Aman Parkash | National Institute Of Technology Kurukshetra (original) (raw)
Papers by Aman Parkash
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
In a surface vehicle ride, the passenger’s comfort is desirable, ensuring safety under various ti... more In a surface vehicle ride, the passenger’s comfort is desirable, ensuring safety under various tight constraints. To consider this requirement, this article has proposed a lateral control for autonomous vehicle considering the lateral offset error constraints. A reduced-order dynamics of the vehicle has been considered for improving and easy implementation of backstepping. A nonlinear control has been developed by utilizing the Time-varying Asymmetric Barrier Lyapunov Function via the Active Disturbance Rejection Control (ADRC) approach. The Extended State Observer, a component of applied ADRC, estimates all unknown system states as well as the unknown disturbance. The desired trajectories (smooth and non-smooth) based on the yaw rate profile have been synthesized to test the performance of the vehicle. Simulation results are presented to show the effectiveness of the proposed closed-loop control strategy. The vehicle performance has been compared using the proposed method, the conv...
Control of Autonomous Vehicle, Presentation for Comprehensive Report (Including Conference presen... more Control of Autonomous Vehicle, Presentation for Comprehensive Report (Including Conference presentation), NIT Kurukshetra, INDIA
According to the road accident report, in this world, forty percent of deaths on the road are hap... more According to the road accident report, in this world, forty percent of deaths on the road are happened [1, WHO Report, 2018]. Improper steering wheel handling is the main reason behind these casualties. That is why the lateral support system is required to enhance safety while employing the autonomous vehicle.
The special requirement of safety is crucial with maneuvering the vehicle for achieving the lane-keeping task in complex environments. A number of controllers are available addressing this requirement.
Through this work, an attempt has been made to improve the performance of autonomous vehicle through extension/modification of existing lateral control methods.
The research work of this thesis has resulted in four proposed control methods. The proposed methods have been implemented for lateral control autonomous vehicle. Also, an extended nonlinear model based on look-ahead has been formulated.
Extensive simulation exercises have been presented. It has been observed and shown that the autonomous vehicle performance is substantially improved on the application of these proposed methods. Few issues have been discussed for further extension of present thesis research.
International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) and DOI: 10.1109/ic-ETITE47903.2020.291, 2020
This paper considers the stabilization problem of a surface autonomous vehicle and develops a con... more This paper considers the stabilization problem of a surface autonomous vehicle and develops a control method for efficient lane keeping. The proposed control law is composed of two components; first is Lyapunov stability based sliding mode control (SMC) designed for the vehicle dynamics for stable and robust operation, the other one is Input-to-State-Stability (ISS) based control designed for the lane keeping performance. It is shown that the proposed controller for nonlinear dynamics is easier due to its simple derivation. The closed-loop system performance and robustness analysis has been presented through a simulation exercise. The results show that the stability is maintained with negligible lateral deviation even in bending motion.
In a surface vehicle ride, the passenger’s comfort is desirable, ensuring safety under various ti... more In a surface vehicle ride, the passenger’s comfort is desirable, ensuring safety under various tight constraints. To consider this requirement, this article has proposed a lateral control for autonomous vehicle considering the lateral offset error constraints. A reduced-order dynamics of the vehicle has been considered for improving and easy implementation of
backstepping. A nonlinear control has been developed by utilizing the Time-varying Asymmetric Barrier Lyapunov Function via the Active Disturbance Rejection Control (ADRC) approach. The Extended State Observer, a component of applied ADRC, estimates all unknown system states as well as the unknown disturbance. The desired trajectories
(smooth and non-smooth) based on the yaw rate profile have been synthesized to test the performance of the vehicle. Simulation results are presented to show the effectiveness of the proposed closed-loop control strategy. The vehicle performance has been compared using the proposed method, the conventional Quadratic Lyapunov Function and Symmetric Barrier Lyapunov Function based control. The proposed control provides a smoother control signal, small tracking error and robustness in presence of hard road constraints for smooth/non-smooth vehicle motion.
