• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.39 no.7
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• pp.618-626
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• 2011

In order to complete missions in a complicated terrain or highly dangerous area, an unmanned aerial vehicle(UAV) needs a fine controller to precisely follow the desired path. A PID controller used for the path following feeds forward path curvature information to the control input to improve the path following performance. High gain for PID controller is necessary to follow path tightly. However the high gain could cause instability or performance degradation when the vehicle has slow dynamics. We present PID controller design method which considers response delay of vehicle as well as path curvature. In order to obtain path curvature the desired path is described as a 3rd order polynomial by applying cubic spline interpolation. We apply the proposed controller to the path following of a UAV which is operated in high altitude and has very slow lateral dynamics. The lateral dynamics are modelled as a first order delayed system in the controller design. Nonlinear simulation shows the UAV with proposed controller follows an arbitrary path very tightly.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.189-193
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• 1996

Piezoelectric actuator is widely used in precision positioning applications due to its excellent positioning resolution. However, serious hysteresis nonlinearity of the actuator deteriorates its open loop positioning capability. Generally, a nonlinear hysteresis model is used in feedforward loop to improve positioning accuracy. In this study, however, a simple lead compensator is proposed as a substitution for a complex nonlinear hysteresis model and tested through experiments for precision position control.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.3
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• pp.288-293
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• 2009

This study presents the active noise control of duct noise. The duct was excited by a steady-state harmonic and white noise force and the control was performed by one control speaker attached to surface of the duct. An adaptive controller based on filtered x LMS(FXLMS) algorithm was used and controller was defined by minimizing the square of the response of the error microphone. The assemble controller, which is called a hybrid ANC(active noise control) system, was combined with feedforward and feedback controller. The feedforward ANC attenuates primary noise that is correlated with the reference signal, while the feedback ANC cancels the narrowband components of the primary noise that are not observed by the reference sensor. Furthermore, in many ANC applications, the periodic components of noise are the most intense and the feedback ANC system has the effect of reducing the spectral peaks of the primary noise, thus easing the burden of the feedforward ANC filter.

• Journal of KSNVE
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• v.10 no.6
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• pp.1048-1058
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• 2000

Thipaper presents a methodology to suppress the vibration of thin rectangular plate clamped all edges using piezo-ceramic material as actuators and sensors. Dynamic characteristics of the structure bonded with distributed actuators/sensors are identified by the Multi-Input Multi-Output (MIMO) frequency domain modeling technique based on the experimental data. Hybrid control scheme is adopted and feedback controller is designed by LQG(Linear Quadratic Gaussian). Feedforward controller is adapted by multiple filtered -$x$ LMS(least mean square) algorithm. Experiment result demonstrates the effective reduction of the vibration label for both the transient and persistent external disturbances.

• 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.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.94-101
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• 2005

This work proposes a new method for describing the hysteresis non-linearity of a piezoelectric actuator. The hysteresis behaviour of piezoelectric actuators, including the minor loop trajectory, are modeled by geometrical relationship between a reference major loop and its minor loops. This hysteresis model is transformed into inverse hysteresis model in order to output compensated voltage with regard to the given input displacement. A feedforward neural network, which is trained by a feedback PID control module, is incorporated to the inverse hysteresis model to compensate unknown dynamics of the piezoelectric system. To show the feasibility of the proposed feedforward-feedback controller, some experiments have been carried out and the tracking performance was compared to that of simple PTD controller.

• KIISE Transactions on Computing Practices
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• v.21 no.4
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• pp.338-342
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• 2015

Recurrent Neural Network (RNN), a machine learning model which can handle time-series data, can possess more varied structures than a feed-forward neural network, since a RNN allows hidden-to-hidden connections. This research focuses on the network structure among hidden neurons, and discusses the information processing capability of RNN. Time-series learning potential and dynamics of RNNs are investigated upon several well-established network structure models. Hidden neuron network structure is found to have significant impact on the performance of a model, and the performance variations are generally correlated with the criticality of the network dynamics. Especially Preferential Attachment Network model showed an interesting behavior. These findings provide clues for performance improvement of the RNN.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.2
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• pp.160-166
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• 2013

This paper proposes a driver supportive device with haptic cue function which can be applicable for vehicles adopting manual transmission system to transmit gear-shifting information to a driver by kinesthetic forces. This haptic cue function is implemented on accelerator pedal by utilizing magnetorheological(MR) fluid and clutch mechanism. In order to achieve this goal, an MR clutch mechanism is devised to be capable of rotary motion of accelerator pedal. The proposed MR clutch is then optimally designed and manufactured under consideration of spatial limitation of vehicles. After transmission torque is experimentally evaluated according to field intensity. The manufactured MR clutch is integrated with accelerator pedal and electric motor to establish the haptic cue device. Control performances are experimentally evaluated via a simple feed-forward control algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1557-1560
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• 1997

In this paper, we have built an experimental magnetic levitation system for a possible use of control education. We have give a mathermatical model of the nonlinear system and have shown the stability region of the linearized system when it is controlled by a PD controller. We also proposed a neural network control system which uses a neural network as a feedforward controller thgether with a conventional feedback PF controller. We have generated a desired output trajectory, which was designed for the benefit of the generalization of the neural network controller, and trained the desired output trajectory, which was desigend for the benefit of the generalization of the neural netowrk controller, and trained a neural network controller with the data of the actual input and the output of the system obtained by applying the desired output trajectroy. A good tracking performance was observed for both the desired trajectiories used and not used for the neural network training.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.590-596
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• 2004

A passivity-based dynamic output feedback controller design is considered for a finite collection of non-square linear systems. Design of a single controller for a set of plants i.e. simultaneous stabilization is an important issue in the area of robust control design. We first determine a squaring gain matrix and an additional dynamics that is connected to the systems in a feedforward way, then a static passivating control law is designed. Consequently, the actual feedback controller will be the static control law combined with the feedforward dynamics. A necessary and sufficient condition for the existence of the parallel feedforward compensator is given by the static output feedback formulation. In contrast to the previous result [1], a technical condition for constructing the parallel feedforward compensator is removed by proposing a new type of the parallel compensator.