of analysis of the components efficiency map data and the overall energy conversion efficiency. The aim of the strategy is to optimize the fuel economy and balance the battery state-of-charge (SOC), while satisfying the vehicle performance and drivability requirements. In order to accomplish this strategy, a fuzzy inference engine with a rule-base extracted from the empirical strategy is designed, which works as the kernel of a fuzzy torque distribution controller to determine the optimal distribution of the driver torque request between the engine and the motor. Simulation results reveal that compared with the conventional strategy which uses precise threshold parameters the proposed FCS improves fuel economy as well as maintains better battery SOC within its operation range.