Amplitude Controlled Adaptive Feedback Resonance in a Single Degree-of-Freedom Mass-Spring Mechanical System

Malas, Anindya; Chatterjee, Satyajit

doi:10.1016/j.proeng.2016.05.070

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…where p 2 n = k m , 2n = c m , h = H m , c is the viscous damping coefficient, n is the attenuation coefficient, h is the amplitude of the excitation force on unit mass, p n is the natural circular frequency of the sensor [26]. Its solution consists of two parts:…”

Section: Principle Of Resonance Sensor Bearing Damage Sensingmentioning

confidence: 99%

Early Weak Fault Diagnosis of Rolling Bearings Based on Fiber Bragg Grating Sensing Monitoring

et al. 2021

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Aiming at the problems of weak dynamic response and difficulty in diagnosis of early damage of rolling bearings, a diagnosis method for early damage of rolling bearings is proposed. Taking radial rolling bearings as the main research object, the load symmetric structure of deep groove ball bearings is analyzed. Based on the mechanical second-order system theory, the sensor monitoring structure is constructed. The generalized resonance principle is used to identify weak signals, and the fiber Bragg grating is used for signal sensing. The signal is obtained through the fiber Bragg grating high-speed demodulator. When a continuous periodic generalized resonance wave appears in the amplitude–frequency analysis of the signal, and there is a high-frequency resonance frequency, it can be proved that the bearing is faulty. The diagnosis method can effectively avoid the interference of low-frequency signals, the frequency spectrum is pure and there is no electromagnetic interference. It fully shows that the fiber Bragg grating sensor is suitable for the monitoring and diagnosis of the early weak fault of the bearing.

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Section: Principle Of Resonance Sensor Bearing Damage Sensingmentioning

confidence: 99%

Early Weak Fault Diagnosis of Rolling Bearings Based on Fiber Bragg Grating Sensing Monitoring

et al. 2021

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“…However, Simulink is a block diagram environment embedded in the MATLAB that allows modeling and simulation of multi-domain dynamics systems. Simulink model of a coupled mass spring damper system is prepared with the mathematical model presented through Equations (21) and (22). PD controller is implemented to track the masses position and Figure 3 represents a schematic of the Simulink model.…”

Section: Modeling Of a Coupled Mass Spring Damper Systems Using Simelmentioning

confidence: 99%

“…The author in [21], presented control of coupled mass spring damper system using polynomial structures approach. Malas and co-worker [22] presented a novel control strategy for inducing sif-sustained oscillation of a single degree-of-freedom MDS mechanical systems. The researchers concluded in their work that the proposed control model is capable of generating stable self-excited oscillation at the natural frequency regardless of the value of the control gain.…”

Section: Introductionmentioning

confidence: 99%

Simulink and Simelectronics based Position Control of a Coupled Mass-Spring Damper Mechanical System

Oyetola¹,

Olaluwoye²

2018

IJECE

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This paper presents the use of Simelectronics Program for modeling and control of a two degrees-of freedom coupled mass-spring-damper mechanical system.The aims of this paper are to establish a mathematical model that represents the dynamic behaviour of a coupled mass-spring damper system and effectively control the mass position using both Simulink and Simelectronics.The mathematical model is derived based on the augmented Lagrange equation and to simulate the dynamic accurately a PD controller is implemented to compensate for the oscillation sustained by the system as a result of the complex conjugate pair poles near to the imaginary axis.The input force has been subjected to an obstacle to mimic actual challenges and to validate the mathematical model a Simulink and Simelectronics models were developed, consequently, the results of the models were compared. According to the result analysis, the controller tracked the position errors and stabilized the positions to zero within a settling time of 6.5sec and significantly reduced the overshoot by 99.5% and 99. 7% in Simulink and Simelectronics respectively. Furthermore, it is found that Simelectronics model proved to be capable having advantages of simplicity, less time-intense and requires no mathematical model over the Simulink approach.

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“…One of the advantages of adaptive control is that it adapts better to changing dynamics [23]. Changes or uncertainties in system parameters can be managed more effectively with adaptive control.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based position control: reinforcement learning approach in spring mass damper systems

Demircioğlu,

Bakır

2024

Phys. Scr.

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This work examines the use of deep Reinforcement Learning (RL) in mass-spring system position control, providing a fresh viewpoint that goes beyond conventional control techniques. Mass-spring systems are widely used in many sectors and are basic models in control theory. The novel aspect of this approach is the thorough examination of the impact of several optimizer algorithms on the RL methodology, which reveals the optimal control tactics. The research applies a Deep Deterministic Policy Gradient (DDPG) algorithm for continuous action spaces, where the actor and critic networks are important components in assessing the agent's performance. The RL agent is trained to follow a reference trajectory using the Simulink environment for system modeling. The study provides insights into the agent's learning approach and performance optimization by evaluating the training process using force-time graphs, reward graphs, and Episode Manager charts. Furthermore, the effect of different combinations of optimizers on the control performance of the agent is examined. The outcomes highlight the importance of optimizer selection in the learning process by revealing significant variations in training times. As a result, a better understanding of the relationship between various optimizers and control performance is provided by this study's novel application of reinforcement learning in mass-spring system control. The results raise the possibility of more potent methods for controlling complex systems and add to the expanding field of study at the interface of control theory and deep learning.

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Amplitude Controlled Adaptive Feedback Resonance in a Single Degree-of-Freedom Mass-Spring Mechanical System

Cited by 6 publications

References 8 publications

Early Weak Fault Diagnosis of Rolling Bearings Based on Fiber Bragg Grating Sensing Monitoring

Early Weak Fault Diagnosis of Rolling Bearings Based on Fiber Bragg Grating Sensing Monitoring

Simulink and Simelectronics based Position Control of a Coupled Mass-Spring Damper Mechanical System

Artificial intelligence-based position control: reinforcement learning approach in spring mass damper systems

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