2D and 3D Visualizations of the Mass-Damper-Spring Model Dynamics Controlled by a Servo-Controlled Linear Actuator

Kandil, Ali; Hamed, Y. S.; Alsharif, Abdullah M.; Awrejcewicz, Jan

doi:10.1109/access.2021.3126868

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…The mass-spring system is a subject that has been studied in detail by modern control theories. In particular, state space methods provide the opportunity to analyze system dynamics in more detail [12]. These methods make it possible to create more complex control strategies by expressing system states and inputs as vectors [13].…”

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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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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“…Liu et al [18,19] proposed different kinds of timedelayed proportional-derivative active controllers in order to mitigate the oscillations in a cantilever beam once with a lumped mass and another with an axial load. Kandil et al [14] proposed the control of a mass-damper-spring model with the aid of servo-controlled linear actuator and linear variable differential transformer as a sensor where the control signal was proportional to the mass position. Zhou et al [34] studied the effect of moving the base of a magnetically coupled oscillator on the dynamical behavior of the saturated equilibrium response.…”

Section: Introductionmentioning

confidence: 99%

Integral Resonant Controller for Suppressing Car’s Oscillations and Eliminating its Inherent Jump Phenomenon

Kandil,

Hamed

2023

Eur. J. Pure Appl. Math.

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This work deals with modeling and controlling the oscillations of a horizontally-supported car under the effect of a nonlinear spring, a damper, and a harmonic excitation external force. The proposed controller is the integral resonant controller (IRC) which is a first order oscillator coupled the car via a linear variable differential transformer (LVDT) and a servo-controlled linear actuator(SCLA). The multiple scales perturbation method is used to obtain an approximate solution and stability analyses are carried out. Based on the stability analysis, the optimum values of the controller parameters are recommended in this work for a better control operation. Several response curves are plotted for clarifying the concept of the proposed integral resonant control algorithm.A numerical simulation of the car’s motion is achieved by numerically integrating the model equations with the 4th order Rung-Kutta algorithm. The applied controller can treat the severe jumps in the oscillation amplitude where it is suppressed at different conditions of forward and backward sweeping of the amplitude and frequency of the external excitation force.

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“…Cong et al [29] analyzed theoretically the nonlinear dynamics of a cantilever beam (cable-stayed) subjected to simultaneous resonance cases that would be very useful in many civil structures. Kandil et al [30] proposed a wide study on the primary resonance of a mass-damper-spring model controlled by an active controller where 2D and 3D plots were given to confirm the control process. Kwarta and Allen [31] built a new technique that used near-resonant vibrations in order to estimate the backbones of the normal modes of the studied dynamical system.…”

Section: Introductionmentioning

confidence: 99%

1/3 Order Subharmonic Resonance Control of a Mass-Damper-Spring Model via Cubic-Position Negative-Velocity Feedback

et al. 2022

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A cubic-position negative-velocity (CPNV) feedback controller is proposed in this research in order to suppress the nontrivial oscillations of the order subharmonic resonance of a mass-damper-spring model. Based on the Krylov–Bogoliubov (KB) averaging method, the model’s equation of motion is approximately solved and tested for stability. The nontrivial solutions region is plotted to determine where these solutions occur and try to quench them. The controller parameters can play crucial roles in eliminating such regions, keeping only the trivial solutions, and improving the transient response of the car’s oscillations. Different response curves and relations are included in this study to provide the reader a wide overview of the control process.

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2D and 3D Visualizations of the Mass-Damper-Spring Model Dynamics Controlled by a Servo-Controlled Linear Actuator

Cited by 8 publications

References 36 publications

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

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

Integral Resonant Controller for Suppressing Car’s Oscillations and Eliminating its Inherent Jump Phenomenon

1/3 Order Subharmonic Resonance Control of a Mass-Damper-Spring Model via Cubic-Position Negative-Velocity Feedback

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