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Papers by Rupam Gupta Roy
Advances in Communication, Devices and Networking, 2018
This investigation deals with the introduction of a noble dynamic fuzzy model reference adaptive ... more This investigation deals with the introduction of a noble dynamic fuzzy model reference adaptive control scheme. In this work, we propose a new model of MRAC using fuzzy control for the speed control of DC Motor. The starting of our work is done with the general comprehensive designing of MRAC for first-order process along with the second-order process using MIT Rule. After that, the description regarding our proposed model is given. For the evaluation of the performance of the controller, fuzzy-based MRAC is applied on DC Motor. The simulation results are compared with other controllers showing that the reaching time and tracking can be extensively reduced.
Journal of Computational Mechanics, Power System and Control, 2020
This paper presents a competent meta-heuristic algorithm, named, Particle Swarm Optimization-Enha... more This paper presents a competent meta-heuristic algorithm, named, Particle Swarm Optimization-Enhanced Bat optimization (PSO-EBO) algorithm to solve the optimal operating approach of the ELD issue. The developed technique integrates two fundamental ideas. Initially, the swarm behavior of the particle is exploited to find the optimal values of the EBO technique. To illustrate the developed method performance, it is used on large, medium, and small, scale test systems to solve the ELD issues of 13-unit, 40-unit and160-unit systems. Moreover, the comparative results are performed to examine the effectiveness of the developed PSO-EBO algorithm with the existing Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), Whale Optimization Algorithm (WOA) and Differential Evolution (DE) algorithms. Finally, the experimentation outcomes obviously propose that the developed PSO-EBO algorithm can find superior solutions regarding the computational time and fuel cost than conventional methods.
Engineering Computations, 2019
Purpose Advanced heavy water reactor (AHWR) is a pressure tube type of heavy water reactor. It el... more Purpose Advanced heavy water reactor (AHWR) is a pressure tube type of heavy water reactor. It eliminates high-pressure heavy water coolant resulting in a reduction of heavy water leakage losses and eliminating heavy water recovery system. It recovers the heat generated in the moderator for feed water heating. However, it requires a satisfactory technological response to develop an effective controller that attains the challenges of the very high-level safety system. Hence, they require application-specific improvement for better controlling performance. Design/methodology/approach The purpose of this study intends to propose a system for controlling state vectors v1 and v2and in AHWR using Grey Wolf second-order sliding mode control (GW-SoSMC) technique. The main aim of the paper is to minimize the errors between the predicted and desired azimuthal angles of the system. With this proposed method, it is possible to mitigate both the chattering phenomenon and controlling performance ...
IEEE Access, 2020
This paper presents an adaptive neuro-fuzzy sliding mode control (ANFSMC) scheme for diving motio... more This paper presents an adaptive neuro-fuzzy sliding mode control (ANFSMC) scheme for diving motion control of an autonomous underwater vehicle (AUV) in the presence of parameter perturbations and wave disturbances. In the derivation of diving motion equations of an AUV, the pitch angle of the vehicle is often assumed to be small in the vertical plane. This is a quite strong restricting condition in underwater operations and may cause serious modeling inaccuracies in AUV's dynamics. The problem of nonlinear uncertain diving behavior with restricting assumption on the pitch angle directly is resolved by a neural network (NN) based equivalent control. The online NN estimator is designed to approximate a part of the equivalent control term containing nonlinear unknown dynamics and external disturbances. Subsequently, corrective control based on an adaptive fuzzy proportional-integral control is applied to eliminate the chattering phenomenon by smoothing the switching signal and also compensate structured uncertainties. The weights of NN are updated such that the corrective control signal of the ANFSMC converges towards zero. The adaptive laws are developed to compute coefficients of PID sliding manifold and adjust the gain of fuzzy switching control. The simulation results are presented to shows the efficacy of the control performance. INDEX TERMS Autonomous underwater vehicle, adaptive neuro-fuzzy sliding mode control, diving motion, neural network, parameter perturbations and chattering phenomenon.
Robotica, 2019
SUMMARYThe flexible motion of the inchworm makes the locomotion mechanism as the prominent one th... more SUMMARYThe flexible motion of the inchworm makes the locomotion mechanism as the prominent one than other limbless animals. Recently, the application of engineering greatly assists the inchworm locomotion to be applicable in the robotic mechanism. Due to the outstanding robustness, sliding mode control (SMC) has been validated as a robust control strategy for diverse types of systems. Even though the SMC techniques have made numerous achievements in several fields, some systems cannot be comfortably accepted as the general SMC approaches. Accordingly, this paper develops the Grey Wolf-Second order sliding mode control (GW-SoSMC) to control the manipulator of the inchworm robot. The GW-SoSMC reduces the chattering phenomenon of SMC and improves the controlling ability of SoSMC by weightage function. Subsequently, it compares the performance of the proposed method with several conventional techniques like Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (A...
