A New Control Method for Input–Output Harmonic Elimination of the PWM Boost-Type Rectifier Under Extreme Unbalanced Operating Conditions

In this work, a new control method for uncertain processes is developed based on two-degree-of-freedom control structure. The setpoint tracking controller designed by input/output linearization technique is used to regulate the disturbance-free output and the disturbance rejection controller designed is designed by high-gain technique. The advantage of two-degree-of-freedom control structure is that setpoint tracking and load disturbance rejection controllers can be designed separately. Open-loop observer is applied to provide disturbance-free response for setpoint tracking controller. The process/disturbance-free model mismatches are fed to the disturbance rejection controller for reducing effect of disturbance. To evaluate the control performance, the proposed control method is applied through the example of a continuous stirred tank reactor with unmeasured input disturbances and random noise kinetic parametric uncertainties. The simulation results show that both types of disturbances can be effectively compensated by the proposed control method.

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Chaotic motion control of a vibro-impact system based on data-driven control method

Journal of Vibration and Control ◽

10.1177/10775463211043320 ◽

2021 ◽

pp. 107754632110433

Author(s):

Xiao-juan Wei ◽

Ning-zhou Li ◽

Wang-cai Ding

Keyword(s):

Motion Control ◽

Data Model ◽

Chaotic Motion ◽

Control Method ◽

Data Driven ◽

Damping Coefficient ◽

Input Output ◽

Output Data ◽

Controlled System ◽

Impact System

For the chaotic motion control of a vibro-impact system with clearance, the parameter feedback chaos control strategy based on the data-driven control method is presented in this article. The pseudo-partial-derivative is estimated on-line by using the input/output data of the controlled system so that the compact form dynamic linearization (CFDL) data model of the controlled system can be established. And then, the chaos controller is designed based on the CFDL data model of the controlled system. And the distance between two adjacent points on the Poincaré section is used as the judgment basis to guide the controller to output a small perturbation to adjust the damping coefficient of the controlled system, so the chaotic motion can be controlled to a periodic motion by dynamically and slightly adjusting the damping coefficient of the controlled system. In this method, the design of the controller is independent of the order of the controlled system and the structure of the mathematical model. Only the input/output data of the controlled system can be used to complete the design of the controller. In the simulation experiment, the effectiveness and feasibility of the proposed control method in this article are verified by simulation results.

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Assessment of vehicle brake control functional stability

Journal of Mechanical Engineering and Transport ◽

10.31649/2413-4503-2020-12-2-33-44 ◽

2021 ◽

Vol 12 (2) ◽

pp. 33-44

Author(s):

Volodymyr Volkov ◽

◽

Igor Gritsuk ◽

Tetiana Volkova ◽

Volodymyr Kuzhel ◽

...

Keyword(s):

Energy Consumption ◽

Control Method ◽

Operating Conditions ◽

National Standards ◽

Passenger Vehicles ◽

The Difference ◽

Braking Control ◽

The Stability ◽

Friction Surfaces

The article is devoted to the study of the influence of the brake control elements of passenger vehicles on the stability of their braking properties. The analysis of the influence of uneven braking forces on the wheels of one axle of vehicles on the deviation of the distribution of braking forces between the axles from its calculated value is carried out. When assessing the error in regulating the distribution of braking forces between the axles of vehicles, three components were taken into account: the theoretical error due to the imperfection of the selected control method (the difference between the actual calculated control characteristic from the ideal), the error created due to the instability of the ratio of the braking forces on the front and rear wheels, an additional error caused by the unevenness of the braking forces on the wheels of individual axles, since the fulfillment of the most stringent requirements of international and national standards for the efficiency of braking of vehicles and is inextricably linked with the need to increase the energy consumption of brake mechanisms. The energy consumption of braking mechanisms is understood as the ability of the latter to dissipate the greatest amount of energy of the braking machine without reducing the braking efficiency indicators to the minimum permissible level. Excessive heating of the braking mechanisms leads to a decrease in the friction coefficient μ of the friction surfaces and increased wear of the friction linings, and the brakes are the most unstable element of the braking control, which ensures the absorption and dissipation of the vehicle's energy during braking. The instability of the braking torques on the front and rear wheels, caused by a change in the coefficients of friction of friction pairs, leads not only to a change in the distribution of braking forces between the axles and individual wheels, but also to a decrease in the braking efficiency of vehicles under operating conditions. A method is proposed that makes it possible to assess the quality of regulation of the distribution of braking forces between the axles of a car, taking into account the instability of the braking forces on the wheels.

