• International Journal of Control, Automation, and Systems
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• v.6 no.5
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• pp.689-701
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• 2008

In this paper, a fully adaptive feedforward feedback synchronized tracking control approach is developed for precision tracking control of 6 degree of freedom (6DOF) Stewart Platform. The proposed controller is designed in decentralized form for implementation simplicity. Interconnections among different subsystems and gravity effect are eliminated by the feedforward control action. Feedback control action guarantees the stability of the system. The gains of the proposed controller can be updated on line without requiring any prior knowledge of Stewart Platform manipulator. Thus the control approach is claimed to be fully adaptive. By employing cross-coupling error technology, the proposed approach can guarantee both of position error and synchronization error converge to zero asymptotically. Because the actuators work in synchronous manner, the tracking performances are improved. The corresponding stability analysis is also presented in this paper. Finally, simulation is demonstrated to verify the effectiveness of the proposed approach.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.584-596
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• 2019

This paper presents a Modified Adaptive Complementary Sliding Mode Control (MACSMC) system for the longitudinal motion control of the Fully-Submerged Hydrofoil Craft (FSHC) in the presence of time varying disturbance and uncertain perturbations. The nonlinear disturbance observer is designed with less conservatism that only boundedness of the derivative of the disturbance is required. Then, a complementary sliding mode control system combined with adaptive law is designed to reduce the bound of stabilization error with fast convergence. In particularly, the modified complementary sliding mode surface which contains the estimation of the disturbance can reduce the switching gain and retain the normal performance of the system. Moreover, a hyperbolic tangent function contained in the control law is utilized to attenuate the chattering of the actuator. The global asymptotic stability of the closed-loop system is demonstrated utilizing the Lyapunov stability theory. Ultimately, the simulation results show the effectiveness of the proposed approach.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.755-767
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• 2003

This paper proposes a robust fault-tolerant control framework for robot manipulators to maintain the required performance and achieve task completion in the presence of both partial joint failures and complete joint failures and uncertainties. In the case of a complete joint failure or free-swinging joint failure causing the complete loss of torque on a joint, a fully-actuated robot manipulator can be viewed as an underactuated robot manipulator. To detect and identify a complete actuator failure, an on-line fault detection operation is also presented. The proposed fault-tolerant control system contains a robust adaptive controller overcoming partial joint failures based on robust adaptive control methodology, an on-line fault detector detecting and identifying complete joint failures, and a robust adaptive controller overcoming partial and complete joint failures, and so eventually it can face and overcome joint failures and uncertainties. Numerical simulations are conducted to validate the proposed robust fault-tolerant control scheme.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.54-67
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• 2003

In this paper, a novel estimation technique for a robust adaptive control scheme is presented for a class of uncertain nonlinear systems with a general set of uncertainty. For a class of introduced more extended semi-strict feedback forms which generalize the systems studied in recent years, a novel estimation technique is proposed to estimate the states of the fully nonlinear unmodeled dynamics without stringent conditions. With the introduction of powerful functions, the estimation error can be tuned to a desired small region around the origin via the estimator parameters. In addition, with some effective functions, a modified adaptive backstepping for dynamic uncertainties is presented to drive the output to an arbitrarily small region around the origin by an appropriate choice of the design parameters. With our proposed schemes, we can remove or relax the assumptions of the existing results.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.276-280
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• 2017

This paper presents computationally efficient image compression for wireless transmission of high-definition video, to adaptively utilize available channel bandwidth and improve image quality. The method indirectly predicts an unknown available channel bandwidth by monitoring encoder buffer status, and adaptively controls a quantization parameter to fully utilize the bandwidth. Experimental results show that the proposed method is robust to variations in channel bandwidth.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the adaptive learning neuro fuzzy controller and ANN controller.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.303-309
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• 2016

This paper presents a channel-adaptive rate control algorithm for low delay video coding. The main goal of the proposed method is to adaptively use the unknown available channel bandwidth while reducing the end-to-end delay between encoder and decoder. The key idea of the proposed algorithm is for the status of the encoder buffer to indirectly reflect the mismatch between the available channel bandwidth and the generated bitrate. Hence, the proposed method fully utilizes the unknown available channel bandwidth by monitoring the encoder buffer status. Simulation results show that although the target bitrate mismatches the available channel bandwidth, the encoder efficiently adapts the given available bandwidth to improve the peak signal-to-noise ratio.

• Journal of Power Electronics
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• v.14 no.4
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• pp.704-711
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• 2014

This paper proposes a combined fuzzy adaptive sliding-mode voltage controller (FASVC) for a three-phase UPS inverter. The proposed FASVC encapsulates two control terms: a fuzzy adaptive compensation control term, which solves the problem of parameter uncertainties, and a sliding-mode feedback control term, which stabilizes the error dynamics of the system. To extract precise load current information, the proposed method uses a conventional load current observer instead of current sensors. In addition, the stability of the proposed control scheme is fully guaranteed by using the Lyapunov stability theory. It is shown that the proposed FASVC can attain excellent voltage regulation features such as a fast dynamic response, low total harmonic distortion (THD), and a small steady-state error under sudden load disturbances, nonlinear loads, and unbalanced loads in the existence of the parameter uncertainties. Finally, experimental results are obtained from a prototype 1 kVA three-phase UPS inverter system via a TMS320F28335 DSP. A comparison of these results with those obtained from a conventional sliding-mode controller (SMC) confirms the superior transient and steady-state performances of the proposed control technique.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.523-525
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• 2006

Adaptive neural state-feedback controllers for the fully nonaffine pure-feedback nonlinear system are presented in this paper. By reformulating the original pure-feedback system to a standard normal form with respect to newly defined state variables, the proposed controllers require no backstepping design procedures. Avoiding backstepping makes the controller structure and stability analysis considerably to be simplified. The proposed controllers employ only one neural network to approximate unknown ideal controllers, which highlights the simplicity of the proposed neural controller.

• Proceedings of the KIPE Conference
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• 2017.07a
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• pp.120-121
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• 2017

This paper proposes an enhanced distributed generation (DG) unit with an adaptive virtual impedance control approach in order to address the inaccurate reactive power sharing problem. The proposed method can adaptively regulate the DG virtual impedance, and the effect of the mismatch in feeder impedances is compensated to share the reactive power accurately. The proposed control strategy is fully distributed and the need for the microgrid central controller is eliminated. Furthermore, the proposed method can be directly implemented without requirement of pre-knowledge of the feeder impedances. Simulations are performed to validate the effectiveness of the proposed control approach.