IEEE Transactions on Intelligent Vehicles, 2019
A new adaptive robust control scheme combining the disturbance-observer-based control (DOBC) with... more A new adaptive robust control scheme combining the disturbance-observer-based control (DOBC) with fuzzy adapted S-Surface control is proposed for trajectory tracking control of autonomous underwater vehicle. The main contribution of the proposed method is that control configuration does not require the bounds of uncertainty of the vehicle to be known and disturbances effect can be estimated and rejected. The proposed control law is mainly composed of three parts: a feed forward control along with disturbance estimator, S-Surface control and single input adaptive fuzzy proportional-integral (PI) compensator. The nominal feed forward controller specifies desired closed loop dynamics with extravagance from known preferred acceleration vector. Meanwhile, the disturbance observer and adaptive fuzzy PI control term to compensate the unknown effects that are disturbances and unmodeled dynamics. An additional term as S-Surface control assures fast convergence due to a nonlinear expression in to surface and also enhance stability of the underwater vehicle in oceanic environment. Moreover, the disturbance observer enhances the robustness performance of the adaptive fuzzy system for disturbances that cannot be modeled by fuzzy logic. The stability of closed loop controlled system is proven to be guaranteed according to Lyapunov theory. Finally, numerical simulation results illustrate the effectiveness and robustness of the proposed control method.
2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], 2014
A novel dynamic fuzzy sliding mode control (DF-SMC) algorithm is developed for heading angle cont... more A novel dynamic fuzzy sliding mode control (DF-SMC) algorithm is developed for heading angle control of autonomous underwater vehicles (AUV's) in horizontal plane. So far, the dynamics of AUV's are highly nonlinear, time varying and hydrodynamic coefficients of vehicle are difficult to be accurately estimated a prior, because of the variations of these coefficients with different operating conditions. These kinds of difficulties cause modeling inaccuracies of AUV's dynamics. Therefore, DFSM C is proposed for regulating heading angle in horizontal plane in presence of parametric uncertainty and disturbances. In this approach, two fuzzy approximators are employed in such a way that, to vary the supports of input-output fuzzy membership functions in the inference engine module. These fuzzy approximators are mainly utilized for updating width of boundary layer and hitting gain. Simulation results shows that, the reaching time and tracking error in the approaching phase can be significantly reduced with chattering problem can also be eliminated. The effectiveness of proposed control strategy and its advantages are indicated in comparison with conventional sliding mode control and fuzzy sliding mode control.
2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], 2014
In this paper, decision making scheme in cognitive radio is proposed by using fuzzy neural system... more In this paper, decision making scheme in cognitive radio is proposed by using fuzzy neural system, due to which secondary users can utilizes the spectrum effectively with seamless communication between cognitive radio and primary users. The spectrum sensing performance is enhanced by using either multiple antennas or multistage spectrum sensing. Due to multiple antennas at sensing node causes more equipment cost in spectrum sensing technique, therefore two stage spectrum sensing scheme is introduced. The proposed fuzzy neural decision making technique include two stage spectrum sensing schemes for identifying available spectrum. In first stage, three parameters such as spectrum utilization efficiency, degree of mobility and distance to the primary user of cognitive radio network are considered as inputs to fuzzy logic decision making process, while output of that process gives spectrum access decision, based on linguistic knowledge of 27 rules. Feedback neural network configuration included in second stage of spectrum sensing, which is trained with the help of generalized delta learning rule. Neural network has two input parameters such as output of fuzzy logic based spectrum sensing and desired values. The reference signal of neural network is obtain from transformation of output membership function in first stage to their mid singleton values. Simulation results shows significant improvement in sensing accuracy by exhibiting higher probability of detection.
Adaptive Behavior, 2019
Navigating, directing, and controlling autonomous underwater vehicles (AUVs) are demanding and co... more Navigating, directing, and controlling autonomous underwater vehicles (AUVs) are demanding and considered complicated compared to the autonomous surface-level performance. In such vehicles, the motion can be controlled depending on the estimation of indefinite hydrodynamic forces and the disturbances that occurs in this vehicle in the underwater background. In this article, Gray Wolf optimization (GWO) is performed along with the second-order sliding mode control (GW-SoSMC) approach for controlling the yaw angle in AUV. The main purpose of the article is to reduce the error that occurs in the system between the controlled signal and desired signal corresponding to yaw angle. Using this proposed model, both the occurrence of chattering and the controlling performance of AUV system can be diminished. Moreover, the proposed model is compared with the existing approaches like, FireFly-SoSMC (FF-SoSMC), Genetic Algorithm-SoSMC (GA-SoSMC), Gray Wolf-SMC (GW-SMC), Group search optimization...
