9,869 research outputs found
A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems
This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding
Mode Controller for the control of dynamic uncertain systems. The proposed
controller combines the advantages of Second order Sliding Mode Control, Fuzzy
Logic Control and Adaptive Control. The reaching conditions, stability and
robustness of the system with the proposed controller are guaranteed. In
addition, the proposed controller is well suited for simple design and
implementation. The effectiveness of the proposed controller over the first
order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based
simulations performed on a DC-DC Buck converter. Based on this comparison, the
proposed controller is shown to obtain the desired transient response without
causing chattering and error under steady-state conditions. The proposed
controller is able to give robust performance in terms of rejection to input
voltage variations and load variations.Comment: 14 page
Fuzzy second order sliding mode control of a unified power flow controller
Purpose. This paper presents an advanced control scheme based on fuzzy logic and second order sliding mode of a unified power flow controller. This controller offers advantages in terms of static and dynamic operation of the power system such as the control law is synthesized using three types of controllers: proportional integral, and sliding mode controller and Fuzzy logic second order sliding mode controller. Their respective performances are compared in terms of reference tracking, sensitivity to perturbations and robustness. We have to study the problem of controlling power in electric system by UPFC. The simulation results show the effectiveness of the proposed method especiallyin chattering-free behavior, response to sudden load variations and robustness. All the simulations for the above work have been carried out using MATLAB / Simulink. Various simulations have given very satisfactory results and we have successfully improved the real and reactive power flows on a transmission lineas well as to regulate voltage at the bus where it is connected, the studies and illustrate the effectiveness and capability of UPFC in improving power.Π Π½Π°ΡΡΠΎΡΡΠ΅ΠΉ ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Π° ΡΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½Π½Π°Ρ ΡΡ
Π΅ΠΌΠ° ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½Π°Ρ Π½Π° Π½Π΅ΡΠ΅ΡΠΊΠΎΠΉ Π»ΠΎΠ³ΠΈΠΊΠ΅ ΠΈ ΡΠ΅ΠΆΠΈΠΌΠ΅ ΡΠΊΠΎΠ»ΡΠΆΠ΅Π½ΠΈΡ Π²ΡΠΎΡΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΄ΠΊΠ° ΡΠ½ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅ΡΠ° ΠΏΠΎΡΠΎΠΊΠ° ΠΌΠΎΡΠ½ΠΎΡΡΠΈ. ΠΠ°Π½Π½ΡΠΉ ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅Ρ ΠΎΠ±Π»Π°Π΄Π°Π΅Ρ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π°ΠΌΠΈ Ρ ΡΠΎΡΠΊΠΈ Π·ΡΠ΅Π½ΠΈΡ ΡΡΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΡΠ½Π΅ΡΠ³ΠΎΡΠΈΡΡΠ΅ΠΌΡ, Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, Π·Π°ΠΊΠΎΠ½ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΡΠ΅ΡΡΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΡΠ΅Ρ
ΡΠΈΠΏΠΎΠ² ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅ΡΠΎΠ²: ΠΏΡΠΎΠΏΠΎΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎ-ΠΈΠ½ΡΠ΅Π³ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ, ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅ΡΠ° ΡΠΊΠΎΠ»ΡΠ·ΡΡΠ΅Π³ΠΎ ΡΠ΅ΠΆΠΈΠΌΠ° ΠΈ ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅ΡΠ° ΡΠΊΠΎΠ»ΡΠ·ΡΡΠ΅Π³ΠΎ ΡΠ΅ΠΆΠΈΠΌΠ° Π½Π΅ΡΠ΅ΡΠΊΠΎΠΉ Π»ΠΎΠ³ΠΈΠΊΠΈ Π²ΡΠΎΡΠΎΠ³ΠΎ ΠΏΠΎΡΡΠ΄ΠΊΠ°. ΠΡ
ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠ΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ ΡΡΠ°Π²Π½ΠΈΠ²Π°ΡΡΡΡ Ρ ΡΠΎΡΠΊΠΈ Π·ΡΠ΅Π½ΠΈΡ ΠΎΡΡΠ»Π΅ΠΆΠΈΠ²Π°Π½ΠΈΡ ΡΡΠ°Π»ΠΎΠ½ΠΎΠ², ΡΡΠ²ΡΡΠ²ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΊ Π²ΠΎΠ·ΠΌΡΡΠ΅Π½ΠΈΡΠΌ ΠΈ Π½Π°Π΄Π΅ΠΆΠ½ΠΎΡΡΠΈ. ΠΠ΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎ ΠΈΠ·ΡΡΠΈΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΌΠΎΡΠ½ΠΎΡΡΡΡ Π² ΡΠ½Π΅ΡΠ³ΠΎΡΠΈΡΡΠ΅ΠΌΠ΅ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠ½ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΡΠΎΠ»Π»Π΅ΡΠ° ΠΏΠΎΡΠΎΠΊΠ° ΠΌΠΎΡΠ½ΠΎΡΡΠΈ (UPFC). Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠ΅ΡΠΎΠ΄Π°, ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ Π² ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ ΠΎΡΡΡΡΡΡΠ²ΠΈΡ Π²ΠΈΠ±ΡΠ°ΡΠΈΠΈ, ΡΠ΅Π°ΠΊΡΠΈΠΈ Π½Π° Π²Π½Π΅Π·Π°ΠΏΠ½ΡΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π½Π°Π³ΡΡΠ·ΠΊΠΈ ΠΈ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΠΈ. ΠΡΠ΅ ΡΠ°ΡΡΠ΅ΡΡ Π΄Π»Ρ Π²ΡΡΠ΅ΡΠΊΠ°Π·Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ Π±ΡΠ»ΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Ρ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ MATLAB/Simulink. Π Π°Π·Π»ΠΈΡΠ½ΡΠ΅ ΡΠ°ΡΡΠ΅ΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π»ΠΈ Π²Π΅ΡΡΠΌΠ° ΡΠ΄ΠΎΠ²Π»Π΅ΡΠ²ΠΎΡΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ, ΠΈ ΠΌΡ ΡΡΠΏΠ΅ΡΠ½ΠΎ ΡΠ»ΡΡΡΠΈΠ»ΠΈ ΠΏΠΎΡΠΎΠΊΠΈ ΡΠ΅Π°Π»ΡΠ½ΠΎΠΉ ΠΈ ΡΠ΅Π°ΠΊΡΠΈΠ²Π½ΠΎΠΉ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ Π½Π° Π»ΠΈΠ½ΠΈΠΈ ΡΠ»Π΅ΠΊΡΡΠΎΠΏΠ΅ΡΠ΅Π΄Π°ΡΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠ΅Π³ΡΠ»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ Π½Π°ΠΏΡΡΠΆΠ΅Π½ΠΈΡ Π½Π° ΡΠΈΠ½Π΅, ΠΊ ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΎΠ½Π° ΠΏΠΎΠ΄ΠΊΠ»ΡΡΠ΅Π½Π°, ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΈΠ·ΡΡΠΈΡΡ ΠΈ ΠΏΡΠΎΠΈΠ»Π»ΡΡΡΡΠΈΡΠΎΠ²Π°ΡΡ ΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ ΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ UPFC Π΄Π»Ρ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΡ ΠΌΠΎΡΠ½ΠΎΡΡΠΈ
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
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Novel fuzzy logic controllers with self-tuning capability
Two controllers which extend the PD+I fuzzy logic controller to deal with the plant having time varying nonlinear dynamics are proposed. The adaptation ability of the first self tuning PD+I fuzzy logic controller (STPD+I_31) is achieved by adjusting the output scaling factor automatically thereby contributing to significant improvement in performance. Second controller (STPD+I_9) is the simplified version of STPD+I_31 which is designed under the imposed constraint that allows only minimum number of rules in the rule bases. The proposed controllers are compared with two classical nonlinear controllers: the pole placement self tuning PID controller and sliding mode controller. All the controllers are applied to the two-links revolute robot for the tracking control. The tracking performance of STPD+I_31 and STPD+I_9 are much better than the pole placement self tuning PID controller during high speed motions while the performance are comparable at low and medium speed. In addition, STPD+I_31 and STPD+I_9 outperform sliding mode controller using same method of comparison study
Design of Adaptive Sliding Mode Fuzzy Control for Robot Manipulator Based on Extended Kalman Filter
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A survey on fractional order control techniques for unmanned aerial and ground vehicles
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Discussion of the technology and research in fuel injectors common rail system
Common rail is one of the most important components in a diesel and gasoline direct injection system. It features a high-pressure (100 bar) fuel rail feeding solenoid valves, as opposed to a low-pressure fuel pump feeding unit injectors. Third-generation common rail diesels now feature piezoelectric injectors for increased precision, with fuel pressures up to 2,500 bar. The purpose of this review paper is to investigate the technology and research in fuel injectors common rail system. This review paper focuses on component of common rail injection system, pioneer of common rail injection, characteristics of common rail injection system, method to reduce smoke and NOx emission simultaneously and impact of common rail injection system. Based on our research, it can be concluded that common rail injection gives many benefit such as good for the engine performance, safe to use, and for to reduce the emission of the vehicle. Fuel injection common rail system is the modern technology that must be developed. Nowadays, our earth is polluting by vehicle output such as smoke. If the common rail system is developed, it can reduce the pollution and keep our atmosphere clean and safe
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