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Chaotic Behavior of DC-DC Boost Converter by using Voltage Mode control 4

Chaotic Behavior of DC-DC Boost Converter by using Voltage M...

引用

4th International Conference for Emerging Technology, INCET 2023

作者： Ganesh, Sandhu Valluru, Sudarshan K. Control of Dynamical Systems and Computation Laboratory Department of Electrical Engineering Delhi110042 India

ISBN: (纸本)9798350335750

This paper examines the Boost converter's chaotic behavior. A voltage mode controlled technique is being utilized to analyze the discrete-time mapping to determine the properties of the bifurcation that operate in a closed loop. By varying some parameters while holding others constant, the reference voltage and input voltage are used to examine the bifurcation diagram. It has been noted that the system becomes unstable and splits into two time-periodic systems, four time - periodic systems and then chaos takes over the system. These occurrences have been shown in the bifurcation diagram, therefore the converter's safe operating range can be determined and verified by using the time-domain waveforms and phase plots. © 2023 IEEE.

关键词： Boost converter

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Design & Validation of ANN based Reinforcement Learning control Algorithm for Coupled Tank System 8

Design & Validation of ANN based Reinforcement Learning Cont...

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Rastogi, Digant Jain, Manika Rayguru, Madan M. Valluru, Sudarshan K. Delhi Technological University Control of Dynamical Systems and Computation Lab Dept. of Electrical Engineering Delhi India

ISBN: (纸本)9798350334012

This paper presents a framework to apply Reinforcement Learning control algorithm on benchmark nonlinear dynamical systems. This work focuses on a novel Artificial Neural Network (ANN) based dynamic programming approach using Value Iteration to obtain optimal control for continuous-time nonlinear system. In particular, Coupled Tank System has been chosen to represent benchmark nonlinear dynamical system. The proposed Artificial Neural Network-Reinforcement Learning (ANN-RL) algorithm, Naive Reinforcement Learning (Naive-RL) algorithm and traditional PID control schemes are investigated on coupled tank system. The ANN-RL algorithm performs better than the Naive-RL and PID controllers in terms of steady state error, stability, oscillations and overshoot. © 2023 IEEE.

关键词： Reinforcement learning

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Comparative Study of Parametric Disturbances Effect on Cart-Inverted Pendulum System Stabilization 1

引用

International Conference on Modern Electronics Devices and Communication systems, MEDCOM 2021

作者： Verma, Neelam Valluru, Sudarshan K. Department of Electrical Engineering Center for Control of Dynamical Systems and Computation Delhi Technological University Delhi India

ISBN: (数字)9789811963834

ISBN: (纸本)9789811963827

The main motivation behind the present work was to validate the impact of pendulum mass, cart mass, and length of pendulum on stabilization and swing-up of cart-inverted pendulum. Inverted pendulum system is a classic benchmark example of underactuated electro-mechanical systems, where the degree of freedom is less than the number of inputs available. PID controller was designed for a set of parameter values of the system. MATLAB simulation was carried out for different values of pendulum mass (0.2–0.25 kg), pendulum length (0.3–0.34 m), and cart mass (2.3–2.45 kg). The performance of considered cart- inverted pendulum system is demonstrated with simulation results for a different set of results, and a comparative analysis was made to comment on their effects on swing-up and stabilization. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stabilization

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Intelligent control of Mobile Robots with ANN Assisted Improved Q-learning: IQ-CRL Algorithm

Intelligent Control of Mobile Robots with ANN Assisted Impro...

引用

2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Rastogi, Digant Jain, Manika Rayguru, Madan M. Valluru, Sudarshan K. Delhi Technological University Control of Dynamical Systems and Computation Laboratory Dept. of Electrical Engineering Delhi India

ISBN: (纸本)9798350331790

This paper presents a novel algorithm, IQ-CRL (Improved Q-learning using Classification and Regression with ANN), which is a control architecture that uses the existing Q-learning algorithm and integrates it with Artificial Neural Networks (ANNs). The proposed algorithm addresses the limitations of traditional Q-learning by incorporating an adaptive mechanism that leverages ANN to find an optimal control policy that is more accurate and at lower computational expense. IQ-CRL is designed to be used efficiently with dynamical systems such as mobile robots. The objective of this study is to evaluate the performance of IQ-CRL in comparison to classical Q-learning and PID controller. The results show that the IQ-CRL algorithm outperforms both the classical Q-learning and PID controller in terms of learning efficiency, control accuracy and computing complexity. © 2023 IEEE.

关键词： Mobile robots

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Adaptive-Optimal control for Reconfigurable Robots

Adaptive-Optimal Control for Reconfigurable Robots

引用

2023 IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2023

作者： Maheshwari, Adya Rautela, Aaditya Rayguru, Madan Mohan Valluru, Sudarshan K. Delhi Technological University Department of Electrical Engineering New Delhi India Delhi Technological University Control of Dynamical Systems and Computation Laboratory Department of Electrical Engineering New Delhi India

ISBN: (纸本)9781665474924

Reconfigurable robots refer to the robotic systems which have the ability to transform their morphological structure to adapt to changes in their surroundings. A specialized control system based upon the dynamic characteristics is required to be modelled so that a vast range of operations can be performed by the reconfigurable robots. Reinforcement learning based technique is therefore considered as a suitable mechanism for carrying out adaptive optimal control for the robotic systems. In this paper, an attempt has been made to model the adaptive optimal control mechanism for a single-link manipulator: a reconfigurable robotic system. The system has been modelled for a dynamic environment. The loss and reward plots are obtained from the simulation of the robotic system by deploying reinforcement learning based techniques with the given controlled input © 2023 IEEE.

关键词： Reinforcement learning

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Bifurcation Analysis of Current-mode controlled CUK converter 2

Bifurcation Analysis of Current-mode controlled CUK converte...

