• Journal of the Korean Society for Precision Engineering
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• v.11 no.6
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• pp.20-27
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• 1994

This paper presents a controller design to coordinate a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme is applied. Since parameters of the robot manipulators such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters works as a disturbance force. To identify the unknown parameters, an improved adaptive control algorithm is directly derived from a chosen Lyapunov's function candidate based on the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing forces coming from the difference between the actual and the estimated system parameters. Numerical examples are shown using three degree-of-freedom planar arm.

• Structural Engineering and Mechanics
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• v.21 no.2
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• pp.185-204
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• 2005

Variations in system parameters due to uncertainties may result in system performance deterioration. Uncertainties in modeling of structures are often considered to ensure that control system is robust with respect to response errors. Hence, the uncertain concept plays an important role in vibration control of the engineering structures. The paper discusses the robustness of the stability of vibration control systems with uncertain parameters. The vibration control problem of an uncertain system is approximated by a deterministic one. The uncertain parameters are described by interval variables. The uncertain state matrix is constructed directly using system physical parameters and avoided to use bounds in Euclidean norm. The feedback gain matrix is determined based on the deterministic systems, and then it is applied to the actual uncertain systems. A method to calculate the upper and lower bounds of eigenvalues of the close-loop system with uncertain parameters is presented. The lower bounds of eigenvalues can be used to estimate the robustness of the stability the controlled system with uncertain parameters. Two numerical examples are given to illustrate the applications of the present approach.

• Journal of Power System Engineering
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• v.3 no.1
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• pp.67-73
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• 1999

It is not easy to control the speed of AC motors accurately without modeling with some parameters for the controlled system. However, there are some application parts which do not require high speed responses strictly and the motor parameters can not to be identified simply. In this paper, a speed control method for a synchronous motor(S.M) with unknown parameters of the motor is investigated. The method is based on the current control algorithm. Speed controller and current controller are designed using PI control law. Some experiments are performed using DSP and power expert system to prove the validity of the proposed method. Throughout experimental results, the method is confirmed successfully. This method is expected to control the system with unknown parameters of the S.M efficiently.

• Nuclear Engineering and Technology
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• v.50 no.7
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• pp.1017-1023
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• 2018

The measurement and calculation of neutronic parameters in nuclear research reactors has an important influence on control and safety of the nuclear reactor. The power peaking factors, reactivity coefficients and kinetic parameters are the most important neutronic parameter for determining the state of the reactor. The position of the control shim safety rods in the core configuration affects these parameters. The main purpose of this work is to use the MTR_PC package to evaluate the effect of the partially insertion of the control rod on the neutronic parameters at the operating core of the Tehran Research Reactor. The simulation results show that by increasing the insertion of control rods (bank) in the core, the absolute values of power peaking factor, reactivity coefficients and effective delayed neutron fraction increased and only prompt neutron life time decreased. In addition, the results show that the changes of moderator temperature coefficients value versus the control rods positions are very significant. The average value of moderator temperature coefficients increase about 98% in the range of 0-70% insertion of control rods.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2557-2561
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• 2005

The PID controller design and choosing PID parameters according to system response are proposed in this paper. Here PID controller is employed to control DC motor speed and Matlab program is used for calculation and simulation. Choosing PID parameters are demonstrated by several contrast experiments and a way for setting PID parameters values is discussed.

• Journal of Advanced Marine Engineering and Technology
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• v.20 no.4
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• pp.20-26
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• 1996

Recently, for the speed control of a diesel engine, some methods using the modern control theory such as LQ control technique, or $\textit{H}_{\infty}$control theory etc., have been reported. However, most of speed controlers of a diesel engine ever developed are still using the PID control algorithm. And, as another approach to the speed control of a diesel engine, the authors proposed already a new method to adjust the parameters of the PID controller by a model matching method. In the previous paper, the authors confirmed that the proposed new method is superior to Ziegler & Nichols's method through the analysis of results of the digital simulations under the assumption that the parameters of a diesel engine are known exactly. But, actually, it is very difficult to find out the value of parameters of a diesel engine accurately. And the parameters of a diesel engine are changigng according to the operating condition of a diesel engine. So, in this paper, a method to estimate the parameters of the PID controller for the speed control of a diesel engine by means of the model matching method are proposed. Also, the digital simulations are carried out in cases either with or without measurement noise. And this paper confirms that the proposed method here is superior to Ziegler & Nichols's method through the analysis of the characteristics of indicial responses.

• Journal of Korean Society of Transportation
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• v.14 no.3
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• pp.127-141
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• 1996

This paper presents a unique approach to urban traffic network signal control. This paper begins with an introduction to traffic control in general, and then goes on to describe the approach of fuzzy control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic network condition and adjacent intersection. The signal timing parameters evolve dynamically using only local information to improve traffic signal flow. The signal timing at an intersection is defined by three parameters : cycle time, phase split, off set. Fuzzy decision rules are used to adjust three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. In this paper the effectiveness of this method is showed through simulation of the traffic signal flow in a network of controlled intersection.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.176-181
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• 1992

In this paper, an adaptive generalized predictive control(GPC) algorithm which minimizes a N-stage cost function is proposed. The resulting controller is based on GPC algorithm and can be used in unknown plant parameters as the parameters of one step ahead predictor are estimated by recursive least squares method. The estimated parameters are extended to G,P, and F amtrix which contain the parameters of N step ahead predictors. And the minimization of cost function assuming no constraints on future controls results in the projected control increment vector. Hence this adaptive GPC algorithm can be used for either unknown system or varing system parameters, and it is also shown through simulations that the algorithm is robust to the variation of system parameters. This adaptive GPC scheme is shown to have the same stability properties as the deterministic GPC, and requires small amount of calculation compared to other adaptive algorithms which minimize N-stage cost function. Especially, in case that the maximum output horizon is 1, the proposed algorithm can be applicable to direct adaptive GPC.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.156-160
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• 1993

This paper presents a controller design to coordinate a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme is applied. Since parameters of the robot manipulators such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters works as a disturbance force. To identify the unknown parameters, an inproved adaptive control algorithm is directly derived from a chosen Lyapunov's function candidate based on the Lyapunov's Second Method. A robust control algorithm is devised to counteract the bounded disturbance inputs such as contact forces and disturbing force coming from the difference between th actual and the estimated system parameters. Numerical examples are shown using three degree-of-freedom planar arm.