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kranthi kumar Deveerasetty

Osaka University, Graduate School of Engineering, Researcher

Indian Institute of Technology (BHU), Varanasi, ELECTRICAL ENGG, Graduate Student

Kochi University of Technology, School of Systems Engineering, Post-Doc

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Dr. Kranthi Kumar Deveerasetty has received the degree of B.E from SIR. C. R. Reddy College of Engineering, Andhra University in 2008 and received the degree of M.Tech in Electrical Engineering from Indian Institute of Technology (BHU), Varanasi, in 2010 and received the degree of Ph.D in Electrical Engineering at Indian Institute of Technology (BHU), Varanasi, India in 2016 under the supervision of Prof. S. K. Nagar. Previously, he was a Post-Doctoral Fellow at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China from Aug 2017-Nov.2019. Currently, he is a Post-doctoral Fellow at Kochi University of Technology, Japan. His research interests are model order reduction, analysis of uncertain systems and controller design for UAV's. During pursuing of M.Tech degree, he received Post- graduation fellowship from the University of Grants Commission, India. Later, in Ph.D., he received Rajiv Gandhi National Fellowship from the University of Grants Commission, India.
Supervisors: Prof.S.K. Nagar and Prof. J. P. Tewari (Ex- co supervisor)
Address: Varanasi, Uttar Pradesh, India

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Papers by kranthi kumar Deveerasetty

Control and simulation test of an unmanned aerial vehicle

by kranthi kumar Deveerasetty and Kochi Oka

The unmanned aerial vehicle (UAV) is coupled with an under-actuated system, and designing a contr... more The unmanned aerial vehicle (UAV) is coupled with an under-actuated system, and designing a controller is a great challenge. This manuscript's main objective is to provide an accurate trajectory tracking of a 4-rotor helicopter UAV with an appropriate tuning controller. To achieve this object, we proposed a new controller to obtain the desired path of a quadrotor. To evaluate the virtue of the proposed controller (Tilt-Fractional Order-Integral-Derivative (T I λ D µ) controller) are compared to the other existing controllers such as TID controller, PID controller, and P I λ D µ controller. The transfer function of the proposed controller is K t s 1 / n + K i s λ + K d s µ where Kt, Ki, and K d are unknown parameters, and n, λ, and µ are known positive real parameters. The validation of the results is experimentally assessed via simulation.

Computation of Impulse-Response Gramian for Interval Systems

IETE Journal of Research, 2019

This paper presents an impulse response gramian for the interval systems. To reduce the higher-or... more This paper presents an impulse response gramian for the interval systems. To reduce the higher-order interval systems, two methods used to evaluate the impulse response gramian is computed in the frequency domain. The first method is based on Routh technique with (α − β) table to evaluate energy integrals for finding the diagonal impulse response gramian. The second method is based on the Inners technique to compute the diagonal elements of the Gramian matrix, while the off-diagonal elements are obtained by the Markov parameters. The method is computationally simple but the reduced-order stable interval systems are difficult to obtain. To overcome this problem, the reduced-order model is developed based on Kharitonov's theorem. The advantage of the proposed technique is useful for system identification and reduction technique. Illustrative examples are included and results are compared and stability of the reduced-order model is guaranteed.

Trajectory tracking of a quadrotor using TID controller

2019 Australian & New Zealand Control Conference (ANZCC), 2019

In this paper, a TID controller is applied for the trajectory tracking of a quadrotor. The mathem... more In this paper, a TID controller is applied for the trajectory tracking of a quadrotor. The mathematical model of the control system is derived and compared with the traditional PD (Proportional-Derivative) controller. In order to improve the control precision of the UAV, the controller is designed by selecting the proper tuning parameters. To explore the effectiveness of the proposed controller, dynamic responses of a UAV obtained by using the TID controller and the validation of the results compared with the PD controller. The control performances are analysed by using MATLAB/Simulink model.

Reduction of SISO Discrete Time Systems Based on GA Approach

This paper presents an algorithm for reduction of discrete time systems using Genetic algorithm (... more This paper presents an algorithm for reduction of discrete time systems using Genetic algorithm (GA). GA method is based on minimizing the integral square error between the transient responses of original high order and reduced order models. The proposed method is simple, comparable in quality and computer oriented. It is shown that the algorithm has several advantages, the reduced order models retain the stability and steady-state value of the original system.

