• Journal of Power Electronics
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• v.19 no.3
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• pp.771-783
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• 2019

Due to nonlinear-synthetic uncertainty including the total unknown nonlinear load torque, the total parameter variation and the fixed load torque, a synchronous reluctance motor (SynRM) driving a continuously variable transmission (CVT) system causes a lot of nonlinear effects. Linear control methods make it hard to achieve good control performance. To increase the control performance and reduce the influence of nonlinear time-synthetic uncertainty, an admixed recurrent Gegenbauer orthogonal polynomials neural network (ARGOPNN) with a modified particle swarm optimization (MPSO) control system is proposed to achieve better control performance. The ARGOPNN with a MPSO control system is composed of an observer controller, a recurrent Gegenbauer orthogonal polynomial neural network (RGOPNN) controller and a remunerated controller. To insure the stability of the control system, the RGOPNN controller with an adaptive law and the remunerated controller with a reckoned law are derived according to the Lyapunov stability theorem. In addition, the two learning rates of the weights in the RGOPNN are regulating by using the MPSO algorithm to enhance convergence. Finally, three types of experimental results with comparative studies are presented to confirm the usefulness of the proposed ARGOPNN with a MPSO control system.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.1
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• pp.75-84
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• 2013

In this paper, the guidance law for formation flight and collision avoidance of multiple Unmanned Aerial Vehicle (UAV)s is proposed. To construct the physically comprehensible guidance law for formation flight, the virtual structure approach is used. To develop a guidance law for collision avoidance considering both other UAVs and unknown static obstacles, a geometric approach using information such as a relative position vector is utilized. Through the Lyapunov theorem, the stability of the proposed guidance law is proved. To combine guidance commands, the concept of the elastic weighting factor inspired by the elastic behavior of shape memory polymer, which tends to regain its original shape after deformation, is introduced. By using the concept of elastic weighting factor, multiple UAVs are able to cope actively with the situation of a collision between both UAVs and static obstacles during the formation flight. To verify the performance of the proposed method, numerical simulations are performed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.11
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• pp.945-954
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• 1990

In this paper, we presents a robust adaptive control system design method in the work coordinate of the robot arm for conveyor tracking. In the design, if the weighting function $L_K$ is smaller than the design parameter then the transient characteristics of system becomes stable, if $L_K$ is larger than then the system becomes unstable. Proposed design method presented here is based on model referenece adaptive control and Popov stability theorem. By the utiliza/tion of an auxilary input, it is improved the transent characteristics of the system in comparison with the conventional model reference adptive control, since the rate of V and V(t) is large. The usefulness of a proposed design method has been confirmed by computer simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.46-50
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• 2013

This paper proposes an adaptive fault accommodation control approach for flexible-joint (FJ) robots with multiple actuator faults. It is assumed that the value and occurrence time of multiple actuator faults are unknown. An adaptive fault accommodation control scheme with prescribed performance bounds, which characterize the convergence rate and maximum overshoot of tracking errors, is designed to accommodate the actuator faults. From the Lyapunov stability theorem, it is proved that all signals of the closed-loop system are semi-globally uniformly ultimately bounded and tracking errors are preserved within prescribed performance bounds regardless of actuator faults.

• Journal of Power Electronics
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• v.16 no.2
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• pp.598-609
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• 2016

The good control performance of permanent magnet linear synchronous motor (LSM) drive systems is difficult to achieve using linear controllers because of uncertainty effects, such as fictitious forces. A backstepping control system using adaptive modified recurrent Laguerre orthogonal polynomial neural network uncertainty observer (OPNNUO) is proposed to increase the robustness of LSM drive systems. First, a field-oriented mechanism is applied to formulate a dynamic equation for an LSM drive system. Second, a backstepping approach is proposed to control the motion of the LSM drive system. With the proposed backstepping control system, the mover position of the LSM drive achieves good transient control performance and robustness. As the LSM drive system is prone to nonlinear and time-varying uncertainties, an adaptive modified recurrent Laguerre OPNNUO is proposed to estimate lumped uncertainties and thereby enhance the robustness of the LSM drive system. The on-line parameter training methodology of the modified recurrent Laguerre OPNN is based on the Lyapunov stability theorem. Furthermore, two optimal learning rates of the modified recurrent Laguerre OPNN are derived to accelerate parameter convergence. Finally, the effectiveness of the proposed control system is verified by experimental results.

