• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.76-85
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• 2022

One of the most fundamental challenges when designing controllers for dynamic systems is the adjustment of controller parameters. Usually the system model is used to get the initial controller, but eventually the controller parameters must be manually adjusted in the real system to achieve the best performance. To avoid this manual tuning step, data-driven methods such as machine learning were used. Recently, reinforcement learning became one alternative of this problem to be considered as an agent learns policies in large state space with trial-and-error Markov Decision Process (MDP) which is widely used in the field of robotics. However, on initial training step, as an agent tries to explore to the new state space with random action and acts directly on the controller parameters in real systems, MDP can lead the system safety-critical system failures. Therefore, the issue of 'safe exploration' became important. In this paper we meet 'safe exploration' condition with Control Barrier Function (CBF) which converts direct constraints on the state space to the implicit constraint of the control inputs. Given an initial low-performance controller, it automatically optimizes the parameters of the control law while ensuring safety by the CBF so that the agent can learn how to predict and control unknown and often stochastic environments. Simulation results on a quadrotor UAV indicate that the proposed method can safely optimize controller parameters quickly and automatically.

• Nuclear Engineering and Technology
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• v.21 no.2
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• pp.99-110
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• 1989

The optimal control of xenon concentration in a nuclear reactor is posed as a linear quadratic regulator problem with state feedback control. Since it is not possible to measure the state variables such as xenon and iodine concentrations directly, implementation of the optimal state feedback control law requires estimation of the unmeasurable state variables. The estimation method used is based on the Luenberger observer. The set of the reactor kinetics equations is a stiff system. This singularly perturbed system arises from the interaction of slow dynamic modes (iodine and xenon concentrations) and fast dynamic modes (neutron flux, fuel and coolant temperatures). The singular perturbation technique is used to overcome this stiffness problem. The observer-based controller of the original system is effected by separate design of the observer and controller of the reduced subsystem and the fast subsystem. In particular, since in the reactor kinetics control problem analyzed in the study the fast mode dies out quickly, we need only design the observer for the reduced slow subsystem. The results of the test problems demonstrated that the state feedback control of the xenon oscillation can be accomplished efficiently and without sacrificing accuracy by using the observer combined with the singular perturbation method.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.405-413
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• 2006

In general mechanical servo systems, friction deteriorates the performance of controllers by its nonlinear characteristics. Especially, friction phenomenon causes steady-state tracking errors and limit cycles in position and velocity control systems, even though gains of controllers are tuned well in linear system model. Even if sensor is used higher accuracy level, it is difficult to improve tracking performance of the position to the same level with a general control method such as PID type. Therefore, many friction models were proposed and compensation methods have been researched actively. In this paper, we consider that the variation of mover's mass is various by loading and unloading. The normal force variation occurs by it an other parameters. Therefore, the proposed control system is composed of main position controller and a friction compensator. A parameter estimator for a nonlinear friction model is designed by adaptive control law and adaptive backstepping control method.

• Journal of Navigation and Port Research
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• v.31 no.1 s.117
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• pp.55-64
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• 2007

In general mechanical servo systems, friction deteriorates the performance of controllers by its nonlinear characteristics. Especially, friction phenomenon causes steady-state tracking errors and limit cycles in position and velocity control systems, even though gains of controllers are tuned well in linear system model. Even if sensor is used higher accuracy level, it is difficult to improve tracking performance of the position to the same level with a general control method such as PID type. Therefore, many friction models were proposed and compensation methods have been researched actively. In this paper, we consider that the variation of mover's mass is various by loading and unloading. The normal force variation occurs by it and other parameters. Therefore, the proposed control system is composed of main position controller and a friction compensator. A parameter estimator for a nonlinear friction model is designed by adaptive control law and adaptive backstepping control method.