Model Order Reduction of S.I.S.O and M.I.M.O Systems Based on Genetic Algorithm

by kranthi kumar Deveerasetty, KRANTHI K U M A R DEVEERASETTY, and SK Bharadwaj

The authors present an algorithm for order reduction of linear systems using the error minimizati... more The authors present an algorithm for order reduction of linear systems using the error minimization by Genetic algorithm (GA). The lower order transfer function are determined by minimizing the integral square error between the transient responses of original and reduced order models using Genetic algorithm. The reduction procedure is simple and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. Genetic Algorithm techniques guarantee stability of reduced order model if the original high order model is stable. the method illustrated through numerical example from literature and the results are compared with other model reduction techniques .

Direct Truncation and Factor Division Method for Order Reduction of Interval Systems

by kranthi kumar Deveerasetty and KRANTHI K U M A R DEVEERASETTY

The paper proposes mixed method for reducing the order of interval systems i.e., systems having u... more The paper proposes mixed method for reducing the order of interval systems i.e., systems having
uncertain but bounded parameters. The denominator of the reduced order model is obtained by direct truncation
method and numerator is derived by direct truncation method and factor division method. A numerical example
has been discussed to illustrate the procedures. The errors between the original higher order and reduced order
models have also been highlighted to support the effectiveness of the proposed methods.

Order Reducing Order of Interval Systems Using Direct Routh Approximation Method

A simple and direct method of approximating higher order interval systems by lower order interval... more A simple and direct method of approximating
higher order interval systems by lower order interval systems
based on Routh approximation without using reciprocal
transformation. Two numerical examples have been discussed
to illustrate the procedures. The errors between the original and
reduced order models are compared.

Mixed Methods for Order Reduction of Linear Continuous Time Interval Systems

The paper proposes mixed methods for reducing the order of interval systems i.e., systems having ... more The paper proposes mixed methods for
reducing the order of interval systems i.e., systems having
uncertain but bounded parameters. The denominator of the
reduced order model is obtained by the differentiation method
and numerator is derived by applying mixed methods such as
differentiation method, factor division and Cauer second form.
A numerical example has been discussed to illustrate the
procedures. The errors between the original higher order and
reduced order models have also been highlighted to support
the effectiveness of the proposed methods.

Mixed method for reducing the order of a linear discrete time interval system

The paper proposes a mixed method for reducing the order of discrete time interval systems. The n... more The paper proposes a mixed method for reducing
the order of discrete time interval systems. The numerator and
denominator of the reduced order model is obtained by
forming  table and combining factor division method and
Cauer second form. Bilinear transformation (z = p+1) is used
during the process of higher order discrete interval systems
before applying reduction method. The proposed method shows
that it is well suited to reduce both denominator and numerator
interval discrete polynomials. The proposed method is compared
with the existing methods of discrete interval system reduction. A
numerical example has been discussed to illustrate the procedure.
Accuracy of the system stability has been verified by using step
response.

Model Order Reduction of Interval Systems Using Modified Routh Approximation and Factor Divison Method

This paper presents a mixed method for reducing order of the large scale interval systems using t... more This paper presents a mixed method for reducing order of the large scale interval systems using the Routh approximation and factor division method. The denominator coefficients of reduced order model is determined by using Routh approximation and numerator coefficients are obtained by using Factor division method. The mixed methods are simple and guarantee the stability of the reduced model if the original system is stable. A numerical example is discussed to illustrate the usefulness of the proposed method.

Order Reduction of Interval Systems Using Alpha and Factor Division Method

Model Order Reduction of Interval Systems Using Mihailov Criterion and Routh Approximation

by kranthi kumar Deveerasetty and KRANTHI K U M A R DEVEERASETTY

This paper presents a mixed method for reducing order of the large scale interval systems using t... more This paper presents a mixed method for reducing order of the large scale interval systems using the Mihailov Criterion and Routh approximation. The denominator coefficients of reduced order model is determined by using Mihailov Criterion and numerator coefficients are obtained by using Routh approximation. The mixed method is simple and guarantees the stability of the reduced model if the original system is stable. A numerical example is discussed to illustrate the usefulness of the proposed method.

Model Order Reduction of Interval Systems Using Routh Approximation and Cauer Second Form

Model Order Reduction of Interval Systems using Mihailov Criterion and Factor Division Method

This paper presents a mixed method for reducing order of the large scale interval systems using t... more This paper presents a mixed method for reducing order of the large scale interval systems using the Mihailov Criterion and factor division method. The denominator coefficients of reduced order model is determined by using Mihailov Criterion and numerator coefficients are obtained by using Factor division method. The mixed methods are simple and guarantee the stability of the reduced model if the original system is stable. Numerical examples are discussed to illustrate the usefulness of the proposed method.

