Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employe...Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.展开更多
The arm driven inverted pendulum system is a highly nonlinear model, muhivariable and absolutely unstable dynamic system so it is very difficult to obtain exact mathematical model and balance the inverted pendulum wit...The arm driven inverted pendulum system is a highly nonlinear model, muhivariable and absolutely unstable dynamic system so it is very difficult to obtain exact mathematical model and balance the inverted pendulum with variable position of the ann. To solve this problem, this paper presents a mathematical model for arm driven inverted pendulum in mid-position configuration and an adaptive gain scheduling linear quadratic regulator control method for the stabilizing the inverted pendulum. The proposed controllers for arm driven inverted pendulum are simulated using MATLAB-SIMULINK and implemented on an experiment system using PIC 18F4431 mieroeontroller. The result of experiment system shows the control performance to be very good in a wide range stabilization of the arm position.展开更多
This paper proposes a fast-locking bang-bang phase-locked loop（BBPLL）. A novel adaptive loop gain controller（ALGC） is proposed to increase the locking speed of the BBPLL. A novel bang-bang phase/frequency detector...This paper proposes a fast-locking bang-bang phase-locked loop（BBPLL）. A novel adaptive loop gain controller（ALGC） is proposed to increase the locking speed of the BBPLL. A novel bang-bang phase/frequency detector（BBPFD） with adaptive-mode-selective circuits is proposed to select the locking mode of the BBPLL during the locking process. Based on the detected results of the BBPFD, the ALGC can dynamically adjust the overall gain of the loop for fast-locking procedure. Compared with the conventional BBPFD, only a few gates are added in the proposed BBPFD. Therefore, the proposed BBPFD with adaptive-mode-selective circuits is realized with little area and power penalties. The fast-locking BBPLL is implemented in a 65 nm CMOS technology. The core area of the BBPLL is 0.022 mm;. Measured results show that the BBPLL operates at a frequency range from0.6 to 2.4 GHz. When operating at 1.8 GHz, the power consumption is 3.1 mW with a 0.9-V supply voltage. With the proposed techniques, the BBPLL achieves a normalized locked time of 1.1μs @ 100 MHz frequency jump.The figure-of-merit of the fast-locking BBPLL is-334 dB.展开更多
基金Project(60634020) supported by the National Natural Science Foundation of China
文摘Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.
文摘The arm driven inverted pendulum system is a highly nonlinear model, muhivariable and absolutely unstable dynamic system so it is very difficult to obtain exact mathematical model and balance the inverted pendulum with variable position of the ann. To solve this problem, this paper presents a mathematical model for arm driven inverted pendulum in mid-position configuration and an adaptive gain scheduling linear quadratic regulator control method for the stabilizing the inverted pendulum. The proposed controllers for arm driven inverted pendulum are simulated using MATLAB-SIMULINK and implemented on an experiment system using PIC 18F4431 mieroeontroller. The result of experiment system shows the control performance to be very good in a wide range stabilization of the arm position.
基金Project supported by the National Nature Science Foundation of China(Nos.61331003,61474108)the National Key Technology Research and Development Program of the Ministry of Science and Technology of China(No.2016ZX03001002)
文摘This paper proposes a fast-locking bang-bang phase-locked loop（BBPLL）. A novel adaptive loop gain controller（ALGC） is proposed to increase the locking speed of the BBPLL. A novel bang-bang phase/frequency detector（BBPFD） with adaptive-mode-selective circuits is proposed to select the locking mode of the BBPLL during the locking process. Based on the detected results of the BBPFD, the ALGC can dynamically adjust the overall gain of the loop for fast-locking procedure. Compared with the conventional BBPFD, only a few gates are added in the proposed BBPFD. Therefore, the proposed BBPFD with adaptive-mode-selective circuits is realized with little area and power penalties. The fast-locking BBPLL is implemented in a 65 nm CMOS technology. The core area of the BBPLL is 0.022 mm;. Measured results show that the BBPLL operates at a frequency range from0.6 to 2.4 GHz. When operating at 1.8 GHz, the power consumption is 3.1 mW with a 0.9-V supply voltage. With the proposed techniques, the BBPLL achieves a normalized locked time of 1.1μs @ 100 MHz frequency jump.The figure-of-merit of the fast-locking BBPLL is-334 dB.
