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Joshua Sunder David Reddipogu

Bio: Joshua Sunder David Reddipogu is an academic researcher from VIT University. The author has contributed to research in topics: Iterative learning control & Tracking error. The author has an hindex of 1, co-authored 2 publications receiving 1 citations.

Papers
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Journal ArticleDOI
TL;DR: Experimental results substantiate that the EIMC scheme can effectively counteract the inherent time delay in the model and offer precise tracking, even in the presence of exogenous disturbance.
Abstract: This paper presents an enhanced internal model control (EIMC) scheme for a time-delayed second order unstable process, which is subjected to exogenous disturbance and model variations. Even though the conventional internal model control (IMC) can provide an asymptotic tracking response with desired stability margins, the major limitation of conventional IMC is that it cannot be applied for an unstable system because a small exogenous disturbance can trigger the control signal to grow unbounded. Hence, modifying the conventional IMC structure to guarantee the internal stability, we present an EIMC scheme which can offer better trade-off between setpoint tracking and disturbance rejection characteristics. To improve the load disturbance rejection characteristics and attenuate the effect of sensor noise, we solve the selection of controller gains as an H∞ optimization problem. One of the key aspects of the EIMC scheme is that the robustness of the closed loop system can be tuned via a single tuning parameter. The performance of the EIMC scheme is experimentally assessed on a magnetic levitation plant for reference tracking application. Experimental results substantiate that the EIMC scheme can effectively counteract the inherent time delay in the model and offer precise tracking, even in the presence of exogenous disturbance. Moreover, by comparing the trajectory tracking performance of EIMC with that of the proportional integral velocity (PIV) controller through cumulative power spectral density (CPSD) of the tracking error, we show that the EIMC can offer better low frequency servo response with minimal vibrations.

2 citations

Book ChapterDOI
01 Jan 2018
TL;DR: The hybrid control algorithm is put forward, which integrates the iterative learning control (ILC) scheme with proportional integral velocity (PIV) control, for improved trajectory tracking of magnetic levitation system.
Abstract: This paper puts forward the hybrid control algorithm, which integrates the iterative learning control (ILC) scheme with proportional integral velocity (PIV) control, for improved trajectory tracking of magnetic levitation system. ILC is a type of model-free controller, which is used for systems that perform repetitive tasks. Adjusting the control inputs based on the error information obtained during previous iterations, ILC tries to enhance the transient response of the closed-loop system. One of the striking features of ILC is that even without the full dynamic model of the plant, it can yield perfect trajectory tracking by learning the plant dynamics through iterations. Adopting this learning control feature of ILC, this paper aims to synthesize ILC with PIV for both improved tracking and better robustness compared to conventional PIV. The efficacy of the proposed ILC-PIV controller framework is assessed through a simulation study on the magnetic levitation plant for reference following application.

Cited by
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Journal ArticleDOI
TL;DR: In this paper , the design of internal model-based dynamic sliding mode control for a DC-DC boost converter was considered and the results were compared with the basic internal model control (IMC) and two-degree-of-freedom IMC (TDF-IMC).
Abstract: This paper considers the design of internal model-based dynamic sliding mode control for a DC-DC boost converter. DC-DC boost converter transfer function model has non-minimum phase behavior with a zero in the right hand side of s-plane. The control structure considered in the paper constitutes an internal model which is so chosen that it compensate the non-minimum phase nature of the system due to a right-hand plane zero of s-plane. Then, a dynamic sliding mode control (SMC) is designed to have a chattering free control signal. A PID type surface is chosen for the dynamic SMC and the surface parameters are tuned to have a critically damped tracking response. Simulation results are carried out for tacking and disturbance rejection and the results are compared with the basic internal model control (IMC) and two-degree-of-freedom IMC (TDF-IMC). It is observed that disturbance rejection for the dynamic SMC is much better than the basic IMC as well as TDF-IMC.

2 citations

Book ChapterDOI
05 Mar 2021
TL;DR: In this paper, configurations of Iterative Learning Control (ILC) scheme namely, current cycle feedback, previous cycle feedback (PCF), Previous and Current cycle Feedback (PCCF) are analyzed, intended to improve trajectory tracking and comparison of results are made under, Nominal Case, External Disturbance and Model Uncertainty on Magnetic levitation system through HIL Mathematical model of Maglev, obtained using Newton second law.
Abstract: Iterative Learning Control (ILC) aim is to improve control performance through iterations. ILC enhances transient behavior of the system, without knowing entire dynamics of plant model. Applied in feedforward path, it learns from past iteration and improves current iteration. Conventional ILC shows instability for exogenous disturbances, thus various ILC schemes are studied. In this paper, configurations of Iterative Learning Control (ILC) scheme namely, current cycle feedback (CCF), previous cycle feedback (PCF), Previous and Current cycle feedback (PCCF) are analyzed, intended to improve trajectory tracking and comparison of results are made under, Nominal Case, External Disturbance and Model Uncertainty on Magnetic levitation system through HIL Mathematical model of Maglev, obtained using Newton second law. To stabilize the open loop unstable system a PIV feedback controller is implemented.