• Journal of Power Electronics
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• v.11 no.5
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• pp.719-725
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• 2011

This paper describes a novel Direct Torque Control (DTC) method for adjustable speed Doubly-Fed Induction Machine (DFIM) drives which is supplied by a two-level Space Vector Modulation (SVM) voltage source inverter (DTC-SVM) in the rotor circuit. The inverter reference voltage vector is obtained by using input-output feedback linearization control and a DFIM model in the stator a-b axes reference frame with stator currents and rotor fluxes as state variables. Moreover, to make this nonlinear controller stable and robust to most varying electrical parameter uncertainties, a two layer recurrent Artificial Neural Network (ANN) is used to estimate a certain function which shows the machine lumped uncertainty. The overall system stability is proved by the Lyapunov theorem. It is shown that the torque and flux tracking errors as well as the updated weights of the ANN are uniformly ultimately bounded. Finally, effectiveness of the proposed control approach is shown by computer simulation results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.2
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• pp.98-106
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• 2013

This paper proposes fault tolerant control laws using sliding mode control and adaptation laws for a satellite with reaction wheel faults. Considering system parameter errors and faults uncertainties in the dynamics of satellite, the control laws were designed. It was assumed that only reaction wheel failures occurred as faults. The reaction wheel faults were reflected in the multiply form. Because the proposed control laws satisfy the Lyapunov stability theorem, the stability is guaranteed. Through computer simulation, it was assured that the proposed adaptive sliding mode controller has a better performance than the existing sliding mode controller under unstable angular rates.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.168-180
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• 2018

In order to achieve the high quality output voltage of single-phase voltage source inverters, in this paper an Adaptive Complementary Sliding Mode Control (ACSMC) is proposed. Firstly, the dynamics model of the single-phase inverter with lumped uncertainty including parameter variations and external disturbances is derived. Then, the conventional Sliding Mode Control (SMC) and Complementary Sliding Mode Control (CSMC) are introduced separately. However, when system parameters vary or external disturbance occurs, the controlling performance such as tracking error, response speed et al. always could not satisfy the requirements based on the SMC and CSMC methods. Consequently, an ACSMC is developed. The ACSMC is composed of a CSMC term, a compensating control term and a filter parameters estimator. The compensating control term is applied to compensate for the system uncertainties, the filter parameters estimator is used for on-line LC parameter estimation by the proposed adaptive law. The adaptive law is derived using the Lyapunov theorem to guarantee the closed-loop stability. In order to decrease the control system cost, an inductor current estimator is developed. Finally, the effectiveness of the proposed controller is validated through Matlab/Simulink and experiments on a prototype single-phase inverter test bed with a TMS320LF28335 DSP. The simulation and experimental results show that compared to the conventional SMC and CSMC, the proposed ACSMC control strategy achieves more excellent performance such as fast transient response, small steady-state error, and low total harmonic distortion no matter under load step change, nonlinear load with inductor parameter variation or external disturbance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.284-291
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• 2021

In this paper, we study the consensus problem for multi-agent systems with input saturations. The goal of consensus is to achieve a swarming behavior of multi-agent systems by reaching the agreement through information exchange. This paper considers agents modeled by first-order dynamics with input saturations. In order to guarantee the global convergence of the agents, it is assumed that the agents are stable. Moreover, considering the disturbances, we propose the PI based duplex control method to achieve the consensus. The proposed P controller and I controller are composed of different information network. Then, we investigate the conditions of the information networks and the control gains of P, I controllers to achieve the consensus applying the Lyapunov stability theorem and the Lasalle's Invariance Principle. Finally, we conduct the simulations to validate the theoretical results.