Source 1primary paper2012IET Electric Power Applications
Toolkit/probabilistic fuzzy neural network control
probabilistic fuzzy neural network control
Also known as: PFNN control
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Probabilistic fuzzy neural network control is a digital signal processor-based control method proposed for an in-wheel motor drive in a light electric vehicle. In the cited study, it is implemented for a six-phase permanent magnet synchronous motor drive and combines probabilistic fuzzy neural network control with feedback linearisation and disturbance observation.
Usefulness & Problems
Why this is useful
This method is useful for controlling a non-linear in-wheel motor drive while estimating lumped uncertainty through an observer. The cited work positions it as an intelligent controlled drive system implemented on embedded DSP hardware for electric vehicle motor control.
Source:
Moreover, a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty for the designed feedback linearisation control.
Problem solved
The specific problem addressed is control of an in-wheel motor drive subject to lumped uncertainty in a six-phase permanent magnet synchronous motor system. The design uses a non-linear disturbance observer to support feedback linearisation by estimating that uncertainty.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete method used to build, optimize, or evolve an engineered system.
Mechanisms
disturbance observationfeedback linearisationprobabilistic fuzzy neural network controluncertainty estimationTechniques
No technique tags yet.
Target processes
No target processes tagged yet.
Input: Light
Implementation Constraints
Implementation was reported on a 32-bit fixed-point DSP TMS320F2812 as part of the proposed intelligent controlled drive system. The available evidence supports use in an in-wheel motor drive for a light electric vehicle, but does not provide construct-like design parameters, tuning details, or any optical implementation despite the listed input modality.
The cited limitation is that observer error degrades system response. Beyond this statement and the single reported application context, the evidence does not provide broader benchmarking, biological relevance, or independent validation.
Validation
Cell-freeBacteriaMammalianMouseHumanTherapeuticIndep. Replication
Supporting Sources
Ranked Claims
A non-linear disturbance observer is applied to estimate lumped uncertainty for the designed feedback linearisation control.
Moreover, a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty for the designed feedback linearisation control.
A non-linear disturbance observer is applied to estimate lumped uncertainty for the designed feedback linearisation control.
Moreover, a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty for the designed feedback linearisation control.
A non-linear disturbance observer is applied to estimate lumped uncertainty for the designed feedback linearisation control.
Moreover, a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty for the designed feedback linearisation control.
A non-linear disturbance observer is applied to estimate lumped uncertainty for the designed feedback linearisation control.
Moreover, a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty for the designed feedback linearisation control.
A non-linear disturbance observer is applied to estimate lumped uncertainty for the designed feedback linearisation control.
Moreover, a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty for the designed feedback linearisation control.
A non-linear disturbance observer is applied to estimate lumped uncertainty for the designed feedback linearisation control.
Moreover, a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty for the designed feedback linearisation control.
A non-linear disturbance observer is applied to estimate lumped uncertainty for the designed feedback linearisation control.
Moreover, a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty for the designed feedback linearisation control.
A 32-bit fixed-point DSP TMS320F2812 is adopted to implement the proposed intelligent controlled drive system.
Moreover, a 32-bit fixed-point DSP, TMS320F2812, is adopted for the implementation of the proposed intelligent controlled drive system.
A 32-bit fixed-point DSP TMS320F2812 is adopted to implement the proposed intelligent controlled drive system.
Moreover, a 32-bit fixed-point DSP, TMS320F2812, is adopted for the implementation of the proposed intelligent controlled drive system.
A 32-bit fixed-point DSP TMS320F2812 is adopted to implement the proposed intelligent controlled drive system.
Moreover, a 32-bit fixed-point DSP, TMS320F2812, is adopted for the implementation of the proposed intelligent controlled drive system.
A 32-bit fixed-point DSP TMS320F2812 is adopted to implement the proposed intelligent controlled drive system.
Moreover, a 32-bit fixed-point DSP, TMS320F2812, is adopted for the implementation of the proposed intelligent controlled drive system.
A 32-bit fixed-point DSP TMS320F2812 is adopted to implement the proposed intelligent controlled drive system.
Moreover, a 32-bit fixed-point DSP, TMS320F2812, is adopted for the implementation of the proposed intelligent controlled drive system.
A 32-bit fixed-point DSP TMS320F2812 is adopted to implement the proposed intelligent controlled drive system.
Moreover, a 32-bit fixed-point DSP, TMS320F2812, is adopted for the implementation of the proposed intelligent controlled drive system.
A 32-bit fixed-point DSP TMS320F2812 is adopted to implement the proposed intelligent controlled drive system.
Moreover, a 32-bit fixed-point DSP, TMS320F2812, is adopted for the implementation of the proposed intelligent controlled drive system.
Observer error degrades system response.
However, the system response is degraded by the existed observer error.
Observer error degrades system response.
However, the system response is degraded by the existed observer error.
Observer error degrades system response.
However, the system response is degraded by the existed observer error.
Observer error degrades system response.
However, the system response is degraded by the existed observer error.
Observer error degrades system response.
However, the system response is degraded by the existed observer error.
Observer error degrades system response.
However, the system response is degraded by the existed observer error.
Observer error degrades system response.
However, the system response is degraded by the existed observer error.
The study proposes a DSP-based probabilistic fuzzy neural network control for controlling an in-wheel motor drive using a six-phase permanent magnet synchronous motor for a light electric vehicle.
