Wireless networked learning control system based on Kalman filter and biogeography-based optimization method

Ma, Haiping; Fei, Minrui; Yang, Zhile; Wang, Haikuan

doi:10.1177/0142331213497619

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…There will be some inevitable problems in network data transmission, for instance, data packet dropout and a fault package, as shown in Figure 12. Problems like these will greatly influence the control effect of the system (Ma H, et.al., 2013). Data packet delay will lead to bad real-time tracking results, data packet dropout may have a direct result on target loss and even trace failure.…”

Section: Error Estimation Based On Kalman Filtermentioning

confidence: 99%

Optimal PID controller design with Kalman filter for Qball-X4 quad-rotor unmanned aerial vehicle

Pan

Liu

Xue

2016

Transactions of the Institute of Measurement and Control

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In this paper, we used a Qball-X4 quad-rotor unmanned aerial vehicle (UAV) which was developed by the Quanser Company as the experimental platform. First, a fundamental mathematical model of the Qball-X4 quad-rotor UAV was built and a simulation model was set up based on the proposed mathematical model; then, a double closed-loop optimal proportional–integral–derivative (PID) controller based on integral of time multiplied by absolute error (ITAE) indices was designed according to the model structure. In consideration of the possible system error and data delay, we designed a corresponding Kalman filter, which can estimate the target trajectory and be put before the proposed PID controller to ensure their validity. Finally, simulation results of the system with presented PID controller and Kalman filter were shown to verify their effectiveness.

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Section: Error Estimation Based On Kalman Filtermentioning

confidence: 99%

Optimal PID controller design with Kalman filter for Qball-X4 quad-rotor unmanned aerial vehicle

Pan

Liu

Xue

2016

Transactions of the Institute of Measurement and Control

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“…An important characteristic in these application areas is that technology improvement based on a wireless industry network is required. Figure 15 shows a schematic representation of the system, where electrical energy generation using a steam turbine involves three energy conversions: extracting thermal energy from the fuel in a petroleum gas tank and using it to raise steam by a steam boiler; converting the thermal energy of the steam into kinetic energy in a steam turbine, and; using a rotary generator to convert the turbine's mechanical energy into electrical energy [31]. From Figure 15, it is known that the measured values of this system are transmitted by different wired/wireless sub-networks to the gateway, which changes the styles of data stacks and continues to transmit to the controller in a backbone PROFIBUS-DP network.…”

Section: Applicationmentioning

confidence: 99%

Design of Hybrid Wired/Wireless Fieldbus Network for Turbine Power Generation System

Fei

Wang

2016

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Hybrid fieldbus network integrating wireless networks with existing wired fieldbuses has become new a research direction in industrial automation systems. In comparison to wired fieldbuses, the hybrid wired/wireless fieldbus network has a different system architecture, data transmission mechanism, communication protocol, etc. This leads to different challenges that need to be addressed. This paper proposes a hybrid wired/wireless fieldbus network which consists of a wireless industrial control network (WICN), a wired PROFIBUS-DP (Process Field Bus-Decentralized Periphery) fieldbus network, and a wired MODBUS/TCP (Mod Bus/Transmission Control Protocol) fieldbus network. They are connected by a new gateway which uses a shared data model to solve data exchange in different network protocols. In this paper, we describe the architecture of the proposed hybrid wired/wireless fieldbus network and data transmission mechanisms in detail, and then evaluate the performance of hybrid fieldbus network via a set of experiments. The experiment results confirm that the proposed hybrid wired/wireless fieldbus network can satisfy the performance requirement of industrial network control systems. Furthermore, in order to further investigate feasibility of the proposed hybrid wired/wireless fieldbus network, it is deployed at a steam turbine power generation system, and the performance figures obtained further verify its feasibility and effectiveness.

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“…The integration of wind energy into existing power system presents technical challenges and that requires consideration of voltage regulation, stability and power quality (PQ) problems (Merabet et al, 2015; Hossain and Ali, 2015; Singh et al, 2011). The PQ problems have been broadly classified into voltage and current related PQ problems (Ma et al, 2013). The current related PQ problems are total harmonic distortion (THD), unbalanced load, reactive power demand, and lagging power factor (Jadhav and Roy, 2015).…”

Section: Introductionmentioning

confidence: 99%

Power quality improvement of wind energy conversion system with unified power quality controller: A hybrid control model

Nagaraju¹,

Shankar²

2020

Transactions of the Institute of Measurement and Control

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The real problems in diminution of power quality (PQ) occur due to the rapid growth of nonlinear load are leading to a sudden decrease of source voltage for a few seconds. All these problems can be compensated by unified power quality controller (UPQC). The proposed research is based on designing a wind energy conversion system (WECS) fed to the dc-link capacitor of UPQC so as to maintain proper voltage across it and operate the UPQC for PQ improvement. The proposed research utilizes two techniques for enhancing the performance of UPQC known as integrated ant lion optimizer (IALO)-adaptive neuro fuzzy inference system (ANFIS), called IALO-ANFIS. Here, induction motor is considered as non-linear load. ALO searching behavior is enhanced by crossover and mutation. Initially, the objective function parameters are defined, that is, voltage, real, grid parameters, load parameters, real and reactive power and current. Based on these parameters, the control pulse is produced for series and shunt active power filter (APF). IALO is used to identify the optimal solutions and creates the training dataset. In light of the accomplished dataset, ANFIS predicts the best control signals of UPQC. During load variation conditions, the proposed strategy minimized the power loss and voltage instability issue individually. Subsequently, the power quality of the system is enhanced. In order to evaluate the effectiveness of the proposed method, three different cases are considered. The performance of the proposed technique is validated through MATLAB/Simulink and compared with existing techniques such as genetic algorithm and ALO.

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Wireless networked learning control system based on Kalman filter and biogeography-based optimization method

Cited by 7 publications

References 36 publications

Optimal PID controller design with Kalman filter for Qball-X4 quad-rotor unmanned aerial vehicle

Optimal PID controller design with Kalman filter for Qball-X4 quad-rotor unmanned aerial vehicle

Design of Hybrid Wired/Wireless Fieldbus Network for Turbine Power Generation System

Power quality improvement of wind energy conversion system with unified power quality controller: A hybrid control model

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