Journal of Vibration and Control, 2022
This paper presents a control scheme for performance improvement in lane-keeping activity of auto... more This paper presents a control scheme for performance improvement in lane-keeping activity of autonomous vehicle, within guaranteed specified deviations. Such feature will provide better and safe maneuvering for autonomous vehicle. The proposed control scheme consists of Asymmetric Barrier Lyapunov Function (ABLF) based backstepping method for ensuring stability and constraints satisfaction. Since autonomous vehicle dynamics is subjected to unknown disturbances
and also the states, it has been shown that the vehicle performance can be improved by the application of Active Disturbance Rejection Control (ADRC) which uses the Extended State Observer (ESO). For the proposed control scheme, the asymptotic stability and convergence of error dynamics have been established. The simulation results have been
presented to illustrate the performance robustness and accuracy provided by the proposed control scheme. Further, it has been show that the vehicle motion performance is better in comparison to the performances obtained from two existing control methods
Computational Intelligence (CI) is a successor of artificial intelligence. CI relies on heuristi... more Computational Intelligence (CI) is a successor of artificial intelligence.
CI relies on heuristic algorithms such as in fuzzy systems, neural networks,
and evolutionary computation. In addition, computational intelligence
also embraces techniques that use Swarm intelligence, Fractals
and Chaos Theory, Artificial immune systems, Wavelets, etc. Computational
intelligence is a combination of learning, adaptation, and evolution
used to intelligent and innovative applications. Computational
intelligence research does not reject statistical methods, but often gives
a complementary view of the implementation of these methods. Computational
intelligence is closely associated with soft computing a combination
of artificial neural networks, fuzzy logic and genetic algorithms,
connectionist systems such as artificial intelligence, and cybernetics.
CI experts mainly consider the biological inspirations from nature for
implementations, but even if biology is extended to include all psychological
and evolutionary inspirations then CI includes only the neural,
fuzzy, and evolutionary algorithms. The Bayesian foundations of learning,
probabilistic and possibilistic reasoning, Markovian chains, belief
networks, and graphical theory have no biological connections. Therefore
genetic algorithms is the only solution to solve optimization problems.
CI studies problems for which there are no effective algorithms,
either because it is not possible to formulate them or because they are
complex and thus not effective in real life applications. Thus the broad
definition is given by: computational intelligence is a branch of computer
science studying problems for which there are no effective computational
algorithms. Biological organisms solve such problems every day: extracting
meaning from perception, understanding language, solving ill-defined
computational vision problems thanks to evolutionary adaptation of the
brain to the environment, surviving in a hostile environment. However,
such problems may be solved in different ways
Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
In a surface vehicle ride, the passenger’s comfort is desirable, ensuring safety under various ti... more In a surface vehicle ride, the passenger’s comfort is desirable, ensuring safety under various tight constraints. To consider this requirement, this article has proposed a lateral control for autonomous vehicle considering the lateral offset error constraints. A reduced-order dynamics of the vehicle has been considered for improving and easy implementation of backstepping. A nonlinear control has been developed by utilizing the Time-varying Asymmetric Barrier Lyapunov Function via the Active Disturbance Rejection Control (ADRC) approach. The Extended State Observer, a component of applied ADRC, estimates all unknown system states as well as the unknown disturbance. The desired trajectories (smooth and non-smooth) based on the yaw rate profile have been synthesized to test the performance of the vehicle. Simulation results are presented to show the effectiveness of the proposed closed-loop control strategy. The vehicle performance has been compared using the proposed method, the conv...
Control of Autonomous Vehicle, Presentation for Comprehensive Report (Including Conference presen... more Control of Autonomous Vehicle, Presentation for Comprehensive Report (Including Conference presentation), NIT Kurukshetra, INDIA
According to the road accident report, in this world, forty percent of deaths on the road are hap... more According to the road accident report, in this world, forty percent of deaths on the road are happened [1, WHO Report, 2018]. Improper steering wheel handling is the main reason behind these casualties. That is why the lateral support system is required to enhance safety while employing the autonomous vehicle.
The special requirement of safety is crucial with maneuvering the vehicle for achieving the lane-keeping task in complex environments. A number of controllers are available addressing this requirement.
Through this work, an attempt has been made to improve the performance of autonomous vehicle through extension/modification of existing lateral control methods.
The research work of this thesis has resulted in four proposed control methods. The proposed methods have been implemented for lateral control autonomous vehicle. Also, an extended nonlinear model based on look-ahead has been formulated.
Extensive simulation exercises have been presented. It has been observed and shown that the autonomous vehicle performance is substantially improved on the application of these proposed methods. Few issues have been discussed for further extension of present thesis research.
