Air flow rate and pressure control approach for the air supply subsystems in PEMFCs.

Simultaneous control of the air flow rate and the cathode pressure plays an essential role in enhancing the performance and prolonging the service life of a polymer electrolyte membrane fuel cell (PEMFC). However, this is challenging to implement in practice due to the high complexity and coupled dynamics of the air supply subsystem in the PEMFC. This work, based on a simplified control-oriented model, puts forward a disturbance-observer-based multi-output feedback control strategy to address the issue of synergistically controlling the air flow rate and cathode pressure.

Improved AET Robust Control for Networked T-S Fuzzy Systems With Asynchronous Constraints.

This article proposes a novel improved adaptive event-triggered (AET) control algorithm for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous constraints. First, taking the limited bandwidth of the network into consideration, an improved AET mechanism is proposed to save the communication resource. Superior to the existing event-triggered mechanism, the improved AET scheme introduces two adjusting parameters, which further contribute to the economization of the communication resource.

Analysis of measurement electrode location in bladder urine monitoring using electrical impedance.

Purpose: The aim of this study was to document more appropriate electrode location of a four-electrode-based electrical impedance technology in the monitoring of bladder filling, and to characterize the relationship between bladder filling duration and the measured electrical impedances.

Methods: A simulation study, based on a 2-dimension computational model, was conducted to determine the preferable locations of excitation and measurement electrodes in a conventional four-electrode setup. A human observation study was subsequently performed on eight healthy volunteers during natural bladder urine accumulation to validate the result of the simulation study.

Probabilistic Guaranteed Gradient Learning-Based Spark Advance Self-Optimizing Control for Spark-Ignited Engines.

In spark-ignited (SI) engines, the spark advance (SA) controls the combustion phase that has a significant impact on the efficiency. Online self-optimizing control (SOC) of SA to maximize the indicated fuel conversion efficiency (IFCE) forms a stochastic optimization problem for a static map due to the stochasticity of combustion. Gradient-based optimization algorithms using periodic dithers are effective methods of dealing with such problems.

Identification of look-up tables using gradient algorithm.

In view of the relatively low computational load, look-up tables (or maps) are usually used to approximate nonlinear function or characterize operating-point-dependent system variables in typical embedded applications. Aiming at the problem of off-line identifying the look-up tables, a method based on the gradient algorithm is presented to estimate the look-up table parameters in this paper. The nonlinear function is approximated in terms of the piecewise linear interpolation model with the look-up table parameters, which can be rewritten as a dot product between the regression vector and unknown parameter vector using membership function.

Experimental comparisons of hypothesis test and moving average based combustion phase controllers.

For engine control, combustion phase is the most effective and direct parameter to improve fuel efficiency. In this paper, the statistical control strategy based on hypothesis test criterion is discussed. Taking location of peak pressure (LPP) as combustion phase indicator, the statistical model of LPP is first proposed, and then the controller design method is discussed on the basis of both Z and T tests.

Movement Performance of Human-Robot Cooperation Control Based on EMG-Driven Hill-Type and Proportional Models for an Ankle Power-Assist Exoskeleton Robot.

Although the merits of electromyography (EMG)-based control of powered assistive systems have been certified, the factors that affect the performance of EMG-based human-robot cooperation, which are very important, have received little attention. This study investigates whether a more physiologically appropriate model could improve the performance of human-robot cooperation control for an ankle power-assist exoskeleton robot. To achieve the goal, an EMG-driven Hill-type neuromusculoskeletal model (HNM) and a linear proportional model (LPM) were developed and calibrated through maximum isometric voluntary dorsiflexion (MIVD).