A digital signal processor (DSP)-based probabilistic fuzzy neural network (PFNN) control is proposed in this study to control an in-wheel motor drive using a six-phase permanent magnet synchronous motor (PMSM) for light electric vehicle (LEV).
The study proposes a DSP-based probabilistic fuzzy neural network control for controlling an in-wheel motor drive using a six-phase permanent magnet synchronous motor for a light electric vehicle.
A digital signal processor (DSP)-based probabilistic fuzzy neural network (PFNN) control is proposed in this study to control an in-wheel motor drive using a six-phase permanent magnet synchronous motor (PMSM) for light electric vehicle (LEV).
The study proposes a DSP-based probabilistic fuzzy neural network control for controlling an in-wheel motor drive using a six-phase permanent magnet synchronous motor for a light electric vehicle.
A digital signal processor (DSP)-based probabilistic fuzzy neural network (PFNN) control is proposed in this study to control an in-wheel motor drive using a six-phase permanent magnet synchronous motor (PMSM) for light electric vehicle (LEV).
The study proposes a DSP-based probabilistic fuzzy neural network control for controlling an in-wheel motor drive using a six-phase permanent magnet synchronous motor for a light electric vehicle.
A digital signal processor (DSP)-based probabilistic fuzzy neural network (PFNN) control is proposed in this study to control an in-wheel motor drive using a six-phase permanent magnet synchronous motor (PMSM) for light electric vehicle (LEV).
The study proposes a DSP-based probabilistic fuzzy neural network control for controlling an in-wheel motor drive using a six-phase permanent magnet synchronous motor for a light electric vehicle.
A digital signal processor (DSP)-based probabilistic fuzzy neural network (PFNN) control is proposed in this study to control an in-wheel motor drive using a six-phase permanent magnet synchronous motor (PMSM) for light electric vehicle (LEV).
The study proposes a DSP-based probabilistic fuzzy neural network control for controlling an in-wheel motor drive using a six-phase permanent magnet synchronous motor for a light electric vehicle.
A digital signal processor (DSP)-based probabilistic fuzzy neural network (PFNN) control is proposed in this study to control an in-wheel motor drive using a six-phase permanent magnet synchronous motor (PMSM) for light electric vehicle (LEV).
The study proposes a DSP-based probabilistic fuzzy neural network control for controlling an in-wheel motor drive using a six-phase permanent magnet synchronous motor for a light electric vehicle.
A digital signal processor (DSP)-based probabilistic fuzzy neural network (PFNN) control is proposed in this study to control an in-wheel motor drive using a six-phase permanent magnet synchronous motor (PMSM) for light electric vehicle (LEV).
Experimental results are presented to show the validity of the proposed PFNN control for the in-wheel motor drive system.
Finally, some experimental results are illustrated to show the validity of the proposed PFNN control for in-wheel motor drive system.
Experimental results are presented to show the validity of the proposed PFNN control for the in-wheel motor drive system.
Finally, some experimental results are illustrated to show the validity of the proposed PFNN control for in-wheel motor drive system.
Experimental results are presented to show the validity of the proposed PFNN control for the in-wheel motor drive system.
Finally, some experimental results are illustrated to show the validity of the proposed PFNN control for in-wheel motor drive system.
Experimental results are presented to show the validity of the proposed PFNN control for the in-wheel motor drive system.
Finally, some experimental results are illustrated to show the validity of the proposed PFNN control for in-wheel motor drive system.
Experimental results are presented to show the validity of the proposed PFNN control for the in-wheel motor drive system.
Finally, some experimental results are illustrated to show the validity of the proposed PFNN control for in-wheel motor drive system.
Experimental results are presented to show the validity of the proposed PFNN control for the in-wheel motor drive system.
Finally, some experimental results are illustrated to show the validity of the proposed PFNN control for in-wheel motor drive system.
Experimental results are presented to show the validity of the proposed PFNN control for the in-wheel motor drive system.
Finally, some experimental results are illustrated to show the validity of the proposed PFNN control for in-wheel motor drive system.
Approval Evidence
1 source3 linked approval claimsfirst-pass slug probabilistic-fuzzy-neural-network-control
A digital signal processor (DSP)-based probabilistic fuzzy neural network (PFNN) control is proposed in this study to control an in-wheel motor drive
Source:
implementationsupports
A 32-bit fixed-point DSP TMS320F2812 is adopted to implement the proposed intelligent controlled drive system.
Moreover, a 32-bit fixed-point DSP, TMS320F2812, is adopted for the implementation of the proposed intelligent controlled drive system.
Source:
proposalsupports
The study proposes a DSP-based probabilistic fuzzy neural network control for controlling an in-wheel motor drive using a six-phase permanent magnet synchronous motor for a light electric vehicle.
A digital signal processor (DSP)-based probabilistic fuzzy neural network (PFNN) control is proposed in this study to control an in-wheel motor drive using a six-phase permanent magnet synchronous motor (PMSM) for light electric vehicle (LEV).
Source:
validity statementsupports
Experimental results are presented to show the validity of the proposed PFNN control for the in-wheel motor drive system.
Finally, some experimental results are illustrated to show the validity of the proposed PFNN control for in-wheel motor drive system.
Source:
Comparisons
Source-backed strengths
The method was implemented on a 32-bit fixed-point DSP (TMS320F2812), indicating feasibility on embedded real-time control hardware. The available evidence also supports integration of uncertainty estimation with the control law through a non-linear disturbance observer.
Ranked Citations
- 1.