International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) and DOI: 10.1109/ic-ETITE47903.2020.291, 2020
This paper considers the stabilization problem of a surface autonomous vehicle and develops a con... more This paper considers the stabilization problem of a surface autonomous vehicle and develops a control method for efficient lane keeping. The proposed control law is composed of two components; first is Lyapunov stability based sliding mode control (SMC) designed for the vehicle dynamics for stable and robust operation, the other one is Input-to-State-Stability (ISS) based control designed for the lane keeping performance. It is shown that the proposed controller for nonlinear dynamics is easier due to its simple derivation. The closed-loop system performance and robustness analysis has been presented through a simulation exercise. The results show that the stability is maintained with negligible lateral deviation even in bending motion.
In a surface vehicle ride, the passenger’s comfort is desirable, ensuring safety under various ti... more In a surface vehicle ride, the passenger’s comfort is desirable, ensuring safety under various tight constraints. To consider this requirement, this article has proposed a lateral control for autonomous vehicle considering the lateral offset error constraints. A reduced-order dynamics of the vehicle has been considered for improving and easy implementation of
backstepping. A nonlinear control has been developed by utilizing the Time-varying Asymmetric Barrier Lyapunov Function via the Active Disturbance Rejection Control (ADRC) approach. The Extended State Observer, a component of applied ADRC, estimates all unknown system states as well as the unknown disturbance. The desired trajectories
(smooth and non-smooth) based on the yaw rate profile have been synthesized to test the performance of the vehicle. Simulation results are presented to show the effectiveness of the proposed closed-loop control strategy. The vehicle performance has been compared using the proposed method, the conventional Quadratic Lyapunov Function and Symmetric Barrier Lyapunov Function based control. The proposed control provides a smoother control signal, small tracking error and robustness in presence of hard road constraints for smooth/non-smooth vehicle motion.
Journal of Vibration and Control, 2022
This paper presents a control scheme for performance improvement in lane-keeping activity of auto... more This paper presents a control scheme for performance improvement in lane-keeping activity of autonomous vehicle, within guaranteed specified deviations. Such feature will provide better and safe maneuvering for autonomous vehicle. The proposed control scheme consists of Asymmetric Barrier Lyapunov Function (ABLF) based backstepping method for ensuring stability and constraints satisfaction. Since autonomous vehicle dynamics is subjected to unknown disturbances
and also the states, it has been shown that the vehicle performance can be improved by the application of Active Disturbance Rejection Control (ADRC) which uses the Extended State Observer (ESO). For the proposed control scheme, the asymptotic stability and convergence of error dynamics have been established. The simulation results have been
presented to illustrate the performance robustness and accuracy provided by the proposed control scheme. Further, it has been show that the vehicle motion performance is better in comparison to the performances obtained from two existing control methods
Computational Intelligence (CI) is a successor of artificial intelligence. CI relies on heuristi... more Computational Intelligence (CI) is a successor of artificial intelligence.
CI relies on heuristic algorithms such as in fuzzy systems, neural networks,
and evolutionary computation. In addition, computational intelligence
also embraces techniques that use Swarm intelligence, Fractals
and Chaos Theory, Artificial immune systems, Wavelets, etc. Computational
intelligence is a combination of learning, adaptation, and evolution
used to intelligent and innovative applications. Computational
intelligence research does not reject statistical methods, but often gives
a complementary view of the implementation of these methods. Computational
intelligence is closely associated with soft computing a combination
of artificial neural networks, fuzzy logic and genetic algorithms,
connectionist systems such as artificial intelligence, and cybernetics.
CI experts mainly consider the biological inspirations from nature for
implementations, but even if biology is extended to include all psychological
and evolutionary inspirations then CI includes only the neural,
fuzzy, and evolutionary algorithms. The Bayesian foundations of learning,
probabilistic and possibilistic reasoning, Markovian chains, belief
networks, and graphical theory have no biological connections. Therefore
genetic algorithms is the only solution to solve optimization problems.
CI studies problems for which there are no effective algorithms,
either because it is not possible to formulate them or because they are
complex and thus not effective in real life applications. Thus the broad
definition is given by: computational intelligence is a branch of computer
science studying problems for which there are no effective computational
algorithms. Biological organisms solve such problems every day: extracting
meaning from perception, understanding language, solving ill-defined
computational vision problems thanks to evolutionary adaptation of the
brain to the environment, surviving in a hostile environment. However,
such problems may be solved in different ways