• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1000-1003
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• 1996

Nonlinear unified predictive control(UPC) algorithm was applied to the temperature control of a batch polymerization reactor for polymethylmethacrylate(PMMA). Before the polymerization reaction is initiated, the parameters of the process model are determined by the recursive least squares(RLS) method. During the reaction, nonlinearities due to generation of heat of reaction and variation of heat transfer coefficients are predicted through the nonlinear model developed. These nonlinearities are added to the process output from the linear process model. And then, the predicted process output is used to calculate the control output sequence. The performance of nonlinear control algorithm was verified by simulation and compared with that of the linear unified predictive control algorithm. In the experiment of a batch PMMA polymerization, nonlinear unified predictive control was implemented to regulate the temperature of the reactor, and the validity of the nonlinear model was verified through the experimental results. The performance of the nonlinear controller turned out to be superior to that of the linear controller for tracking abrupt changes in setpoint.

• Transactions of The Korea Fluid Power Systems Society
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• v.4 no.4
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• pp.8-14
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• 2007

The electro-hydraulic pump is usually used in testing equipments which require one control function. But until now, it is not applied to industrial equipments which are exposed to severe working environment and require various control functions. This paper proposes a technique which controls continuously flow, pressure and power by utilizing switching control mode. Mathematical model is developed from the continuity equation for the pressurized control volume and the torque balance for the swash plate motion. To simplify the model we make the linear state equation by differentiating the nonlinear model. We analyze the stability and disturbance by using the state variable model. Finally, we review the control performances of flow, pressure and power by tests using PID controller.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1212-1217
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• 2008

This paper studies the feasibility of using the nonlinear model predictive control as a formation flight control algorithm for unmanned aerial vehicles. The optimal control inputs for formation flight are calculated through the cost function which incorporates the relative positions of the individual vehicles to maintain a desired formation and also the inequality constraints on inputs and states using the Karush-Kuhn-Tucker conditions. In the nonlinear model predictive control setting, the optimal control inputs are implemented in a receding horizon manner, which is suitable for dealing with dynamic constraints. Numerical simulations are executed for the validation of the proposed scheme.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.296-304
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• 2002

A position tracking control schemes on the precise mechanical system in presence of nonlinear dynamic friction is proposed. A nonlinear dynamic friction is regarded as the bristle friction model to compensate effects of friction. The conventional sliding mode controller often has been used as a non-model-based friction controller, but it has a poor tracking performance in high-precision position tracking application since it completely cannot compensate the friction effect below a certain precision level. Thus to improve the precise position tracking performance, we propose the sliding mode control method combined with the friction-model-based observer having tunable structure of the transient response. Then this control scheme has a good transient response as well as the high precise tracking performance compared with the conventional sliding mode control without observer and the control system with similar type of observer. The experiments on the bali-screw drive table with the nonlinear dynamic friction show the feasibility of the proposed control scheme.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.173-179
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• 2003

In this paper, a real-time multibody vehicle dynamics and control model has been developed for a virtual reality intelligent vehicle simulator. The simulator consists of low PCs for a virtual reality visualization system, vehicle dynamics and control analysis system a control loading system, and a network monitoring system. Virtual environment is created by 3D Studio Max graphic tool and OpenGVS real-time rendering library. A real-time vehicle dynamics and control model consists of a control module based on the sliding mode control for adaptive cruise control and a real-time multibody vehicle dynamics module based on the subsystem synthesis method. To verify the real-time capability of the model, cut-in, cut-out simulations have been carried out.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.115-121
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• 2022

This paper proposes a distributed model predictive formation control algorithm for a group of unmanned ground vehicles (UGVs) with guaranteeing collision avoidance between UGVs. Generally, the model predictive control based formation control has a disadvantage in that it takes a long time to compute control inputs when considering collision avoidance between UGVs. In this paper, in order to overcome this problem, the formation control algorithm is implemented in a distributed manner so that it could be individually controlled. Also, a collision-avoidance method considering real-time is proposed. The proposed formation control algorithm is implemented based on robot operating system (ROS), open source-based middleware. Through the various simulation tests, it is confirmed that the formation control of five UGVs is successfully performed while avoiding collisions between UGVs.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2000.10a
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• pp.501-508
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• 2000

It is demanded to find the dynamic model of a real structure to design a controller. However, as the structure has inherently infinite number of degree-of-freedom, it is impossible to obtain an exact dynamic model of the structure. Instead a reduction model with finite degree-of-freedom is used for the design of a controller. So there exists uncertainty between a real model and a reduction model which causes poor performance of control. All these uncertainties can degrade the control performance and even cause the control instability. Thus, robust control strategy considering the above uncertainties can be an alternative one to guarantee the performance and stability of the control. This study deals with the experimental verification of robust controller design for the active mass driver. $\mu$-synthesis technique is employed as a robust control strategy. Some weights are chosen based on the difference between the initial plant with which the controller is designed and the perturbed plant to be controlled having the actuator uncertainty. The robustness of $\mu$-synthesis technique is compared with the result of LQG strategy, which does not consider the uncertainty.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.958-961
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• 2001

Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.99-106
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• 2003

A new reconfigurable model following flight control method based on direct adaptive scheme is presented. Using the timescale separation principle, both the inner-loop and the outer-loop states are controlled simultaneously. For the timescale separation assumption to be satisfied, the inner-loop model dynamics is set to be fast whereas the outer-loop model dynamics is set to be relatively slow. The stability and convergence of the proposed control law is proved by Lyapunov theorem. One of the merits of the proposed reconfigurable controller is that the FDI process and the persistent input excitation are not necessary, which is suitable for the flight control system. To evaluate the reconfiguration performance of the proposed control method, numerical simulation is performed using six degree-of-freedom nonlinear dynamics.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.2
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• pp.63-68
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• 2015

This paper presents the algorithm for envelope protection of helicopters. The algorithm consists of two feedback control loops: inner loop and outer loop. As an inner loop control, model following control is designed to meet the ADS-33 handling qualities specification by minimizing the tracking errors between the responses of the actual model and those of the command filter. In order to implement envelope protection, saturation limiter is imposed to command channels in command filter, whose limits are computed corresponding to the envelope limit. Fast model predictive control is designed as an outer loop control to deal with saturation constraints generated by the inner loop envelope protection and also imposed by outer loop envelope protection variables. Simulation results show that the proposed algorithm yields good envelope protection performance.