• 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.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.786-791
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• 2003

This paper proposes an adaptive control algorithm for nonholonomic mobile robots with unknown parameters and the proposed control method is used in numerical simulations for applying to a practical twowheeled welding mobile robot(WMR). The proposed adaptive controller to track an arbitrary given welding path is designed by using back-stepping technique and is derived for a nonlinear model under the assumption such that the system parameters are partially known. Moreover, the proposed adaptive control system is stable in the sense of Lyapunov stability. Inertia moments of system are considered to be unknown parameters and their values can be estimated simply by using update laws proposed in an adaptive control scheme of this research. The simulation results are provided to show the effectiveness of the accurate tracking capability of the proposed controller for two-wheeled welding mobile robot with a smooth curved reference welding path.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.3
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• pp.73-82
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• 1992

An industrial robot, installed real manufacturing processes an element of the system autmation, can be considered as an uncertain system due to dynamic uncertainties in inertial parameters and varying payloads. Most difficuties in controlling a robot manipulator are caused by the fact that the dynamic equations describing the motions of the manipulator are inherently nonlinear and heavily coupled effects between joints and associated links. Existing robot conrol systems have constant predefined gains and do not cover the complex dynamic interactions between manipulator joints. As a result, the manipulator is severly limited in range of application, speed of operation and variation of payload. The proposed controller is operated by adjusting its gains based on the response of the manipulator in such a way that the manipulator closely matches the reference model trajectories defined by the desinger. The proposed manipulator studied has two loops, an inner loop of model reference adaptive controller and an outer loop of state feedback controller with integral action to guarantee the stability of the adaptive scheme. This adaptation algorithm is based on the hyperstailiy approach with an improved Lyapunov function. The coupling among joints and the nonlinearity in the dynamic equation are explicitly considered. The designed manipulator controller shows good tracking performance in practical working environment, various load variations and parameter uncertainties.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.5
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• pp.8-17
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• 2002

This paper considers the problems of robust decentralized control for uncertain discrete-time large-scale systems with delays in interconnections and state feedback gain perturbations. Based on the Lyapunov method, the state feedback control design for robust stability is given in terms of solutions to a linear matrix inequality (LMI), and the measure of non-fragility in controller is presented. The solutions of the LMI can be easily obtained using efficient convex optimization techniques. A numerical example is included to illustrate the design procedures.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.2
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• pp.16-24
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• 2010

This paper is proposed a novel fuzzy disturbance observer based on backstepping method for nonlinear systems with unknown disturbance. Using fuzzy logic systems, a fuzzy disturbance observer with the disturbance observation input is introduced for unknown disturbance. To guarantee that the proposed disturbance observer estimates the unknown disturbance, the disturbance observation error dynamic system is employed. Under the framework of the backstepping design, the fuzzy disturbance observer is constructed recursively and an adaptive laws and the disturbance observation input are derived. Numerical examples are given to demonstrate the validity of our proposed disturbance observer for nonlinear systems.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.181-190
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• 2015

This paper presents a new approach to control the position of robot arm in workspace a robot manipulator under unknown system parameters and bounded disturbance inputs. To control the motion of the manipulator, an inverse dynamics control scheme was applied. Since parameters of the robot arm such as mass and inertia are not perfectly known, the difference between the actual and estimated parameters was considered as a external disturbance force. To identify the known parameters, an improved robust control algorithm is directly derived from 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 SCARA arm with four joints.

• Journal of the Korean Society for Precision Engineering
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• v.7 no.4
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• pp.85-102
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• 1990

Up to now, most robot control systems are very naive. They consist of a number of independent position-servo loops to control each joint angle separately. Those control systems have constant predefined gains and do not cover the complex dynamic interactions between manipulator joints. As a result, the manipulator is severely limited in range of application, speed of operation and variation of payload. This study proposed a new method to design a robot manipulator controller capable of tracking the reference trajectories of joint angles in a reasonable accuracy to cope with actual situations of varying payload, uncertain parameters. The adaptive model following control method has been used to improve existing robot manipulator controllers. The proposed controller is operated by adjusting its gains based on the response of the manipulator in such a way that the manipulator closely matches the reference model trajectories defined by the designer. The stability of adaptive controller is based on the Second Method of Lyapunov. The coupling among joints and the nonlinearity in the dynamic equation are explicitly considered. The designed manipulator controller shows good tracking performance under various load varia- tion and parameter uncertainties.

• Journal of Ocean Engineering and Technology
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• v.16 no.1
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• pp.8-15
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• 2002

This paper presents a neural net based nonlinear adaptive controller for an autonomous underwater vehicle (AUV). AUV's dynamics are highly nonlinear and their hydrodynamic coefficients vary with different operational conditions, so it is necessary for the high performance control system of an AUV to have the capacities of learning and adapting to the change of the AUV's dynamics. In this paper a linearly parameterized neural network is used to approximate the uncertainties of the AUV's dynamic, and the basis function vector of network is constructed according to th AUV's physical properties. A sliding mode control scheme is introduced to attenuate the effect of the neural network's reconstruction errors and the disturbances in AUV's dynamics. Using Lyapunov theory, the stability of the presented control system is guaranteed as well as the uniformly boundedness of tracking errors and neural network's weights estimation errors. Finally, numerical simulations for motion control of an AUV are performed to illustrate the effectiveness of the proposed techniques.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.162-169
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• 1999

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital Signal Processors. Digital signal processors DSPs. are micro-processors that are particularly developed for variables. Digital version of most advanced control algorithms can be defined as sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a biable computatinal tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for implementation of real-time control of robot system by the simulation and experiment.

• 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.