• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2001.10a
• /
• pp.134-139
• /
• 2001

This paper presents a new scheme of neural network controller to improve the robustuous of robot manipulator using digital signal processors. Digital signal processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of 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 viable computational tool in digital implementation of sophisticated controllers. During past decade it was proposed the well-established theorys for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Perforating of the proposed controller is illustrated. This paper describes a new approach to the design of adaptive controller and implementation of real-time control for assembling robotic manipulator using digital signal processor. Digital signal processors used in implementing real time adaptive control algorithm are TMS320C50 series made in TI'Co..

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.468-472
• /
• 2003

The lifting technique is a standard control procedure that is commonly applied to dual-rate systems, where a critical difficulty is that care must be taken so that the resulting equivalent system preserves the causality constraint between the control signal and the measured output. To overcome this difficulty, the most attractive result has been suggested by defining control time sequences as the union of sample and hold time sequences. However, the sacrifice of regular control period scheme results in some serious disadvantages; restrictions on the implementation to hardware and the corresponding inefficient control scheme. On the contrary, this paper proposes a novel dual-rate control technique, which redescribes the system as a control-rate-based system having regular control period and designs the controller, with no causality constraint, through Linear Matrix Inequality (LMI) formulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2001.04a
• /
• pp.255-260
• /
• 2001

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C31 is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Performance of the neural controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1996.10a
• /
• pp.133-137
• /
• 1996

This paper presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have bevome increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking arre indispensable capabilities for their versatile application. the need to meet demanding control requirement in increasingly complex dynamical control systems under sygnificant uncertainties leads toward design of implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme the ntworks intrduced are neural nets with dynamic neurouns whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure fast in computation and suitable for implementation of real-time control, Performance of the neural controller is illustrated by simulation and experimental results for a SCAEA robot.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.4
• /
• pp.150-158
• /
• 2018

In this paper, we introduce an electric vehicle charging system using power line communication and propose a method to correct the error by applying a deep learning algorithm when an error occurs in the control signal of an electric vehicle charging system using power line communication. The error detection and correction of the control signal can be solved through the conventional error correcting code schemes, but the error is detected and corrected more efficiently by using the deep learning based error correcting code scheme. Therefore, we introduce deep learning based error correction code scheme and apply this scheme to electric vehicle charging system using power line communication. we proceed simulation and confirm performance with bit error rate. we judge whether the deep learning based error correction code scheme is more effective than the conventional schemes.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.12
• /
• pp.1220-1230
• /
• 2006

In this paper, we propose a flew technique to the design and real-time control of an adaptive controller for robotic manipulator based on digital signal processors. The Texas Instruments DSPs(TMS320C80) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for a dual arm robot manipulator with eight joints. joint space and cartesian space.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.49 no.7
• /
• pp.345-349
• /
• 2000

This paper presents an extended integral control with the PI controller by introducing the delay and decaying factors. The extended integral control scheme is developed by substituting the proportional convolution integral control for the PI(Proportional Integral) control. So far, the integral part of PI controller produces a signal that is proportional to the time integral of the input signal to the controller. The steady-state operation points are affected forever by errors in the past due to the input signal containing the information of the error in the past. These phenomena may cause some disturbances for other control purposes related to the given PI control. Introduction of forgetting factors to the error in the past can resolve the disturbance problems. Various forgetting factors are developed using the delay elements, the decaying factors, and the combination of the delay and decaying factors. The proposed various extended integral control schemes can be applicable to the corresponding PI control designs in which the error in the past may badly affect the current steady-state operation points and may cause some disturbances for other control purposes.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.19 no.5
• /
• pp.470-474
• /
• 2014

This study proposes an improved synchronization control scheme for a low-cost 400Hz power supply for a no-break power transfer system. In the case of aircraft applications, the 400Hz power supply called ground power units is accepted and used as the external electrical power system during stopovers on ground. A momentary break in the supply occurs when shifting from one power source to another. To allow shifting without a break in the supply, the two power sources are momentarily connected in parallel. The proposed synchronization control is achieved by connecting an existing synchronization bus to the voltage zero-crossing signal of a generator power with discrete logic ICs and analog circuits. Therefore, unlike expensive controllers, such as DSP and CAN, the proposed control scheme is rather simple and may decrease operational cost. The practical feasibility of the proposed control scheme is proven by experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.5
• /
• pp.1634-1652
• /
• 2022

Due to the increasing need for data sharing in the age of big data, how to achieve data access control and implement user permission revocation in the blockchain environment becomes an urgent problem. To solve the above problems, we propose a novel blockchain-based data sharing scheme (BDSS) with fine-grained access control and permission revocation in this paper, which regards the medical environment as the application scenario. In this scheme, we separate the public part and private part of the electronic medical record (EMR). Then, we use symmetric searchable encryption (SSE) technology to encrypt these two parts separately, and use attribute-based encryption (ABE) technology to encrypt symmetric keys which used in SSE technology separately. This guarantees better fine-grained access control and makes patients to share data at ease. In addition, we design a mechanism for EMR permission grant and revocation so that hospital can verify attribute set to determine whether to grant and revoke access permission through blockchain, so it is no longer necessary for ciphertext re-encryption and key update. Finally, security analysis, security proof and performance evaluation demonstrate that the proposed scheme is safe and effective in practical applications.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1997.04a
• /
• pp.125-130
• /
• 1997

In this paper, it is presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C3x is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, andsuitable for implementation of robust control.