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Application of Blow-off Wind Tunnel Control Based on Genetic Algorithm Optimized BP-Neural Network PID Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.310.557 ◽

2013 ◽

Vol 310 ◽

pp. 557-559 ◽

Cited By ~ 2

Author(s):

Li Ji ◽

Xiao Fei Lian

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Wind Tunnel ◽

Bp Neural Network ◽

Control Method ◽

Network Control ◽

Operating Conditions ◽

Algorithm Optimization ◽

Large Delay ◽

Neural Network Pid

For a blow-off tunnel running, there is the large delay and lag issues. We build a mathematical model of the wind tunnel Mach number control by the test modeling method, then analyse the pros and cons of various control methods based on BP neural network control algorithm. Put forward genetic algorithm optimization neural network adaptive control method to solve the large inertia of the wind tunnel system, and large delay. A large number of simulation studies, run a variety of operating conditions for the wind tunnel simulation proved that the improved adaptive neural network PID control method is reasonable and effective.

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A Deep Neural Network Model for Learning Runtime Frequency Response Function Using Sensor Measurements

Volume 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability ◽

10.1115/msec2021-64065 ◽

2021 ◽

Author(s):

Yongzhi Qu ◽

Gregory W. Vogl ◽

Zechao Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Fourier Transform ◽

Time Domain ◽

Frequency Response Function ◽

Frequency Component ◽

Operating Conditions ◽

Input Output ◽

The Fourier Transform ◽

The Time Domain

Abstract The frequency response function (FRF), defined as the ratio between the Fourier transform of the time-domain output and the Fourier transform of the time-domain input, is a common tool to analyze the relationships between inputs and outputs of a mechanical system. Learning the FRF for mechanical systems can facilitate system identification, condition-based health monitoring, and improve performance metrics, by providing an input-output model that describes the system dynamics. Existing FRF identification assumes there is a one-to-one mapping between each input frequency component and output frequency component. However, during dynamic operations, the FRF can present complex dependencies with frequency cross-correlations due to modulation effects, nonlinearities, and mechanical noise. Furthermore, existing FRFs assume linearity between input-output spectrums with varying mechanical loads, while in practice FRFs can depend on the operating conditions and show high nonlinearities. Outputs of existing neural networks are typically low-dimensional labels rather than real-time high-dimensional measurements. This paper proposes a vector regression method based on deep neural networks for the learning of runtime FRFs from measurement data under different operating conditions. More specifically, a neural network based on an encoder-decoder with a symmetric compression structure is proposed. The deep encoder-decoder network features simultaneous learning of the regression relationship between input and output embeddings, as well as a discriminative model for output spectrum classification under different operating conditions. The learning model is validated using experimental data from a high-pressure hydraulic test rig. The results show that the proposed model can learn the FRF between sensor measurements under different operating conditions with high accuracy and denoising capability. The learned FRF model provides an estimation for sensor measurements when a physical sensor is not feasible and can be used for operating condition recognition.

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Active Fault-Tolerant Control Based on Multiple Input Multiple Output-Model Free Adaptive Control for Four Wheel Independently Driven Electric Vehicle Drive System

Applied Sciences ◽

10.3390/app9020276 ◽

2019 ◽

Vol 9 (2) ◽

pp. 276 ◽

Cited By ~ 2

Author(s):

Yugong Luo ◽

Yun Hu ◽

Fachao Jiang ◽

Rui Chen ◽

Yongsheng Wang

Keyword(s):

Control System ◽

Active Fault ◽

Control Method ◽

Fault Tolerant ◽

Fault Tolerant Control ◽

Drive System ◽

Input Output ◽

Vehicle Model ◽

Model Free ◽

Active Fault Tolerant Control

To solve the problems with the existing active fault-tolerant control system, which does not consider the cooperative control of the drive system and steering system or accurately relies on the vehicle model when one or more motors fail, a multi-input and multi-output model-free adaptive active fault-tolerant control method for four-wheel independently driven electric vehicles is proposed. The method, which only uses the input/output data of the vehicle in the control system design, is based on a new dynamic linearization technique with a pseudo-partial derivative, aimed at solving the complex and nonlinear issues of the vehicle model. The desired control objectives can be achieved by the coordinated adaptive fault-tolerant control of the drive and steering systems under different failure conditions of the drive system. The error convergence and input-output boundedness of the control system are proven by means of stability analysis. Finally, simulations and further experiments are carried out to validate the effectiveness and real-time response of the fault-tolerant system in different driving scenarios. The results demonstrate that our proposed approach can maintain the longitudinal speed error (within 3%) and lateral stability, thereby improving the safety of the vehicles.

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