Advances in Communication, Devices and Networking, 2018
This investigation deals with the introduction of a noble dynamic fuzzy model reference adaptive ... more This investigation deals with the introduction of a noble dynamic fuzzy model reference adaptive control scheme. In this work, we propose a new model of MRAC using fuzzy control for the speed control of DC Motor. The starting of our work is done with the general comprehensive designing of MRAC for first-order process along with the second-order process using MIT Rule. After that, the description regarding our proposed model is given. For the evaluation of the performance of the controller, fuzzy-based MRAC is applied on DC Motor. The simulation results are compared with other controllers showing that the reaching time and tracking can be extensively reduced.
Journal of Computational Mechanics, Power System and Control, 2020
This paper presents a competent meta-heuristic algorithm, named, Particle Swarm Optimization-Enha... more This paper presents a competent meta-heuristic algorithm, named, Particle Swarm Optimization-Enhanced Bat optimization (PSO-EBO) algorithm to solve the optimal operating approach of the ELD issue. The developed technique integrates two fundamental ideas. Initially, the swarm behavior of the particle is exploited to find the optimal values of the EBO technique. To illustrate the developed method performance, it is used on large, medium, and small, scale test systems to solve the ELD issues of 13-unit, 40-unit and160-unit systems. Moreover, the comparative results are performed to examine the effectiveness of the developed PSO-EBO algorithm with the existing Grey Wolf Optimization (GWO), Artificial Bee Colony (ABC), Whale Optimization Algorithm (WOA) and Differential Evolution (DE) algorithms. Finally, the experimentation outcomes obviously propose that the developed PSO-EBO algorithm can find superior solutions regarding the computational time and fuel cost than conventional methods.
Engineering Computations, 2019
Purpose Advanced heavy water reactor (AHWR) is a pressure tube type of heavy water reactor. It el... more Purpose Advanced heavy water reactor (AHWR) is a pressure tube type of heavy water reactor. It eliminates high-pressure heavy water coolant resulting in a reduction of heavy water leakage losses and eliminating heavy water recovery system. It recovers the heat generated in the moderator for feed water heating. However, it requires a satisfactory technological response to develop an effective controller that attains the challenges of the very high-level safety system. Hence, they require application-specific improvement for better controlling performance. Design/methodology/approach The purpose of this study intends to propose a system for controlling state vectors v1 and v2and in AHWR using Grey Wolf second-order sliding mode control (GW-SoSMC) technique. The main aim of the paper is to minimize the errors between the predicted and desired azimuthal angles of the system. With this proposed method, it is possible to mitigate both the chattering phenomenon and controlling performance ...
IEEE Access, 2020
This paper presents an adaptive neuro-fuzzy sliding mode control (ANFSMC) scheme for diving motio... more This paper presents an adaptive neuro-fuzzy sliding mode control (ANFSMC) scheme for diving motion control of an autonomous underwater vehicle (AUV) in the presence of parameter perturbations and wave disturbances. In the derivation of diving motion equations of an AUV, the pitch angle of the vehicle is often assumed to be small in the vertical plane. This is a quite strong restricting condition in underwater operations and may cause serious modeling inaccuracies in AUV's dynamics. The problem of nonlinear uncertain diving behavior with restricting assumption on the pitch angle directly is resolved by a neural network (NN) based equivalent control. The online NN estimator is designed to approximate a part of the equivalent control term containing nonlinear unknown dynamics and external disturbances. Subsequently, corrective control based on an adaptive fuzzy proportional-integral control is applied to eliminate the chattering phenomenon by smoothing the switching signal and also compensate structured uncertainties. The weights of NN are updated such that the corrective control signal of the ANFSMC converges towards zero. The adaptive laws are developed to compute coefficients of PID sliding manifold and adjust the gain of fuzzy switching control. The simulation results are presented to shows the efficacy of the control performance. INDEX TERMS Autonomous underwater vehicle, adaptive neuro-fuzzy sliding mode control, diving motion, neural network, parameter perturbations and chattering phenomenon.
Robotica, 2019
SUMMARYThe flexible motion of the inchworm makes the locomotion mechanism as the prominent one th... more SUMMARYThe flexible motion of the inchworm makes the locomotion mechanism as the prominent one than other limbless animals. Recently, the application of engineering greatly assists the inchworm locomotion to be applicable in the robotic mechanism. Due to the outstanding robustness, sliding mode control (SMC) has been validated as a robust control strategy for diverse types of systems. Even though the SMC techniques have made numerous achievements in several fields, some systems cannot be comfortably accepted as the general SMC approaches. Accordingly, this paper develops the Grey Wolf-Second order sliding mode control (GW-SoSMC) to control the manipulator of the inchworm robot. The GW-SoSMC reduces the chattering phenomenon of SMC and improves the controlling ability of SoSMC by weightage function. Subsequently, it compares the performance of the proposed method with several conventional techniques like Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (A...
