Robust adaptive backstepping fast terminal sliding mode controller for uncertain quadrotor UAV

In this article, we investigate the neural adaptive quantized control problem for microelectromechanical system (MEMS) gyroscope with full-state constraints and lumped disturbances. With two different kinds of one-to-one nonlinear mappings, the traditional gyroscope model with matched disturbances is transformed into an unconstrained one with both unmatched and matched disturbances, thus the predefined time-varying state constraints imposed on MEMS gyroscope can be achieved. To compensate for the lumped disturbances, a state estimator-based minimal learning parameter neural network is proposed to obtain fast and smooth disturbance estimates for both position and velocity control loops, which not only can eliminate the poor transient behaviors that widely appear in the available neural adaptive control with a large adaptive gain, but also greatly reduce the number of leaning parameters updated online. Furthermore, by employing a hysteresis logarithmic quantizer, the neglected difficulty, named as constrained data bandwidth of actuator can be overcome with less chattering in control signal, which is more convenient to implement. Finally, the neural adaptive control for MEMS gyroscope is developed such that satisfactory tracking performance is achieved despite of large disturbances, full-state constraints as well as quantized input. The effectiveness and advantages of the proposed control method are demonstrated through extensive simulations.

Neural Adaptive Control for MEMS Gyroscope With Full-State Constraints and Quantized Input

Observer-based H∞ fault-tolerant attitude control for satellite with actuator and sensor faults

Anti-saturation adaptive finite-time neural network based fault-tolerant tracking control for a quadrotor UAV with external disturbances

Fuzzy wavelet neural control with improved prescribed performance for MEMS gyroscope subject to input quantization

High-order ESO based output feedback dynamic surface control for quadrotors under position constraints and uncertainties

https://www.researchsquare.com/article/rs-1360661/latest.pdf

Optimization of redundant degree of freedom in robotic milling considering chatter stability

Indirect Measurement Method of Ultrasonic Bone Cutting Force Based on Anti- node Vibration Displacement

Analytical prediction of cogging torque needs accurate flux density and relative permeance closed-form expressions. The accurate two-dimensional (2D) air gap flux density distribution function and the 2D permeance function are currently almost always applied to surface-mounted permanent magnet (SPM) machines, instead of interior permanent magnet (IPM) machines, due to complications from IPMs severe magnetic saturation and leakage flux. To address these issues, this paper proposes a set of new methods to derive the accurate closed-form 2D expressions of IPMs for both flux density and relative permeance. As for the flux density 2D model, a virtual equivalence model for IPM is introduced, so that Laplace’s equation and quasi-Poisson equation can be directly applied to IPM. As for permeance, the same virtual equivalence model also enables 2D models derivation for IPM. Subsequently, the resulting cogging torque analytical expression is obtained with the accurate relative permeance and air gap flux density models. The results from the proposed 2D analytical models showed similar accuracy to the finite element analysis (FEA). In addition, as demonstrated, the proposed 2D analytical models is a highly efficient tool set in the design process of cogging torque optimization, facilitating fast evaluation of different design factors.

/pdf/two-dimensional-analytical-models-for-cogging-torque-2i2aww9i.pdf

Two-Dimensional Analytical Models for Cogging Torque Prediction in Interior Permanent Magnet Machine

The cogging torque reduction methods in permanent magnet (PM) machines are more or less FEA trial and error-based and cause undesirable side effects, such as distorted back EMF, new harmonic components, and structure asymmetry. A slot-opening grouping method can address these issues. However, it needs to model all slots in FEA design validation during practical cogging torque optimization iterations, and also leads to back-EMF reduction. In this paper, a new grouping strategy, termed in-phase unit (IPU), is introduced, together with the slot-opening angle-shift method. The slot openings with the same cogging torque distribution pattern are grouped into an IPU, and the cogging torque peaks of the slot openings within an IPU can now be interleaved. Thereby, the major harmonic components of the cogging torque are cancelled out, instead of being summed up. The IPU grouping principle, the slot-opening shift angle computation, and the selection of the harmonic order to cancel are analyzed in detail. By comparison, the proposed method not only simplifies cogging torque optimization iterations by only modeling the slots in one IPU in FEA, but also compensates for the back-EMF constant reduction. The effectiveness of the proposed methods is validated by two typical interior PM machine design cases.

/pdf/an-in-phase-unit-slot-opening-shift-method-for-cogging-3imuoq07.pdf

Linwei Wang

Papers

High-order ESO based output feedback dynamic surface control for quadrotors under position constraints and uncertainties

Optimization of redundant degree of freedom in robotic milling considering chatter stability

Indirect Measurement Method of Ultrasonic Bone Cutting Force Based on Anti- node Vibration Displacement

Two-Dimensional Analytical Models for Cogging Torque Prediction in Interior Permanent Magnet Machine

An In-Phase Unit Slot-Opening Shift Method for Cogging Torque Reduction in Interior Permanent Magnet Machine