Source 1primary paper2012IET Electric Power Applications
Toolkit/non-linear disturbance observer
non-linear disturbance observer
Also known as: NDO
Taxonomy: Technique Branch / Method. Workflows sit above the mechanism and technique branches rather than replacing them.
Summary
A non-linear disturbance observer (NDO) is an engineering control method used within a feedback linearisation framework to estimate lumped uncertainty. In the cited in-wheel motor drive study, it was implemented as part of a DSP-based intelligent control system for a six-phase permanent magnet synchronous motor.
Usefulness & Problems
Why this is useful
This method is useful for control architectures that require online estimation of aggregated model uncertainty or external disturbance terms during feedback linearisation. The supplied evidence supports utility in an in-wheel motor drive context, but does not provide broader biological or biochemical applications.
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 estimation of lumped uncertainty in a designed feedback linearisation controller. The cited study applies this to an in-wheel motor drive for a light electric vehicle.
Problem links
Need conditional protein clearance
DerivedA non-linear disturbance observer (NDO) is an engineering control method used to estimate lumped uncertainty within a feedback linearisation control framework. In the cited study, it was incorporated into a DSP-based controller for an in-wheel motor drive using a six-phase permanent magnet synchronous motor.
Taxonomy & Function
Primary hierarchy
Technique Branch
Method: A concrete method used to build, optimize, or evolve an engineered system.
Mechanisms
Degradationdisturbance estimationdisturbance estimationfeedback linearisationfeedback linearisationTarget processes
degradationImplementation Constraints
cofactor dependency: cofactor requirement unknownencoding mode: genetically encodedimplementation constraint: context specific validationoperating role: builder
The reported implementation used a 32-bit fixed-point DSP, specifically the TMS320F2812, to run the proposed control system. The available evidence does not specify parameterisation details, observer equations, or any biological implementation considerations.
The supplied evidence states that observer error degrades system response. No quantitative performance metrics, robustness bounds, or validation outside the reported motor-drive application are provided in the evidence.
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.
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 source2 linked approval claimsfirst-pass slug non-linear-disturbance-observer
a non-linear disturbance observer (NDO) is applied to estimate the lumped uncertainty
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:
limitationsupports
Observer error degrades system response.
However, the system response is degraded by the existed observer error.
Source:
Comparisons
Source-backed strengths
The main supported strength is explicit incorporation of disturbance estimation into the control loop through a non-linear observer. The study also demonstrates practical real-time implementation on a 32-bit fixed-point DSP (TMS320F2812).
Compared with CRISPRoff
non-linear disturbance observer and CRISPRoff address a similar problem space because they share degradation.
Shared frame: same top-level item type; shared target processes: degradation; shared mechanisms: degradation
Strengths here: looks easier to implement in practice.
Compared with nanobody-mediated proteolysis-targeting chimeras
non-linear disturbance observer and nanobody-mediated proteolysis-targeting chimeras address a similar problem space because they share degradation.
Shared frame: same top-level item type; shared target processes: degradation; shared mechanisms: degradation
Strengths here: looks easier to implement in practice.
Compared with Split-Cas9-based targeted gene editing
non-linear disturbance observer and Split-Cas9-based targeted gene editing address a similar problem space because they share degradation.
Shared frame: same top-level item type; shared target processes: degradation; shared mechanisms: degradation
Strengths here: looks easier to implement in practice.
Ranked Citations
- 1.