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

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.5
• /
• pp.401-406
• /
• 2012

This paper considers multi-agent systems with interval time-varying delays and switching interconnection topology. By construction of a suitable Lyapunov-Krasovskii's functional, new delay-dependent consensus control conditions for the systems are established in terms of LMIs (Linear Matrix Inequalities) which can be easily solved by various effective optimization algorithms. One numerical example is given to illustrate the effectiveness of the proposed methods.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.3
• /
• pp.24-32
• /
• 1997

This paper is on a discrete controller order reduction with the closed-loop stability and performance guaranteed. to achieve this, after finding the solutionsof two lyapunov inequalities and balancing the full order controller system, we find the reudced order controlers using the balanced truncation (BT) and the balanced singular perturbation approximation (BSPA). When the solutions of the two lyapunov inequalities exist, it is shown that the resulting controllers guarantee the closed-loop stability, and .inf.-norm error bounds are derived for the closed-loop performance region for the BT and in low frequency region for the BSPA. Finally, a numerical example is given to illustrate the validity of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.60 no.2
• /
• pp.376-382
• /
• 2011

This paper proposes delay-dependent stability conditions of the fuzzy Markovian jumping Hopfield neural networks of neutral type with time-varying delays. By constructing a suitable Lyapunov-Krasovskii's (L-K) functional and utilizing Finsler's lemma, new delay-dependent stability criteria for the systems are established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. A numerical example is given to illustrate the effectiveness of the proposed methods.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.1
• /
• pp.408-421
• /
• 2015

Because the wheel of V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor (PMSM) has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming job. In order to overcome difficulties for design of the linear controllers, a hybrid recurrent Chebyshev neural network (NN) control system is proposed to control for a PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Chebyshev NN control system consists of an inspector control, a recurrent Chebyshev NN control with adaptive law and a recouped control. Moreover, the online parameters tuning methodology of adaptive law in the recurrent Chebyshev NN can be derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, the optimal learning rate of the parameters based on discrete-type Lyapunov function is derived to achieve fast convergence. The recurrent Chebyshev NN with fast convergence has the online learning ability to respond to the system's nonlinear and time-varying behaviors. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.38 no.4
• /
• pp.335-341
• /
• 2010

To improve the performance of inner loop based on PD controller for a unmanned helicopter, neural networks are applied. The performance of PD controller designed on the response characteristics of error dynamics decreases because of uncertain nonlinearities of the system. The nonlinearities are decoupled to modified dynamic inversion model(MDIM) and are compensated by the neural networks. For the training of the neural networks, online weight adaptation laws which are derived from Lyapunov's direct method are used to guarantee the stability of the controller. The results of the improved performance of PD controller by neural networks are illustrated in the simulation of unmanned helicopter with nonlinearities,

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.35 no.11
• /
• pp.990-998
• /
• 2007

Adaptive output feedback control technique using Neural Networks(NN) is proposed for uncertain nonlinear Multi-Input Multi-Output(MIMO) systems. Modified Dynamic Inversion Model(MDIM) is introduced to decouple uncertain nonlinearities from inversion-based control input. MDIM consists of approximated dynamic inversion model and inversion model error. One NN is applied to compensate the MDIM of the system. The output of the NN augments the tracking controller which is based upon a filtered error approximation with online weight adaptation laws which are derived from Lyapunov's direct method to guarantee tracking performance and ultimate boundedness. Several numerical results are illustrated in the simulation of Van der Pol system and unmanned helicopter with model uncertainties.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.4
• /
• pp.349-354
• /
• 2015

This paper presents the stability conditions of the Takagi-Sugeno (T-S) fuzzy sampled-data filter. The error system between the T-S fuzzy system and fuzzy filter is presented. In the sense of the Lyapunov stability analysis, the stability conditions are given, which can be represented in terms of linear matrix inequalities (LMIs). The proposed stability conditions utilize the different approach from the conventional methods, and have better performance than that of the conventional ones. The simulation example is given to show the effectiveness of the proposed method.

• Korean Journal of Applied Biomechanics
• /
• v.31 no.1
• /
• pp.30-36
• /
• 2021

Objective: The objective of the present study was to analyze the relationship between strength of the lower extremity's joints and their local dynamic stability (LDS) of gait in elderly women. Method: Forty-five elderly women participated in this study. Average age, height, mass, and preference walking speed were 73.5±3.7 years, 153.8±4.8 cm, 56.7±6.4 kg, and 1.2±0.1 m/s, respectively. They were tested torque peak of the knee and ankle joints with a Human Norm and while they were walking on a treadmill at their preference speed for a long while, kinematic data were obtained using six 3-D motion capture cameras. LDS of the lower extremity's joints were calculated in maximum Lyapunov Exponent (LyE). Correlation coefficients between torque of the joints and LyE were obtained using Spearman rank. Level of significance was set at p<.05. Results: Knee flexion torque and its LDS was negatively associated with adduction-abduction and flexion-extension movement (p<.05). In addition, ratio of the knee flexion torque to extension and LDS was negatively related to internal-external rotation. Conclusion: In conclusion, knee flexion strength should preferentially be strengthened to increase LDS of the lower extremity's joints for preventing from small perturbations during walking in elderly women.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.6
• /
• pp.915-927
• /
• 2008

Networked control systems(NCSs) have gained increasing attention in recent years due to their advantages and potential applications. The network Quality-of-Service(QoS) in NCSs always fluctuates due to changes of the traffic load and available network resources. To handle the network QoS variations problem, this paper presents an intelligent scheduling control method for NCSs, where the sampling period and the control parameters are simultaneously scheduled to compensate the effect of QoS variation on NCSs performance. For NCSs with network-induced delays and packet dropouts, a discrete-time switch model is proposed. By defining a sampling-period-dependent Lyapunov function and a common quadratic Lyapunov function, the stability conditions are derived for NCSs in terms of linear matrix inequalities(LMIs). Based on the obtained stability conditions, the corresponding controller design problem is solved and the performance optimization problem is also investigated. Simulation results are given to demonstrate the effectiveness of the proposed approaches.