• International Journal of Aeronautical and Space Sciences
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• v.12 no.1
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• pp.43-56
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• 2011

An integrated system composed of guidance, navigation and control (GNC) system for autonomous proximity operations and the docking of two spacecraft was developed. The position maneuvers were determined through the integration of the state-dependent Riccati equation formulated from nonlinear relative motion dynamics and relative navigation using rendezvous laser vision (Lidar) and a vision sensor system. In the vision sensor system, a switch between sensors was made along the approach phase in order to provide continuously effective navigation. As an extension of the rendezvous laser vision system, an automated terminal guidance scheme based on the Clohessy-Wiltshire state transition matrix was used to formulate a "V-bar hopping approach" reference trajectory. A proximity operations strategy was then adapted from the approach strategy used with the automated transfer vehicle. The attitude maneuvers, determined from a linear quadratic Gaussian-type control including quaternion based attitude estimation using star trackers or a vision sensor system, provided precise attitude control and robustness under uncertainties in the moments of inertia and external disturbances. These functions were then integrated into an autonomous GNC system that can perform proximity operations and meet all conditions for successful docking. A six-degree of freedom simulation was used to demonstrate the effectiveness of the integrated system.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.2
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• pp.163-172
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• 2014

This paper proposes a novel guidance law based on the block backstepping sliding mode control and extended state observer (ESO), which also takes into account the autopilot dynamic characteristics of the near space interceptor (NSI), and the impact angle constraint of attacking the maneuvering target. Based on the backstepping control approach, the target maneuvers and the parameter uncertainties of the autopilot are regarded as disturbances of the outer loop and inner loop, respectively. Then, the ESO is constructed to estimate the target acceleration and the inner loop disturbance, and the block backstepping sliding model guidance law is employed, based on the estimated disturbance value. Furthermore, in order to avoid the "explosion of complexity" problem, first-order low-pass filters are also introduced, to obtain differentiations of the virtual control variables. The stability of the closed-loop guidance system is also proven, based on the Lyapunov theory. Finally, simulation results demonstrate that the proposed guidance law can not only overcome the influence of the autopilot dynamic delay and target maneuvers, but also obtain a small miss distance.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.8
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• pp.639-648
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• 2002

The stewart platform manipulator is a manipulator that has the closed-loop structure with an upper plate end-effector and a base frame. The stewart platform manipulator has the merit of high working accuracy and high stiffness compared with a serial manipulator. However, this is a complex structure, so controllability of the system is not so good. In this paper, we introduce a new robust motion control algorithm using partial state feedback for a class of nonlinear systems in the presence of modelling uncertainties and external disturbances. The major contribution of this work introduces the development and design of robust observer for the state and the perturbation, which is integrated into a variable structure controller(VSC) structure. The combination of controller/observer improves the control performance, because of the robust routine called sliding mode control with sliding perturbation observer(SMCSPO). Simulation and experiment are performed to apply to the manipulator. And their results show a high accuracy and a good performance.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.27-35
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• 1998

In this paper we will develope the immunized recurrent neural network control system with high robustness in dynamically changing environmental conditions. The variation of internal parameters of a system and external(or internal) disturbances can be considered as antigen, and the control input which can be regarded as antibody can be generated against uncertainties. The antibody will be generated from previous control informations and if a antibody for an antigen can not be generated from the corresponding information. the immune system produces another antibody by genetic operations. We apply this concept to a robot manipulator and evaluate the effectiveness of the above proposed system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.438-442
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• 2003

For an efficient sliding mode control system stability and chattering avoidance should be guaranteed. A continuation method using boundary layer is well known as one solution for this. However since not only model uncertainties and disturbances but also control task itself is variable. it is practically impossible to set controller parameters - control discontinuity, control bandwidth, boundary layer thickness - in advance. In this paper first an adaptation law of control discontinuity is introduced to assure system stability and then fuzzy logic based tuning algorithm of design parameters is applied based on monitored performance indices of tracking error, control chattering, and model precision. In the end maximum control bandwidth not exciting unmodeled dynamics and minimum control discontinuity, boundary layer thickness making system stable and free of chattering are found respectively. This eliminates control chattering and enhances control accuracy as much as possible under given control situation. In order to demonstrate the validity of the proposed algorithm safe headway maintenance control for autonomous transportation system is simulated. The control results show that the proposed algorithm guarantees system stability all the time and tunes control parameters consistently and in consequence implements an efficient control in terms of both accuracy and actuator chattering.