Model Order Reduction of Interval Systems Using Mihailov Criterion and Cauer Second Form

This paper presents a new mixed method for reducing the large scale interval systems using the Mi... more This paper presents a new mixed method for reducing the large scale interval systems using the Mihailov Criterion and Cauer second form. The reduced order model of denominator is determined by using Mihailov Criterion and numerator coefficients are obtained by using Cauer second form. We show that the mixed method is simple and guarantees the stability of the reduced model if the original system is stable.

A New Algorithm for Model Order Reduction of Interval Systems

Mixed method of interval systems is a combination of classical reduction methods and stability pr... more Mixed method of interval systems is a combination of classical reduction methods and stability preserving methods of interval systems. This paper proposed a new method for model order reduction of systems with uncertain parameters. The bounds on the uncertain parameters are known a priori. Two separate methods are used for finding parameters of the numerator and denominator. The numerator parameters are obtained by either of these methods such as differentiation method, factor division method, cauer second form, moment matching method or Pade approximation method. The denominator is obtained by the differentiation method in all the cases. A numerical example has been discussed to illustrate the procedures. From the above mixed methods, differentiation method and cauer second form as resulted in better approximation when compared with other methods. The errors between the original higher order and reduced order models have also been highlighted to support the effectiveness of the proposed methods.

UAV by kranthi kumar Deveerasetty

Conference Presentations by kranthi kumar Deveerasetty

PID with derivative filter and integral sliding-mode controller techniques applied to an indoor micro quadrotor

by kranthi kumar Deveerasetty and yimin zhou

18th International Conference on Control, Automation and Systems (ICCAS 2018), 2018

The paper proposes two different controllers for tracking control of a quadrotor. An Euler-Lagran... more The paper proposes two different controllers for tracking control of a quadrotor. An Euler-Lagrange formulation is used to drive the dynamic mathematical model of a quadrotor. To illustrate the advantages of the proposed controllers and for simplicity, all the aerodynamics, Coriolis, drags and gyroscopic effect are abandoned. A PID controller with derivative filter is proposed for the position tracking of the quadrotor here. The advantage of the proposed PID controller include easiness of implementation, effectuate higher stability and less dependent on the system model. The integral sliding-mode controller is proposed to obtain the desired attitude angles for the quadrotor tracking and its advantage is unaffected by the system model errors. The designed controllers can stabilize the roll, pitch, and yaw angles and make the quadrotor to move (x, y, z) positions to their desired values. The Lyapunovs theorem is useful to obtain the quadrotor controllers and also ensure the system track to attain and maintain on the sliding surfaces. The validation of results has been tested in MATLAB simulation and exhibits adequate performance.

Control and simulation test of an unmanned aerial vehicle

by kranthi kumar Deveerasetty and Kochi Oka

The unmanned aerial vehicle (UAV) is coupled with an under-actuated system, and designing a contr... more The unmanned aerial vehicle (UAV) is coupled with an under-actuated system, and designing a controller is a great challenge. This manuscript's main objective is to provide an accurate trajectory tracking of a 4-rotor helicopter UAV with an appropriate tuning controller. To achieve this object, we proposed a new controller to obtain the desired path of a quadrotor. To evaluate the virtue of the proposed controller (Tilt-Fractional Order-Integral-Derivative (T I λ D µ) controller) are compared to the other existing controllers such as TID controller, PID controller, and P I λ D µ controller. The transfer function of the proposed controller is K t s 1 / n + K i s λ + K d s µ where Kt, Ki, and K d are unknown parameters, and n, λ, and µ are known positive real parameters. The validation of the results is experimentally assessed via simulation.

Computation of Impulse-Response Gramian for Interval Systems

IETE Journal of Research, 2019

This paper presents an impulse response gramian for the interval systems. To reduce the higher-or... more This paper presents an impulse response gramian for the interval systems. To reduce the higher-order interval systems, two methods used to evaluate the impulse response gramian is computed in the frequency domain. The first method is based on Routh technique with (α − β) table to evaluate energy integrals for finding the diagonal impulse response gramian. The second method is based on the Inners technique to compute the diagonal elements of the Gramian matrix, while the off-diagonal elements are obtained by the Markov parameters. The method is computationally simple but the reduced-order stable interval systems are difficult to obtain. To overcome this problem, the reduced-order model is developed based on Kharitonov's theorem. The advantage of the proposed technique is useful for system identification and reduction technique. Illustrative examples are included and results are compared and stability of the reduced-order model is guaranteed.

