38 research outputs found
Deep Learning Based Non-Intrusive Load Monitoring for a Three-Phase System
Non-Intrusive Load Monitoring (NILM) is a method to determine the power consumption of individual appliances from the overall power consumption measured by a single measurement device, which is usually the main meter. Increase in the adoption of smart meters has facilitated large scale implementation of NILM, which can provide information about individual loads to the utilities and consumers. This will lead to significant energy savings as well as better demand-side management. Researchers have proposed several methods and have successfully implemented NILM for residential sectors that have a single-phase supply. However, NILM has not been successfully implemented for industrial and commercial buildings that have a three-phase supply, due to several challenges. These buildings consume significant amount of power and implementing NILM to these buildings has the potential to yield substantial benefits. In this paper, we propose a novel deep learning-based approach to address some of the key challenges in implementing NILM for buildings that have a three-phase supply. Our approach introduces an ensemble learning technique that does not require training of multiple neural network models, which reduces the computational requirements and makes it economically feasible. The model was tested on a three-phase system that consists of both three- phase loads and single-phase loads. The results show significant improvement in load disaggregation compared to the existing methods and indicate its applicability
Development of intelligent collision avoidance systems
This paper introduces a new paradigm for
avoiding vehicle collisions in Intelligent Transportation
Systems. The key feature of the concept is that we
introduce interactive control of different vehicular
sub-systems ultimately leading to interdependent
maneuvering of vehicles in order to avoid collision
encounters. A general scenario of motion of vehicular
systems passing a common point at the same time is studied
here. Using a fuzzy logic-based controller, collision avoidance maneuvers
of the individual vehicular sub-systems are realized. An inter-vehicle communication
(IVC) system that facilitates interactions between the
vehicles is studied as a top tier above the individual
vehicular controllers. The concept that is adopted here is
that in order to control a general system with participatory
sub-systems of vehicles, all the influencing members have
to interactively work together in order to effectively
negotiate to achieve an optimal result in avoiding collision
encounters. While the interactive operations are being
carried out, each vehicular sub-system may act as a
‘Master’ one time and a ‘Slave’ at another time. This
switching between the states of‘Master’ and ‘Slave’ should
be done efficiently to better prevent a collision encounter.
Even though above paradigm is proved only for two
interactive sub-systems, it is emphasized that the concept
can be extended to any number of vehicular sub-systems to
realize effective collision avoidance maneuvers.Senate Research Gran
Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer
[[abstract]]Automated vehicle control systems are a key technology for intelligent vehicle highway systems (IVHSs). This paper presents an automated vehicle control algorithm for combined longitudinal and lateral motion control of highway vehicles, with special emphasis on front-wheel-steered four-wheel road vehicles. The controller is synthesized using an online neural-estimator-based control law that works in combination with a lateral velocity observer. The online adaptive neural-estimator-based design approach enables the controller to counteract for inherent model discrepancies, strong nonlinearities, and coupling effects. The neurocontrol approach can guarantee the uniform ultimate bounds (UUBs) of the tracking and observer errors and the bounds of the neural weights. The key design features are (1) inherent coupling effects will be taken into account as a result of combining of the two control issues, viz., lateral and longitudinal control;(2) rather ad hoc numerical approximations of lateral velocity will be avoided via a combined controller-observer design; and (3) closed-loop stability issues of the overall system will be established. The algorithm is validated via a formative mathematical analysis based on a Lyapunov approach and numerical simulations in the presence of parametric uncertainties as well as severe and adverse driving conditions.[[incitationindex]]SCI[[booktype]]ç´™
Neuroadaptive Combined Lateral and Longitudinal Control of Highway Vehicles Using RBF Networks
A neural network (NN) adaptive model-based combined
lateral and longitudinal vehicle control algorithm for
highway applications is presented in this paper. The controller
is synthesized using a proportional plus derivative control coupled
with an online adaptive neural module that acts as a dynamic
compensator to counteract inherent model discrepancies,
strong nonlinearities, and coupling effects. The closed-loop stability
issues of this combined control scheme are analyzed using a
Lyapunov-based method. The neurocontrol approach can guarantee
the uniform ultimate bounds of the tracking errors and
bounds of NN weights. A complex nonlinear three-degreeof-
freedom dynamic model of a passenger wagon is developed to
simulate the vehicle motion and for controller design. The controller
is tested and verified via computer simulations in the presence
of parametric uncertainties and severe driving condition
Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer
Automated vehicle control systems are a key technology
for intelligent vehicle highway systems (IVHSs). This paper
presents an automated vehicle control algorithm for combined longitudinal
and lateral motion control of highway vehicles, with special
emphasis on front-wheel-steered four-wheel road vehicles. The
controller is synthesized using an online neural-estimator-based
control law that works in combination with a lateral velocity
observer. The online adaptive neural-estimator-based design approach
enables the controller to counteract for inherent model
discrepancies, strong nonlinearities, and coupling effects. The neurocontrol
approach can guarantee the uniform ultimate bounds
(UUBs) of the tracking and observer errors and the bounds of the
neural weights. The key design features are 1) inherent coupling
effects will be taken into account as a result of combining of the
two control issues, viz., lateral and longitudinal control; 2) rather
ad hoc numerical approximations of lateral velocity will be avoided
via a combined controller–observer design; and 3) closed-loop
stability issues of the overall system will be established. The algorithm
is validated via a formative mathematical analysis based on
a Lyapunov approach and numerical simulations in the presence
of parametric uncertainties, as well as severe and adverse driving
condition
A novel active inrush suppression method for SC converter
A new type of Inrush suppression circuit in SC buck converter is proposed. The method is realized by the hybrid of passive
and active circuits. It adapts PIC micro controller to generate multi pulse to drive multi switches. Computer simulation shows
that this method suppresses inrush current drastically
A compact and high performance SC DC/DC buck converter
A novel paralleling interleaved discharging (PID) approach is presented to reduce the output ripple and continuous input current
waveform in step-down switched capacitor (SC). Theoretical analysis and the computer simulation show that PID method
can reduce output ripple by a factor of three and improve output power level by 8. 7%. The PID method can provide a large
range of constant desired values of the output voltage for a given input voltage by paralleling
Discrete-time neuroadaptive control using dynamic state feedback with application to vehicle motion control for intelligent vehicle highway systems
Discrete time neuro-compensated dynamic state feedback control system for lateral and longitudinal control of intelligent vehicle highway systems (IVHS) is developed. A discrete time counterpart of the continuous time non-linear IHVS model is obtained in state-space form and the controller is analysed in three stages, with and without compensation mechanisms resulting in an implementation from low to high complexity. Gain parameters of the dynamic state feedback control are optimised with respect to a minimisation of a linear quadratic cost function. The weight convergence of the neuro-compensation algorithm is established in discrete time Lyapunov sense via a graphical method. The performance enhancement of each design stage of the controller is presented and compared with the aid of computer simulations
Discrete-time neuroadaptive control using dynamic state feedback with application to vehicle motion control for intelligent vehicle highway systems
Discrete time neuro-compensated dynamic state feedback control system for lateral and longitudinal
control of intelligent vehicle highway systems (IVHS) is developed. A discrete time counterpart of the
continuous time non-linear IHVS model is obtained in state-space form and the controller is analysed in three
stages, with and without compensation mechanisms resulting in an implementation from low to high
complexity. Gain parameters of the dynamic state feedback control are optimised with respect to a
minimisation of a linear quadratic cost function. The weight convergence of the neuro-compensation
algorithm is established in discrete time Lyapunov sense via a graphical method. The performance
enhancement of each design stage of the controller is presented and compared with the aid of computer
simulations