• 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.4
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• pp.1-7
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• 2002

QFT is a very practical design technique that emphasizes the use of feedback for achieving the desired system performances in despite of plant uncertainties and disturbances. The loop shaping procedure of QFT is employed to design the robust controller, until the desired bounds are satisfied. This paper presents an optimization algorithm for designing PID controller using the loop shaping of QFT. The proposed method identifies the parameter vector of PID controller from a linear system that develops from rearranging the two dimensional system matrices and output vectors obtained from the QFT bounds. The feasibilities of the suggested algorithm are illustrated with a turbine speed control problem.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.10
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• pp.2554-2566
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• 1994

Robot position/force control has been a difficult task owing to the interaction between a robot and an environment with a rather high stiffness. In addition to the dynamic instability, the interaction causes the following problem : 1) chattering at steady-state, 2) dynamic coupling effect of robot, and 3) performance degradation due to a titled environment. To solve the problem, the Time Delay Control(TDC), which has been known to be quiet robust to plant uncertainties and disturbances, has been applied. In conjunction to TDC, the following three ideas were also used : 1) To reduce the amplitude of the chattering at the steady state, a novel scheme was adopted to enhance the resolution type solution of A/D conversion for the force sensor. 2) To reduce the dynamic coupling, a trajectory type position command was tried on a comparative basis to the step command, as well as a more accurate mass matrix was used instead of the constant mass matrix. 3) And finally to improve the performance in the tilted environment, force derivatives instead of position derivatives were used in the TDC law. Computer simulations and experiments resulted in obvious improvements on the quality of the hybrid control, thereby clearly demonstrating the effectiveness of TDC with the proposed ideas.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.148-153
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• 1999

In this paper, it is proposed the adaptive-sliding mode control technique which is new approach to implement the robust control of industrial robot manipulator with external disturbances and parameter uncertainties. Over the past decade, the design of advanced control systems for industrial robotic manipulators has been a very active area of research and two major design categories have emerged. Sliding mode control is a well-known technique for robust control of uncertain nonlinear systems. The robustness of sliding model controllers can be shown in continuous time, but digital implementation may not preserve robustness properties because the sampling process limits the existence of a true sliding mode. Adaptive control algorithm is designed by using the principle of the model reference adaptive control method based upon the hyperstability theory. The proposed control scheme has a simple structure is computationally fast and does not require knowledge of the complex dynamic model or the parameter values of the manipulator or the payload. Simulation results how that the proposed method not only improves the performance of the system but also reduces the chattering problem of sliding mode control. Consequently, it is expected that the new adaptive sliding mode control algorithm will be suited for various practical applications of industrial robot control system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.2
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• pp.113-118
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• 2010

This paper proposed an ECAM (Electronic cam) control system which has simple and general structure. The proposed cam controller adopted the linear and polynomial curve-fitting method to generates a smooth cam profile curve function. Smooth motion trajectory of master actuator guarantees the good performance of slave motion and has an important effect on the interpolation quality of ECAM. The auto-tuning PID velocity controller was applied to overcome the uncertainties in ECAM, and the gains of the controller are updated continuously to ensure the consistency of system performance under varying working conditions. The robustness of system against the varying load torque disturbances and noises is guaranteed by using the load torque disturbance observer to suppress the disturbance on master actuator. The velocity compensator was applied to compensate the degradation of performance of slave motion caused from the varying driving speed of master motion. The stability and validity of the proposed ECAM control system was verified by simulation results.