Trajectory tracking of a quadrotor using TID controller

2019 Australian & New Zealand Control Conference (ANZCC), 2019

In this paper, a TID controller is applied for the trajectory tracking of a quadrotor. The mathem... more In this paper, a TID controller is applied for the trajectory tracking of a quadrotor. The mathematical model of the control system is derived and compared with the traditional PD (Proportional-Derivative) controller. In order to improve the control precision of the UAV, the controller is designed by selecting the proper tuning parameters. To explore the effectiveness of the proposed controller, dynamic responses of a UAV obtained by using the TID controller and the validation of the results compared with the PD controller. The control performances are analysed by using MATLAB/Simulink model.

Reduction of SISO Discrete Time Systems Based on GA Approach

This paper presents an algorithm for reduction of discrete time systems using Genetic algorithm (... more This paper presents an algorithm for reduction of discrete time systems using Genetic algorithm (GA). GA method is based on minimizing the integral square error between the transient responses of original high order and reduced order models. The proposed method is simple, comparable in quality and computer oriented. It is shown that the algorithm has several advantages, the reduced order models retain the stability and steady-state value of the original system.

Model Order Reduction of S.I.S.O and M.I.M.O Systems Based on Genetic Algorithm

by kranthi kumar Deveerasetty, KRANTHI K U M A R DEVEERASETTY, and SK Bharadwaj

The authors present an algorithm for order reduction of linear systems using the error minimizati... more The authors present an algorithm for order reduction of linear systems using the error minimization by Genetic algorithm (GA). The lower order transfer function are determined by minimizing the integral square error between the transient responses of original and reduced order models using Genetic algorithm. The reduction procedure is simple and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. Genetic Algorithm techniques guarantee stability of reduced order model if the original high order model is stable. the method illustrated through numerical example from literature and the results are compared with other model reduction techniques .

Direct Truncation and Factor Division Method for Order Reduction of Interval Systems

by kranthi kumar Deveerasetty and KRANTHI K U M A R DEVEERASETTY

The paper proposes mixed method for reducing the order of interval systems i.e., systems having u... more The paper proposes mixed method for reducing the order of interval systems i.e., systems having
uncertain but bounded parameters. The denominator of the reduced order model is obtained by direct truncation
method and numerator is derived by direct truncation method and factor division method. A numerical example
has been discussed to illustrate the procedures. The errors between the original higher order and reduced order
models have also been highlighted to support the effectiveness of the proposed methods.

Order Reducing Order of Interval Systems Using Direct Routh Approximation Method

A simple and direct method of approximating higher order interval systems by lower order interval... more A simple and direct method of approximating
higher order interval systems by lower order interval systems
based on Routh approximation without using reciprocal
transformation. Two numerical examples have been discussed
to illustrate the procedures. The errors between the original and
reduced order models are compared.

Mixed Methods for Order Reduction of Linear Continuous Time Interval Systems

The paper proposes mixed methods for reducing the order of interval systems i.e., systems having ... more The paper proposes mixed methods for
reducing the order of interval systems i.e., systems having
uncertain but bounded parameters. The denominator of the
reduced order model is obtained by the differentiation method
and numerator is derived by applying mixed methods such as
differentiation method, factor division and Cauer second form.
A numerical example has been discussed to illustrate the
procedures. The errors between the original higher order and
reduced order models have also been highlighted to support
the effectiveness of the proposed methods.

Mixed method for reducing the order of a linear discrete time interval system

The paper proposes a mixed method for reducing the order of discrete time interval systems. The n... more The paper proposes a mixed method for reducing
the order of discrete time interval systems. The numerator and
denominator of the reduced order model is obtained by
forming  table and combining factor division method and
Cauer second form. Bilinear transformation (z = p+1) is used
during the process of higher order discrete interval systems
before applying reduction method. The proposed method shows
that it is well suited to reduce both denominator and numerator
interval discrete polynomials. The proposed method is compared
with the existing methods of discrete interval system reduction. A
numerical example has been discussed to illustrate the procedure.
Accuracy of the system stability has been verified by using step
response.

