A rate-dependent KP modeling and direct compensation control technique for hysteresis in piezo-nanopositioning stages

2021 ◽  
pp. 1045389X2110235
Author(s):  
Rui Xu ◽  
Dapeng Tian ◽  
Miaolei Zhou
Keyword(s):  
Neural Network ◽  
Density Function ◽  
Control Technique ◽  
Dynamic Density ◽  
Compensation Control ◽  
Rate Dependent ◽  
The Neural Network ◽  
Reference Signals ◽  
Direct Compensation ◽  
Frequency Behavior

This paper first presents a rate-dependent Krasnosel’skii-Pokrovskii (RKP) model to capture the hysteresis of piezo-nanopositioning stages. The dynamic density function of the RKP model is obtained via neural network with frequency behavior input signal. Under the persistently exciting condition, the convergence of the neural network with Krasnosel’skii-Pokrovskii (KP) operators is proved rigorously. In order to address the hysteresis issue, a direct compensation control (DCC) approach with the KP compensation operator is proposed, where its dynamic density function is same as that of the RKP model. Some experiments with different reference signals are conducted to verify the effectiveness of the proposed modeling and DCC method on piezo-nanopositioning stages.

scholarly journals An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

2022 ◽  
Author(s):  
Asad Ali Khan ◽  
Omar A Beg ◽  
Yufang Jin ◽  
Sara Ahmed
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Control Level ◽  
Operating Conditions ◽  
Training Data ◽  
Control Technique ◽  
Normal Operation ◽  
Secondary Control ◽  
False Data Injection ◽  
The Neural Network

An explainable intelligent framework for cyber anomaly mitigation of cyber-physical inverter-based systems is presented.<div><br></div><div>Smart inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort the normal operation therefore, an effective cyber anomaly mitigation technique using an artificial neural network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using adynamic recurrent neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection to the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. The neural network is trained offline and tested online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of microgrid in the presence of cyber anomalies as demonstrated by real-time simulations on a real-time digital simulator OPAL-RT.<br></div>

An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

2022 ◽  
Author(s):  
Asad Ali Khan
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Control Level ◽  
Operating Conditions ◽  
Training Data ◽  
Control Technique ◽  
Normal Operation ◽  
Secondary Control ◽  
False Data Injection ◽  
The Neural Network

An explainable intelligent framework for cyber anomaly mitigation of cyber-physical inverter-based systems is presented.<div><br></div><div>Smart inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort the normal operation therefore, an effective cyber anomaly mitigation technique using an artificial neural network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using adynamic recurrent neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection to the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. The neural network is trained offline and tested online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of microgrid in the presence of cyber anomalies as demonstrated by real-time simulations on a real-time digital simulator OPAL-RT.<br></div>

scholarly journals A Modified RBF Neuro-Sliding Mode Control Technique for a Grid Connected PMSG Based Variable Speed Wind Energy Conversion System

2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Rostand Marc Douanla ◽  
Godpromesse Kenné ◽  
François Béceau Pelap ◽  
Armel Simo Fotso
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Control Technique ◽  
Nonlinear Controller ◽  
Parameter Uncertainties ◽  
Power Extraction ◽  
Mode Control ◽  
The Neural Network

A modified control scheme based on the combination of online trained neural network and sliding mode techniques is proposed to enhance maximum power extraction for a grid connected permanent magnet synchronous generator (PMSG) wind turbine system. The proposed control method does not need the knowledge of the uncertainty bounds nor the exact model of the nonlinear system. Since the neural network is trained online, the time to estimate good weights can affect the dynamic performance of the process during the startup phase. Therefore an appropriate way to smoothly and explicitly accelerate the neural network rate of convergence during the startup phase is proposed. Furthermore, a flexible grid side voltage source converter control structure which can handle both grid connected and standalone modes based on conventional proportional integral (PI) control method is presented. Simulations are done in Matlab/Simulink environment to verify the effectiveness and assess the performance of the proposed controller. The results analysis shows the superiority of the proposed RBF neuro-sliding mode controller compared to a nonlinear controller based on sliding mode control method when the system undergoes parameter uncertainties.

scholarly journals AI based Vector Control Method for BLDC Motor with Multi Switch Three-Phase Topology

2021 ◽  
Vol 58 (1) ◽  
pp. 3132-3141
Author(s):  
Ch. Vinay Kumar, G. Madhusudhana Rao, A. Raghu Ram
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Constant Load ◽  
Control Technique ◽  
Duty Ratio ◽  
Bldc Motor ◽  
Speed Sensor ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Coupling System

