• 드라이브 ㆍ 컨트롤
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• 제14권2호
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• pp.16-22
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• 2017

This paper presents the algorithm of an adaptive model-free-control-based steering control for multi-axle all-terrain cranes for which the recursive least squares with forgetting are applied. To optimally control the actual system in the real world, the linear or nonlinear mathematical model of the system should be given for the determination of the optimal control inputs; however, it is difficult to derive the mathematical model due to the actual system's complexity and nonlinearity. To address this problem, the proposed adaptive model-free controller is used to control the steering angle of a multi-axle crane. The proposed model-free control algorithm uses only the input and output signals of the system to determine the optimal inputs. The recursive least-squares algorithm identifies first-order systems. The uncertainty between the identified system and the actual system was estimated based on the disturbance observer. The proposed control algorithm was used for the steering control of a multi-axle crane, where only the steering input and the desired yaw rate were employed, to track the reference path. The controller and performance evaluations were constructed and conducted in the Matlab/Simulink environment. The evaluation results show that the proposed adaptive model-free-control-based steering-control algorithm produces a sound path-tracking performance.

• 드라이브 ㆍ 컨트롤
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• 제19권1호
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• pp.51-61
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• 2022

This paper presents a model-free system based on a framework of a backstepping sliding mode control (BSMC) with a radial basis function neural network (RBFNN) and adaptive mechanism for electro-hydraulic systems (EHSs). First, an EHS mathematical model was dedicatedly derived to understand the system behavior. Based on the system structure, BSMC was employed to satisfy the output performance. Due to the highly nonlinear characteristics and the presence of parametric uncertainties, a model-free approximator based on an RBFNN was developed to compensate for the EHS dynamics, thus addressing the difficulty in the requirement of system information. Adaptive laws based on the actor-critic neural network (ACNN) were implemented to suppress the existing error in the approximation and satisfy system qualification. The stability of the closed-loop system was theoretically proven by the Lyapunov function. To evaluate the effectiveness of the proposed algorithm, proportional-integrated-derivative (PID) and improved PID with ACNN (ACPID), which are considered two complete model-free methods, and adaptive backstepping sliding mode control, considered an ideal model-based method with the same adaptive laws, were used as two benchmark control strategies in a comparative simulation. The simulated results validated the superiority of the proposed algorithm in achieving nearly the same performance as the ideal adaptive BSMC.

• Journal of Power Electronics
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• 제19권5호
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• pp.1193-1202
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• 2019

A SMPMSM drive system is a typical nonlinear system with time-varying parameters and unmodeled dynamics. The speed outer loop and current inner loop control structures are coupled and coexist with various disturbances, which makes the speed control of SMPMSM drive systems challenging. First, an ultra-local model of a PMSM driving system is established online based on the algebraic estimation method of model-free control. Second, based on the backstepping control framework, model-free adaptive integral backstepping (MF-AIB) control is proposed. This scheme is applied to the permanent magnet synchronous motor (PMSM) drive system of an electric vehicle for the first time. The validity of the proposed control scheme is verified by system simulations and experimental results obtained from a SMPMSM drive system bench test.

• 전기학회논문지
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• 제58권7호
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• pp.1411-1417
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• 2009

In this paper, we consider the design of $H_{\infty}$ control of linear discrete-time systems having no mathematical model. The basic approach is to use Q-learning which is a reinforcement learning method based on actor-critic structure. The model-free control design is to use not the mathematical model of the system but the informations on states and inputs. As a result, the derived iterative algorithm is expressed as linear matrix inequalities(LMI) of measured data from system states and inputs. It is shown that, for a sufficiently rich enough disturbance, this algorithm converges to the standard $H_{\infty}$ control solution obtained using the exact system model. A simple numerical example is given to show the usefulness of our result on practical application.

• International Journal of Control, Automation, and Systems
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• 제6권4호
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• pp.506-514
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• 2008

This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

• Journal of Power Electronics
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• 제19권1호
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• pp.168-178
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• 2019

Discrete space vector modulation (DSVM) is an effective method to improve the steady-state performance of the finite control set predictive control for permanent magnet synchronous motor drive systems. However, it requires complex computations due to the presence of numerous virtual voltage vectors. This paper proposes an improved finite control set model-free predictive control using DSVM to reduce the computational burden. First, model-free deadbeat current control is used to generate the reference voltage vector. Then, based on the principle that the voltage vector closest to the reference voltage vector minimizes the cost function, the optimal voltage vector is obtained in an effective way which avoids evaluation of the cost function. Additionally, in order to implement double-objective control, a two-level decisional cost function is designed to sequentially reduce the stator currents tracking error and the inverter switching frequency. The effectiveness of the proposed control is validated based on experimental tests.

• 로봇학회논문지
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• 제15권1호
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• pp.77-89
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• 2020

This paper proposes a simple and intuitive model-free torque-tracking control for rotary electro-hydraulic actuators. The undesirable natural-velocity-feedback effect is discussed by introducing mechanical impedance into the electro-hydraulic actuation system. The proposed model-free torque control comprises inner- and outer-loop control to achieve two control objectives. Inner-loop control reduces the mechanical impedance passively and optimally. To improve the tracking accuracy, a certain form of proportional-integral-derivative control is applied to the outer loop. The robustness of the proposed closed-loop system against external disturbances is demonstrated by transforming the two-loop control structure into a disturbance observer form. The proposed method is validated on a single joint electro-hydraulic actuator.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.139.3-139
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• 2001

The free-model concept is introduced as an alternative intelligent system technique to design a controller with input and output data only. The idea of free model comes from the Taylor series approximation, where an output can be estimated when such data as position, velocity, and acceleration are known. The parameters in the free model can be estimated using the input-output data and a controller can be designed based on the free model. The free model thus developed is shown to be controllable, observable, and robust. The accuracy of the free-model approximation can be improved by increasing the observation window and the order of the free model. The LQR method is applied to the free model to design power system stabilizers ...

• 한국항해항만학회지
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• 제33권9호
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• pp.603-608
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• 2009

In this paper, a 3m-class free running model ship will be introduced with its manoeuvring performance experiments. The results of turning circle test and zig-zag test will be explained. The developed system are equipped with GPS, main control computer, wireless LAN, IMU (Inertial Measurement Unit), self-propulsion propeller and driving rudder. Its motion can be controlled by RC (Radio Control) and wireless LAN from land based center. Automatic navigation is also available by pre-programmed algorithm. The trajectory of navigation can be acquired by GPS and it provides us with important data for ship's motion control experiments. The results of manoeuvring performance experiment have shown that the developed free running model ship can be used to verify the test of turning circle and zig-zag. For next step, other experimental researches such as ship collision avoidance system and automatic berthing can be considered in the future.

• 한국생산제조학회지
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• 제9권3호
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• pp.111-122
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• 2000

It is difficult to make shape library for featrue-based modeling because free-form surface is various shaped complicated To make modeling using similar shape feature-based model is easy and fast. Recently RE(Reverse Engineering) technolo-gy is very convenient method to get free-form surface. This study develops surface editor which makes surface modeling to manipulate control points and this study We study on the effective model data management using database system.