Hysteresis is a nonlinear characteristic ubiquitously exhibited by smart material sensors and actuators, such as piezoelectric actuators and shape memory alloys. The Prandtl–Ishlinskii (PI) operator is widely used to describe hysteresis of smart material systems due to its simple structure and the existence of analytical inverse. A PI operator consists of a weighted superposition of play (backlash) operators. While adaptive estimation of the weights for PI operators has been reported in the literature, rigorous analysis of parameter convergence is lacking. In this article, we establish persistent excitation and thus parameter convergence for adaptive weight estimation under a rather modest condition on the input to the PI operator. The analysis is further supported via simulation, where a recursive least square (RLS) method is adopted for parameter estimation.

This paper proposes an improved model of height profile for drop-on-demand printing of ultraviolet curable ink. Unlike previous model, the proposed model propagates volume and covered area based on height difference between adjacent drops. Height profile is then calculated from the propagated volume and area. Measurements of two-drop and three-drop patterns are used to experimentally compute model parameters. The parameters are used to predict and validate height profiles of four and more drops in a straight line. Using the same root-mean-square (RMS) error as benchmark, this model achieves 5.9% RMS height profile error on four-drop lines. This represents more than 60% reduction from graph-based model and an improvement from our previous effort.

The demand for clean, sustainable, and cost-effective energy continues to increase due to global population growth and the corresponding use of consumer products. The provision of heat to a thermoacoustic prime mover results in the generation of an acoustic wave that can be converted into electrical power. Thermoacoustic devices offer highly reliable and transportable power generation with low environmental impact using a variety of fuel sources. This paper focuses on the design and testing of a single-stage, traveling-wave, thermoacoustic engine. The system configuration, component design, and integration of sensors will be described. Performance testing and system analysis show that for a 300 W heat source, the thermoacoustic machine generates a 54 Hz acoustic wave with a thermal efficiency of 7.8%. The system’s acoustic power output may be increased by 84% through improved heat exchanger design. Tuning of the acoustic system and optimization of the bi-directional turbine merit attention to realize an applicable waste heat energy harvesting system.

In this article, a compressive sensing-based reconstruction algorithm is applied to data acquired from a nodding multibeam Lidar system following a Lissajous-like trajectory. Multibeam Lidar systems provide 3D depth information of the environment, but the vertical resolution of these devices may be insufficient in many applications. To mitigate this issue, the Lidar can be nodded to obtain higher vertical resolution at the cost of increased scan time. Using Lissajous-like nodding trajectories allows for the trade-off between scan time and horizontal and vertical resolutions through the choice of scan parameters. These patterns also naturally subsample the imaged area. In this article, a compressive sensing-based reconstruction algorithm is applied to the data collected during a relatively fast and therefore low-resolution Lissajous-like scan. Experiments and simulations show the feasibility of this method and compare the reconstructions to those made using simple nearest-neighbor interpolation.