Source 1primary paper2012IET Electric Power Applications
Toolkit/feedback linearisation control
feedback linearisation control
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
Feedback linearisation control is an engineering control method designed to regulate an in-wheel motor drive system. In the cited study, it is implemented within a DSP-based control architecture for a light electric vehicle drive using a six-phase permanent magnet synchronous motor.
Usefulness & Problems
Why this is useful
This method is useful for controlling a non-linear in-wheel motor drive system in a light electric vehicle context. The cited work also pairs it with a non-linear disturbance observer to estimate lumped uncertainty, indicating utility for control under model uncertainty.
Source:
Moreover, a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty for the designed feedback linearisation control.
Problem solved
It addresses the problem of controlling a non-linear in-wheel motor drive system. The source specifically frames it as part of a control solution for a six-phase permanent magnet synchronous motor drive in a light electric vehicle.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete method used to build, optimize, or evolve an engineered system.
Mechanisms
disturbance observationdisturbance observationfeedback linearizationfeedback linearizationTarget processes
No target processes tagged yet.
Implementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: builder
The proposed intelligent controlled drive system was implemented on a 32-bit fixed-point DSP TMS320F2812. The available evidence places the method in an in-wheel motor drive for a light electric vehicle and indicates integration with a non-linear disturbance observer, but it does not provide further construct or tuning details.
The cited source states that observer error degrades system response. Beyond this point, the provided evidence does not report quantitative performance metrics, comparative benchmarks, or validation across multiple platforms.
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 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 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.
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.
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).
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.
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 source1 linked approval claimfirst-pass slug feedback-linearisation-control
a feedback linearisation control is designed to control the in-wheel motor drive system
Source:
functionsupports
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.
Source:
Comparisons
Source-backed strengths
The available evidence shows that the method was implemented in a real-time digital control system using a 32-bit fixed-point DSP TMS320F2812. The study also incorporates a non-linear disturbance observer for lumped uncertainty estimation, supporting its use in uncertain drive-system conditions.
Compared with CoTV
feedback linearisation control and CoTV address a similar problem space.
Shared frame: same top-level item type
Strengths here: looks easier to implement in practice.
Compared with light-dependent protein (un)folding reactions
feedback linearisation control and light-dependent protein (un)folding reactions address a similar problem space.
Shared frame: same top-level item type
Strengths here: looks easier to implement in practice.
Compared with probabilistic fuzzy neural network control
feedback linearisation control and probabilistic fuzzy neural network control address a similar problem space.
Shared frame: same top-level item type; shared mechanisms: disturbance observation
Strengths here: looks easier to implement in practice.
Ranked Citations
- 1.