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RISS 인기검색어
- 검색키워드
- 저자 : Shiuh-Jer Huang (검색결과 5 건)
- 무료
- 기관 내 무료
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Intelligent humanoid mobile robot with embedded control and stereo visual feedback
Shiuh-Jer Huang,Sheng Liu,Chun-His Wu 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.9
Generally, an intelligent humanoid robot system should have dual arms, mobile ability and stereo vision for establishing the interactivecapability in unknown environment. Stereo vision system detects unknown objects or obstacles from their visual range and estimates 3Dcoordinates in a working environment. Dual arms and mobile platform are employed to approach the detected object and execute interactionbased on visual servo information. Here a wheel-based 10 DOF dual arms mobile robot with FPGA hardware control structure and aDigital signal processor (DSP) based CMOS stereo vision system was designed and built to construct an distributed embedded low costvisual guided intelligent robotic system. The intelligent fuzzy sliding mode control strategy was employed to design the robot arm andplatform motion control system. The experimental results show that the proposed stereo vision algorithm has good recognition ability andaccuracy. Each joint angular tracking error of humanoid robot can keep within 0.05o by using distributed FSMC controller.
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Vision guided dual arms robotic system with DSP and FPGA integrated system structure
Shiuh-Jer Huang,Jian-Cheng Huang 대한기계학회 2011 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.25 No.8
Usually, a humanoid robot has two arms and stereo vision system to execute human daily actions. It has complicate mechanism and mechatronics control system structure. The hardware control structure should be planned ingeniously to execute the complicate computation of 3D image processing and manipulate a multi degree of freedom dual arms motion control, especially for mobile robot system. Here a 7 DOF dual arms robot with FPGA hardware control structure and a digital signal processor (DSP) based CMOS stereo vision system are designed and built in our lab. The intelligent fuzzy sliding mode control strategy is employed to establish the visual guided robotic motion control software. This low cost humanoid robotic system has compact control structure and mechanism integration for mobile application purpose. Object detecting and tracking schemes in 3D space were developed for locating the target position and then guided the robot arm to pick and place objects or track the specified moving target. Experimental results show that this delicate robotic system has basic humanoid function.
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Un-symmetric Input Temperature Control by using Fuzzy Sliding Mode Controller with Gain Auto-tuning
Shiuh-Jer Huang,Hung-Yi Chen 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.2
In order to obtain the desired product quality, temperature is an important control parameter in chemical and semiconductor manufacturing processes. Generally, the temperature control system has nonlinear time-varying, slow response speed, time-delay and un-symmetric control input dynamic characteristics. It is difficult to accurately establish the dynamic model for designing a general purpose temperature controller to achieve good control performance. Here a model-free fuzzy sliding mode control strategy is employed to design an intelligent temperature controller with gain-scheduling scheme or gain auto-tuning algorithm for a closed chamber with heater one-way input only. The concept of gain scheduling is employed to adjust the mapping ranges of the input and output fuzzy membership functions during the control process for improving the transient and steady-state control performances. The experimental results show that the steady state error of the step input response is always less than 0.2oC without overshoot by using this intelligent control schemes. It is suitable for industrial temperature control systems.
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Intelligent Robotic Gripper with Adaptive Grasping Force
Shiuh-Jer Huang,Wei-Han Chang,Jui-Yiao Su 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.5
The on-off control robot gripper is widely employed in pick-and-place operations in Cartesian space forhandling hard objects between two positions. Without contact force monitoring, it can not be applied in fragile orsoft objects handling. Although, an appropriate grasping force or gripper opening for each target could be searchedby trial-and-error process, it needs expensive force/torque sensor or an accurate gripper position controller. It hastoo expensive and complex control strategy disadvantages for most of industrial applications. In addition, it cannot overcome the target slip problem due to mass uncertainty and dynamic factor. Here, an intelligent gripper isdesigned with embedded distributed control structure for overcoming the uncertainty of object’s mass and soft/hardfeatures. A communication signal is specified to integrate both robot arm and gripper control kernels for executingthe robotic position control and gripper force control functions in sequence. An efficient model-free intelligentfuzzy sliding mode control strategy is employed to design the position and force controllers of gripper, respectively. Experimental results of pick-and-place soft and hard objects with grasping force auto-tuning and anti-slip controlstrategy are shown by pictures to verify the dynamic performance of this distributed control system. The positionand force tracking errors are less than 1 mm and 0.1 N, respectively.
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Adaptive neural network controller for the molten steel level control of strip casting processes
Hung-Yi Chen,Shiuh-Jer Huang 대한기계학회 2010 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.24 No.3
The twin-roll strip casting process is a steel-strip production method which combines continuous casting and hot rolling processes. The production line from molten liquid steel to the final steel-strip is shortened and the production cost is reduced significantly as compared to conventional continuous casting. The quality of strip casting process depends on many process parameters, such as molten steel level in the pool, solidification position, and roll gap. Their relationships are complex and the strip casting process has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive neural network controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure combined with a sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions by using the reaching condition of a specified sliding surface. This surface has the on-line learning ability to respond to the system’s nonlinear and time-varying behaviors. Since this model-free controller has a simple control structure and small number of control parameters, it is easy to implement. Simulation results, based on a semiexperimental system dynamic model and parameters, are executed to show the control performance of the proposed intelligent controller. In addition, the control performance is compared with that of a traditional PID controller.
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