Brushless direct current (BLDC) Motors are extensively used because of their characteristics. Such characteristics are high dynamic response and high-power density.  BLDCM control system is a nonlinear, multi-variable, strong-coupling system. In this paper it is proposed that a neural network controller is used for the five level switch of the BLDC motor to enhance the power factor and reduce the current distortions with respect to its rise time, startup torque. This method is also done in comparison with the PID controllers. The working principle of the BLDC is with the help of five-switch control scheme can be implemented here. The different values of load were used to consider the total operation of the BLDC motor is to be controlled. After the completion of the training and testing of the neural network, it might be maintain the constant load values and its variables. To calculate the duty ratio of the DC-DC converter, it will be adjusted to regulate the speed of the BLDC motor. However the DC link of the five switching inverter is used here for the boosting of the voltage.  The effectiveness of the proposed control technique can be realized with help of the speed sensor. Various tests have been conducted in the simulation the proposed technology is the robust technology and it is proven that very effective and suitable control technique. 

An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

2022 ◽  
Author(s):  
Asad Ali Khan
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Control Level ◽  
Operating Conditions ◽  
Training Data ◽  
Control Technique ◽  
Normal Operation ◽  
Secondary Control ◽  
False Data Injection ◽  
The Neural Network

An explainable intelligent framework for cyber anomaly mitigation of cyber-physical inverter-based systems is presented.<div><br></div><div>Smart inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort the normal operation therefore, an effective cyber anomaly mitigation technique using an artificial neural network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using adynamic recurrent neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection to the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. The neural network is trained offline and tested online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of microgrid in the presence of cyber anomalies as demonstrated by real-time simulations on a real-time digital simulator OPAL-RT.<br></div>

Distinct relaxation timescales of neurites revealed by rate-dependent indentation, relaxation and micro-rheology tests

Soft Matter ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 166-174 ◽  
Author(s):  
Ze Gong ◽  
Chao Fang ◽  
Ran You ◽  
Xueying Shao ◽  
Xi Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Dynamic Response ◽  
Dynamic Mechanical Properties ◽  
Axon Outgrowth ◽  
Dynamic Mechanical ◽  
Rate Dependent ◽  
The Neural Network

Although the dynamic response of neurites is believed to play crucial roles in processes like axon outgrowth and formation of the neural network, the dynamic mechanical properties of such protrusions remain poorly understood.

Fault Prediction Method Research of the Power Plant Fan

2012 ◽  
Vol 580 ◽  
pp. 99-104 ◽  
Author(s):  
Jian Ping Sun ◽  
Ming Gao ◽  
Ya Lun Li
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Density Function ◽  
Fault Prediction ◽  
Estimation Methods ◽  
Prediction Algorithm ◽  
Local Optimum ◽  
Function Estimation ◽  
The Neural Network ◽  
Density Function Estimation

To solve the problem of the power plant fan fault prediction, proposed that combining with neural network method and nonparametric density function estimation methods based on parzen window the estimation to achieve fault detection. To improve the prediction performance of neural network, used PSO method, which can realize weights optimization of the neural network prediction, avoid falling into local optimum. Using sliding time window achieve the multi-step prediction of the neural network, and ensure the prediction accuracy. Then, fault is predicted by prediction residuals through density function estimation and hypothesis test. Finally, by using the vibration fault prediction of the air feeder of a power plant in Shanxi as research object to test this method, the simulation result illustrate this fault prediction algorithm can predict the fault of fan timely and effectively.

scholarly journals A New RBF Neural Network-Based Fault-Tolerant Active Control for Fractional Time-Delayed Systems

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1501
Author(s):  
Bo Wang ◽  
Hadi Jahanshahi ◽  
Christos Volos ◽  
Stelios Bekiros ◽  
Muhammad Altaf Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Active Control ◽  
Fault Tolerant ◽  
Rbf Neural Network ◽  
Control Technique ◽  
Delayed Systems ◽  
Systems Model ◽  
The Neural Network ◽  
Control Scheme ◽  
Uncertain Time

Recently, intelligent control techniques have received considerable attention. In most studies, the systems’ model is assumed to be without any delay, and the effects of faults and failure in actuators are ignored. However, in real practice, sensor malfunctioning, mounting limitation, and defects in actuators bring about faults, failure, delay, and disturbances. Consequently, applying controllers that do not consider these problems could significantly deteriorate controllers’ performance. In order to address this issue, in the current paper, we propose a new neural network-based fault-tolerant active control for fractional time-delayed systems. The neural network estimator is integrated with active control to compensate for all uncertainties and disturbances. The suggested method’s stability is achieved based on the concept of active control and the Lyapunov stability theorem. Then, a fractional-order memristor system is investigated, and some characteristics of this chaotic system are studied. Lastly, by applying the proposed control scheme, synchronization results of the fractional time-delayed memristor system in the presence of faults and uncertainties are studied. The simulation results suggest the effectiveness of the proposed control technique for uncertain time-delayed nonlinear systems.

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