IEEE Transactions on Intelligent Vehicles, 2019
A new adaptive robust control scheme combining the disturbance-observer-based control (DOBC) with... more A new adaptive robust control scheme combining the disturbance-observer-based control (DOBC) with fuzzy adapted S-Surface control is proposed for trajectory tracking control of autonomous underwater vehicle. The main contribution of the proposed method is that control configuration does not require the bounds of uncertainty of the vehicle to be known and disturbances effect can be estimated and rejected. The proposed control law is mainly composed of three parts: a feed forward control along with disturbance estimator, S-Surface control and single input adaptive fuzzy proportional-integral (PI) compensator. The nominal feed forward controller specifies desired closed loop dynamics with extravagance from known preferred acceleration vector. Meanwhile, the disturbance observer and adaptive fuzzy PI control term to compensate the unknown effects that are disturbances and unmodeled dynamics. An additional term as S-Surface control assures fast convergence due to a nonlinear expression in to surface and also enhance stability of the underwater vehicle in oceanic environment. Moreover, the disturbance observer enhances the robustness performance of the adaptive fuzzy system for disturbances that cannot be modeled by fuzzy logic. The stability of closed loop controlled system is proven to be guaranteed according to Lyapunov theory. Finally, numerical simulation results illustrate the effectiveness and robustness of the proposed control method.
2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], 2014
A novel dynamic fuzzy sliding mode control (DF-SMC) algorithm is developed for heading angle cont... more A novel dynamic fuzzy sliding mode control (DF-SMC) algorithm is developed for heading angle control of autonomous underwater vehicles (AUV's) in horizontal plane. So far, the dynamics of AUV's are highly nonlinear, time varying and hydrodynamic coefficients of vehicle are difficult to be accurately estimated a prior, because of the variations of these coefficients with different operating conditions. These kinds of difficulties cause modeling inaccuracies of AUV's dynamics. Therefore, DFSM C is proposed for regulating heading angle in horizontal plane in presence of parametric uncertainty and disturbances. In this approach, two fuzzy approximators are employed in such a way that, to vary the supports of input-output fuzzy membership functions in the inference engine module. These fuzzy approximators are mainly utilized for updating width of boundary layer and hitting gain. Simulation results shows that, the reaching time and tracking error in the approaching phase can be significantly reduced with chattering problem can also be eliminated. The effectiveness of proposed control strategy and its advantages are indicated in comparison with conventional sliding mode control and fuzzy sliding mode control.
2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], 2014
In this paper, decision making scheme in cognitive radio is proposed by using fuzzy neural system... more In this paper, decision making scheme in cognitive radio is proposed by using fuzzy neural system, due to which secondary users can utilizes the spectrum effectively with seamless communication between cognitive radio and primary users. The spectrum sensing performance is enhanced by using either multiple antennas or multistage spectrum sensing. Due to multiple antennas at sensing node causes more equipment cost in spectrum sensing technique, therefore two stage spectrum sensing scheme is introduced. The proposed fuzzy neural decision making technique include two stage spectrum sensing schemes for identifying available spectrum. In first stage, three parameters such as spectrum utilization efficiency, degree of mobility and distance to the primary user of cognitive radio network are considered as inputs to fuzzy logic decision making process, while output of that process gives spectrum access decision, based on linguistic knowledge of 27 rules. Feedback neural network configuration included in second stage of spectrum sensing, which is trained with the help of generalized delta learning rule. Neural network has two input parameters such as output of fuzzy logic based spectrum sensing and desired values. The reference signal of neural network is obtain from transformation of output membership function in first stage to their mid singleton values. Simulation results shows significant improvement in sensing accuracy by exhibiting higher probability of detection.
Adaptive Behavior, 2019
Navigating, directing, and controlling autonomous underwater vehicles (AUVs) are demanding and co... more Navigating, directing, and controlling autonomous underwater vehicles (AUVs) are demanding and considered complicated compared to the autonomous surface-level performance. In such vehicles, the motion can be controlled depending on the estimation of indefinite hydrodynamic forces and the disturbances that occurs in this vehicle in the underwater background. In this article, Gray Wolf optimization (GWO) is performed along with the second-order sliding mode control (GW-SoSMC) approach for controlling the yaw angle in AUV. The main purpose of the article is to reduce the error that occurs in the system between the controlled signal and desired signal corresponding to yaw angle. Using this proposed model, both the occurrence of chattering and the controlling performance of AUV system can be diminished. Moreover, the proposed model is compared with the existing approaches like, FireFly-SoSMC (FF-SoSMC), Genetic Algorithm-SoSMC (GA-SoSMC), Gray Wolf-SMC (GW-SMC), Group search optimization...