Neural Moving Horizon Estimation for Robust Flight Control

Estimating and reacting to disturbances is crucial for robust flight control of quadrotors. Existing estimators typically require significant tuning for a specific flight scenario or training with extensive ground-truth disturbance data to achieve satisfactory performance. In this paper, we propose a neural moving horizon estimator (NeuroMHE) that can automatically tune the key parameters modeled by a neural network and adapt to different flight scenarios. We achieve this by deriving the analytical gradients of the MHE estimates with respect to the weighting matrices, which enables a seamless embedding of the MHE as a learnable layer into neural networks for highly effective learning. Interestingly, we show that the gradients can be computed efficiently using a Kalman filter in a recursive form. Moreover, we develop a model-based policy gradient algorithm to train NeuroMHE directly from the quadrotor trajectory tracking error without needing the ground-truth disturbance data. The effectiveness of NeuroMHE is verified extensively via both simulations and physical experiments on quadrotors in various challenging flights. Notably, NeuroMHE outperforms a state-of-the-art neural network-based estimator, reducing force estimation errors by up to 76.7%, while using a portable neural network that has only 7.7% of the learnable parameters of the latter. The proposed method is general and can be applied to robust adaptive control of other robotic systems

Trust-Region Neural Moving Horizon Estimation for Robots

Accurate disturbance estimation is essential for safe robot operations. The recently proposed neural moving horizon estimation (NeuroMHE), which uses a portable neural network to model the MHE's weightings, has shown promise in further pushing the accuracy and efficiency boundary. Currently, NeuroMHE is trained through gradient descent, with its gradient computed recursively using a Kalman filter. This paper proposes a trust-region policy optimization method for training NeuroMHE. We achieve this by providing the second-order derivatives of MHE, referred to as the MHE Hessian. Remarkably, we show that much of computation already used to obtain the gradient, especially the Kalman filter, can be efficiently reused to compute the MHE Hessian. This offers linear computational complexity relative to the MHE horizon. As a case study, we evaluate the proposed trust region NeuroMHE on real quadrotor flight data for disturbance estimation. Our approach demonstrates highly efficient training in under 5 min using only 100 data points. It outperforms a state-of-the-art neural estimator by up to 68.1% in force estimation accuracy, utilizing only 1.4% of its network parameters. Furthermore, our method showcases enhanced robustness to network initialization compared to the gradient descent counterpart.Comment: This paper (not the final version) has been accepted for presentation at the ICRA202

Stress–Strain Strength Characteristics of Undisturbed Granite Residual Soil Considering Different Patterns of Variation of Mean Effective Stress

Granite residual soil is one of the most frequently encountered problem soils in tropical regions, whose mechanical behavior heavily depends on the pattern of variation of mean effective stress (p’) during shearing, which can be classified into three categories: increasing-p’, constant-p’, and decreasing-p’. Unfortunately, so far, the stress–strain strength characteristics of granite residual soils have been studied mainly under increasing-p’ stress paths, although it is very likely to encounter stress paths with decreasing p’ in practice, especially in excavation engineering. Moreover, most pertinent research has focused on remolded granite residual soils, whereas undisturbed specimens have not yet received enough attention. In this paper, stress path triaxial tests considering different patterns of variation of mean effective stress were conducted on an undisturbed granite residual soil. Subsequently, a variable termed loading angle was introduced to quantitatively represent stress path. The influences of stress path on the Mohr–Coulomb strength parameters, deformation characteristics, ductility, and shearing stiffness were analyzed, with an emphasis on the role of pattern of variation of mean effective stress. The experimental results show that friction angle of the soil increases while cohesion decreases with the increase in loading angle. The increase in loading angle leads to less volume contraction and smaller failure strain. During shearing, the soil exhibited a less brittle response under stress paths with smaller loading angles. The initial secant shear modulus first decreased and then increased as the loading angle increased, with the minimum shearing stiffness occurring at a certain loading angle lying between 90° and 123.7°

Impact Analysis of Geometric Characteristics and Boundary Conditions on the Stiffness of Sheet Metal Parts

This article discusses the traditional stiffness characterization of sheet metal, examined the impact sensitivity of general cover panel geometry to the sheet metal stiffness, and determines the main factors affecting it. The sheet metal stiffness was characterized using both material and geometry properties. Extensive study was conducted on the effects of boundary constraints to the sheet metal stiffness, along with analysis of same range center stiffness variations due to different size boundaries and positions. These research results showed that for automotive body panels, internal bulge or dent geometric feature is the most sensitive factor affecting its stiffness, as long as its height is within the range of (10 ~ 25) mm. Under unilateral constraint conditions, the sheet metal stiffness exhibits a logarithmic relationship with respect to material and geometry properties; under bilateral constraint conditions it’s linear. The stiffness in the same size range is basically the same even when the boundary dimensions are different. Within the same sheet metal, the stiffness in the same size range is the minimum around the edges, and basically is the same across the internal regions. This research work provided significant insights and guidance to the optimization of the main body section design, as well as the improvement on the assembly precision of body panels. DOI: http://dx.doi.org/10.5755/j01.ms.20.4.5955</p

The crystal structure of 1-propyl-2-nitro-imidazole oxide, C6H9N3O3

C6H9N3O3, monoclinic, P21/n (no. 14), a = 9.5584(4) Å, b = 6.7828(3) Å, c = 12.8337(6) Å, β = 103.8810(10)∘, V = 807.74(6) Å3, Z = 4, Rgt(F) = 0.0455, wRref(F2) = 0.1166, T = 170.0 K

Efficiency enhancement of flexible OLEDs by using nano-corrugated substrates and conformal Ag transparent anodes

In flexible OLEDs (FOLEDs), the traditional ITO anode has disadvantages such as refractive-index mismatches among substrate and other functional layers, leads to light loss of nearly 80%, meanwhile, its brittle nature and lack in raw materials hinder its further applications. We investigated an efficient FOLED using a semi-transparent silver (Ag) anode, whereas the device was built on a nano-corrugated flexible polycarbonate (PC) substrate prepared by thermal nanoimprint lithography. The corrugations were well preserved on each layer of the device, both the micro-cavity effect and surface plasmon polariton (SPP) modes of light loss were effectively suppressed. As a result, the current efficiency of the FOLED using a conformal corrugated Ag anode enhanced by 100% compared with a planar Ag anode device, and enhanced by 13% with conventional ITO device. In addition, owing to the quasi-periodical arrangements of the corrugations, the device achieved broad spectra and Lambertian angular emission. The Ag anode significantly improved the bending properties of the OLED as compared to the conventional ITO device, leading to a longer lifetime in practical use. The proposed manufacturing strategy will be useful for fabricating nano corrugations on plastic substrate of FOLED in a cost-effective and convenient manner