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Ground reaction force (GRF) characteristics of amputee walking are important for the analysis of clinical gait data, and also to update model reference adaptive impedance (MRAI) controllers. GRF estimation is a better alternative than direct GRF measurement because of the disadvantages of load cells, such as high cost, integration difficulties due to weight and physical dimensions, the possibility of overload, and measurement noise. This paper presents four robust MRAI observer/controller combinations for GRF estimation-based control of a prosthesis and a legged robot model in the presence of parametric uncertainties and unmodeled dynamics, in which the robot model is employed to mimic able-bodied walking. Since unknown GRFs can reduce the performance of prosthesis control systems, the proposed MRAI controllers are designed to compensate their effects. All four proposed observer/controller combinations enable the system to imitate the biomechanics of able-bodied walking as defined by mechanical impedance, and provide flexible and smooth gait. Lyapunov analysis of the observer/controller combinations is used to prove stability. The four MRAI controllers are compared with each other from the perspective of GRF estimation accuracy, prosthetic knee angle tracking accuracy, parameter estimation accuracy, control effort, and computational effort. Simulation results show that all four MRAI controllers achieve good tracking performance, stable limit cycles, and accurate GRF estimation. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology, employing a super-twisting sliding mode observer, to reconstruct un-measureable atomic-forces at nano-Newton precision in a Vertically Oriented Probe Microscope (VOPM). The VOPM senses the deflection of a vertically oriented cantilever, caused by shear-force interaction with a confined water layer above the sample-substrate. The paper describes the development of a model and the subsequent experimental process for computing its parameters. This forms the basis for the design of a super-twisting observer to estimate the unknown shear-forces. The reconstructed force can be decomposed into elastic and viscous components, which are important in biological research. [ABSTRACT FROM AUTHOR]

The transverse dynamic force microscope (TDFM) and its shear force sensing principle permit true noncontact force detection in contrast to typical atomic force microscopes. The two TDFM measurement signals for the cantilever allow, in principle, two different scanning modes of which, in particular, the second presented here permits a full-scale noncontact scan. Previous research work mainly focused on developing the sensing mechanism, whereas this paper investigates the vertical axis dynamics for advanced robust closed-loop control. This paper presents a new TDFM digital control solution, built on field-programmable gate array equipment running at high implementation frequencies. The integrated control system allows the implementation of online customizable controllers, and raster scans in two modes at very high detection bandwidth and nanoprecision. Robust control algorithms are designed, implemented, and practically assessed. The two realized scanning modes are experimentally evaluated by imaging nanospheres with known dimensions in wet conditions. [ABSTRACT FROM AUTHOR]

Food-borne diseases associated with fresh produce consistently cause serious public health issues. Although sanitization measures are utilized to enhance the safety of fresh produce, strategies that neglect the dynamic nature of commercial wash processes are limited, creating the potential for pathogen cross-contamination and major disease outbreaks. In light of this risk, there is an urgent need for new control approaches during produce washing to reduce the probability of outbreaks. As an important step in this direction, a hybrid extended Kalman filter (HEKF) and particle swarm optimization (PSO)-based noise statistics optimization are designed for a produce wash system. The HEKF uses discrete-time free chlorine (FC) measurements, and PSO is used to optimize the noise statistics of the process noise model. The process model and HEKF enable the estimation of chemical oxygen demand (COD) in the water wash, FC concentration, Escherichia coli concentration (PC) in the water wash, and E. coli level (P) on the lettuce. Although control is not explicitly addressed in this paper, the estimation technique proposed here will enable not only monitoring but also advanced control methods. The HEKF is applied to estimate E. coli O157:H7 contamination of shredded lettuce during an industrial wash. The HEKF estimates COD with a root mean square error (RSME) of 8.24 mg/L, FC concentration with an RMSE of 0.09 mg/L, PC in the wash water with an RMSE of 0.19 MPN/ml, and P on the lettuce with an RSME of 0.04 MPN/g. A sensitivity analysis demonstrates that the estimator has good robustness. [ABSTRACT FROM AUTHOR]