Model Order Reduction of Interval Systems Using Modified Routh Approximation and Factor Divison Method

This paper presents a mixed method for reducing order of the large scale interval systems using t... more This paper presents a mixed method for reducing order of the large scale interval systems using the Routh approximation and factor division method. The denominator coefficients of reduced order model is determined by using Routh approximation and numerator coefficients are obtained by using Factor division method. The mixed methods are simple and guarantee the stability of the reduced model if the original system is stable. A numerical example is discussed to illustrate the usefulness of the proposed method.

Order Reduction of Interval Systems Using Alpha and Factor Division Method

Model Order Reduction of Interval Systems Using Mihailov Criterion and Routh Approximation

by kranthi kumar Deveerasetty and KRANTHI K U M A R DEVEERASETTY

This paper presents a mixed method for reducing order of the large scale interval systems using t... more This paper presents a mixed method for reducing order of the large scale interval systems using the Mihailov Criterion and Routh approximation. The denominator coefficients of reduced order model is determined by using Mihailov Criterion and numerator coefficients are obtained by using Routh approximation. The mixed method is simple and guarantees the stability of the reduced model if the original system is stable. A numerical example is discussed to illustrate the usefulness of the proposed method.

Model Order Reduction of Interval Systems Using Routh Approximation and Cauer Second Form

Model Order Reduction of Interval Systems using Mihailov Criterion and Factor Division Method

This paper presents a mixed method for reducing order of the large scale interval systems using t... more This paper presents a mixed method for reducing order of the large scale interval systems using the Mihailov Criterion and factor division method. The denominator coefficients of reduced order model is determined by using Mihailov Criterion and numerator coefficients are obtained by using Factor division method. The mixed methods are simple and guarantee the stability of the reduced model if the original system is stable. Numerical examples are discussed to illustrate the usefulness of the proposed method.

Model Order Reduction of Interval Systems Using Mihailov Criterion and Cauer Second Form

This paper presents a new mixed method for reducing the large scale interval systems using the Mi... more This paper presents a new mixed method for reducing the large scale interval systems using the Mihailov Criterion and Cauer second form. The reduced order model of denominator is determined by using Mihailov Criterion and numerator coefficients are obtained by using Cauer second form. We show that the mixed method is simple and guarantees the stability of the reduced model if the original system is stable.

A New Algorithm for Model Order Reduction of Interval Systems

Mixed method of interval systems is a combination of classical reduction methods and stability pr... more Mixed method of interval systems is a combination of classical reduction methods and stability preserving methods of interval systems. This paper proposed a new method for model order reduction of systems with uncertain parameters. The bounds on the uncertain parameters are known a priori. Two separate methods are used for finding parameters of the numerator and denominator. The numerator parameters are obtained by either of these methods such as differentiation method, factor division method, cauer second form, moment matching method or Pade approximation method. The denominator is obtained by the differentiation method in all the cases. A numerical example has been discussed to illustrate the procedures. From the above mixed methods, differentiation method and cauer second form as resulted in better approximation when compared with other methods. The errors between the original higher order and reduced order models have also been highlighted to support the effectiveness of the proposed methods.

PID with derivative filter and integral sliding-mode controller techniques applied to an indoor micro quadrotor

by kranthi kumar Deveerasetty and yimin zhou

18th International Conference on Control, Automation and Systems (ICCAS 2018), 2018

The paper proposes two different controllers for tracking control of a quadrotor. An Euler-Lagran... more The paper proposes two different controllers for tracking control of a quadrotor. An Euler-Lagrange formulation is used to drive the dynamic mathematical model of a quadrotor. To illustrate the advantages of the proposed controllers and for simplicity, all the aerodynamics, Coriolis, drags and gyroscopic effect are abandoned. A PID controller with derivative filter is proposed for the position tracking of the quadrotor here. The advantage of the proposed PID controller include easiness of implementation, effectuate higher stability and less dependent on the system model. The integral sliding-mode controller is proposed to obtain the desired attitude angles for the quadrotor tracking and its advantage is unaffected by the system model errors. The designed controllers can stabilize the roll, pitch, and yaw angles and make the quadrotor to move (x, y, z) positions to their desired values. The Lyapunovs theorem is useful to obtain the quadrotor controllers and also ensure the system track to attain and maintain on the sliding surfaces. The validation of results has been tested in MATLAB simulation and exhibits adequate performance.