引用

2nd Asian Conference on Innovation in Technology, ASIANCON 2022

作者： Kumar, Avinash Valluru, Sudarshan K. Delhi Technological University Control of Dynamical Systems and Computation Lab Department of Electrical Engineering Delhi India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

This paper analyses the bifurcation behaviour of the CUK converter operating under continuous conduction mode. Discrete-time mapping is used to analyse the bifurcation parameters operating under a closed loop with a current-controlled scheme. to study the bifurcation diagram parameters like reference current and the Input voltage are varied by keeping others constant. It has been observed that the system loses its stability and bifurcates from 1 time-periodic to 2-time-periodic. The system then enters into chaos. These phenomena have been observed in the bifurcation diagram and hence the maximum range in which the converter can operate safely is known and is validated by time-domain waveforms and phase plots. © 2022 IEEE.

关键词： Bifurcation (mathematics)

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Learned Regions of Attraction for Safe Motion Primitive Transitions

Learned Regions of Attraction for Safe Motion Primitive Tran...

引用

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Wyatt Ubellacker Aaron D. Ames Departments of Control and Dynamical Systems Mechanical and Civil Engineering California Institute of Technology Pasadena CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Estimating regions of attraction (ROAs) of dynamical systems is critical for understanding the operational bounds within which a system will converge to a desired state. In this paper, we introduce a neural network-based approach to approximating ROAs that leverages labeled data generated by offline sampling and simulation of initial conditions, with labels determined by flow membership in an "explicit region of attraction." This framework is designed to estimate ROAs with a level of precision suitable for integration into a motion primitive transition framework as conditions to switch between candidate primitive behaviors. To account for gaps between the simulated environment and the real world, online learning is employed; this refines the offline-learned model of the ROA based on observed discrepancies between predicted and actual system behaviors. We validate this methodology on a quadrupedal robot, demonstrating that our ROA estimates can effectively model regions of attraction for a high-dimensional system. We show this for multiple primitive behaviors and in environments different from the training data. The outcomes highlight the usefulness of our method in estimating regions of attraction and informing transition conditions between primitive behaviors.

关键词： Training Neural networks Training data Switches Predictive models Robot sensing systems Data models Quadrupedal robots dynamical systems Intelligent robots

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Maneuvering control of Autonomous Underwater Vehicle (AUV) by Simulated Annealing and Moth Flame Optimization Tuned controllers

Maneuvering Control of Autonomous Underwater Vehicle (AUV) b...

引用

2022 International Conference on Electronic systems and Intelligent Computing, ICESIC 2022

作者： Valluru, Sudarshan K. Thareia, Hitesh Center for Control of Dynamical Systems and Computation Dept. of Electrical Engineering Delhi Technological University Delhi110042 India

ISBN: (纸本)9781665483858

This paper emphasizes maneuvering control of Autonomous Underwater Vehicle (AUV) with the help of Linear-Proportional Integral Derivative (L-PID) and Fractional Order PID (FOPID) controllers. The values of the gain parameters of L-PID (Kp, Ki, Kd) and FOPID (Kp, Ki, Kd,λ, μ) are tuned by Simulated Annealing (SA) and Moth Flame optimization (MFO) techniques. The performance of both the controllers is compared and the better algorithm to tune the parameters of the controllers is determined. A step function is applied to the system's input to test the AUV, and the corresponding response to the input function is examined. The transient parameters of the AUV, such as rise time, settling time, overshoot, and performance index IT AE were calculated and compared. The investigatory results show that both the algorithms used to tune the FO PID controller accurately steer the AUV. Also, the MFO tuned FOPID controller is more efficient than the SA tuned FOPID controller. © 2022 IEEE.

关键词： Simulated annealing

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Reinforcement Learning in Simultaneous Localization and Mapping 5th

Reinforcement Learning in Simultaneous Localization and Mapp...

引用

5th International Conference on Recent Developments in control, Automation and Power engineering, RDCAPE 2023

作者： Kumar, Abhik Valluru, Sudarshan K. Rayguru, M.M. Control of Dynamical Systems Computation Laboratory Department of Electrical Engineering Delhi Technological University Delhi India

ISBN: (纸本)9789819600465

The primary objective of this research work is to investigate model-free path planning for reconfigurable robots using value and policy iterations. The focus is on developing and evaluating an autonomous algorithm for robot path planning. Initially, the A-Star algorithm was modified to incorporate model-based learning. Subsequently, model-free reinforced learning was incorporated through value iteration and policy iteration. The experimental validations of modified A-Star algorithm is conducted in python virtual environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Reinforcement learning

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Robust Locomotion on Legged Robots through Planning on Motion Primitive Graphs

Robust Locomotion on Legged Robots through Planning on Motio...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Wyatt Ubellacker Aaron D. Ames Departments of Control and Dynamical Systems Mechanical and Civil Engineering California Institute of Technology Pasaden CA USA

The functional demands of robotic systems often require completing various tasks or behaviors under the effect of disturbances or uncertain environments. Of increasing interest is the autonomy for dynamic robots, such as multirotors, motor vehicles, and legged platforms. Here, disturbances and environmental conditions can have significant impact on the successful performance of the individual dynamic behaviors, referred to as “motion primitives”. Despite this, robustness can be achieved by switching to and transitioning through suitable motion primitives. This paper contributes such a method by presenting an abstraction of the motion primitive dynamics and a corresponding”motion primitive transfer function”. From this, a mixed discrete and continuous “motion primitive graph” is constructed, and an algorithm capable of online search of this graph is detailed. The result is a framework capable of realizing holistic robustness on dynamic systems. This is experimentally demonstrated for a set of motion primitives on a quadrupedal robot, subject to various environmental and intentional disturbances.

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