• Journal of Mechanical Science and Technology
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• v.14 no.5
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• pp.499-507
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• 2000

Smooth shift is one of the key issues in automatic power transmission control systems. However, the torque sensors are too expensive to be used in shift controllers on production vehicles. In order to provide a basic strategy for smooth shifting by using RPM sensors only and in order to accomplish the shift within a designated time, this paper studies detailed characteristics of the smooth shift for clutch-to-clutch shift mechanism. A desired trajectory of slip speed is proposed for smooth acceleration shift defined in this paper. Also the clutch torque needed to achieve this trajectory is derived, and it may be used as a open loop shift control law.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.158-161
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• 1996

A robust adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs an adaptive fuzzy system to compensate for the uncertainty of the plant. In order to improve the robustness under approximation errors and disturbances, the proposed architecture includes deadzone in adaptation laws. Unlike the previously proposed schemes, the magnitude of approximate errors and disturbances is not required in the determination of the deadzone size, since it is estimated using the adaptation law. The proposed algorithm is proven to be globally stable in the Lyapunov sense, with tracking errors converging to the proposed architecture.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.51-57
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• 2022

The problem of optimal stochastic GA control of the system with uncertain parameters and unsure noise covariates is studied. First, without knowing the explicit form of the dynamic system, the open-loop determinism problem with path optimization is solved. Next, Gaussian linear quadratic controllers (LQG) are designed for linear systems that depend on the nominal path. A robust genetic neural network (NN) fuzzy controller is synthesized, which consists of a Kalman filter and an optimal controller to assure the asymptotic stability of the discrete control system. A simulation is performed to prove the suitability and performance of the recommended algorithm. The results indicated that the recommended method is a feasible method to improve the performance of active tuned mass damper (ATMD) shear buildings under random earthquake disturbances.

• Journal of Power Electronics
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• v.18 no.4
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• pp.1075-1085
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• 2018

To deal with the operation performance degradation of permanent magnet synchronous machine (PMSM) drives with uncertainties and unmodeled dynamics, this paper presents a finite-time nonlinear disturbance observer (FTNDO) based discretized integral sliding mode (DISM) composite control scheme. Based on the reaching-law approach, a DISM speed controller featuring a superior dynamic quality and global robustness against disturbances is constructed. This controller can avoid the reaching phase and overlarge control action. In addition, a sliding mode differentiator based FTNDO is devised and extended to the discrete-time domain for disturbance estimation. The attractive features of the FTNDO are that it can provide a finite-time converging estimation and alleviate the chattering effect in conventional sliding mode observers, while retaining robustness to parameter variations. By feeding the estimate forward to the pre-stage DISM controller, both disturbances and chattering can be significantly suppressed. Moreover, considering the estimation error of a FTNDO caused by discrete sampling, a stability analysis of the composite controller is discussed. Experimental results validate the superiority of the presented scheme.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.845-851
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• 2008

A simple RMRAC (Robust Model Reference Adaptive Control) scheme for the PMSM (Permanent Magnent Synchronous Motor) is proposed in the synchronous frame. A current control of PMSM is the most inner loop of electro-mechanical driving systems and it requires a fast and simple control law to play a foundation role in the control hierarchy. In the proposed synchronous current model, the input signal is composed of a calculated voltage by proposed adaptive laws and real system disturbance. The gains of feed-forward and feedback controllers are estimated by the proposed modified Gradient method respectively, where the system disturbances are assumed as filtered current tracking errors. After the estimation of the system disturbances from the tracking errors, the corresponding voltage is fed forward to control input voltage to compensate for the disturbances. The proposed method is robust against high frequency disturbance and has a fast dynamic response. It also shows a good real-time performance due to it's simplicity of control structure. Through the simulations and real experiments, efficiency of the proposed method is verified.

• Journal of the military operations research society of Korea
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• v.2 no.1
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• pp.177-184
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• 1976

As the organizational size of a military service or business increases and its management becomes complex, the success in its management depends less on static type of management but more on careful, dynamic type of management. In this thesis, an operations research technique is applied to the problems of determining optimal air traffic control rule and of optimal capacity of air base for a military air base. An airport runway is regarded as the service facility in a queueing mechanism, used by landing, low approach, and departing aircraft. The usual order of service gives priority different classes of aircraft such as landings, departures, and low approaches; here service disciplines are considered assigning priorities to different classes of aricraft grouped according to required runway time. Several such priority rules are compared by means of a steady-state queueing model with non-preemptive priorities. From the survey conducted for the thesis development, it was found that the flight pattern such as departure, law approach, and landing within a control zone, follows a Poisson distribution and the service time follows an Erlang distribution. In the problem of choosing the optimal air traffic control rule, the control rule of giving service priority to the aircraft with a minimum average waiting cost, regardless of flight patterns, was found to be the optimal one. Through a simulation with data collected at K-O O Air Base, the optimal take-off interval and the optimal capacity of aircraft to be employed were determined.