Feedrate optimization for computer numerically controlled (CNC) machine tools is a challenging task that is growing in importance as manufacturing industry demands faster machine tools. The majority of research in this area focusses on optimizing feedrate using modeled process constraints. Some researchers have suggested using measured process parameters instead. The former approach suffers from uncertainties in the modeled process data that is the starting point of the optimization. The latter approach has difficulties achieving high levels of optimality. This study proposes the combination of both modeled and measured process data. To this end, a control architecture is described that allows combining measured and modeled process constraints. Within this architecture, a new algorithm to determine time optimum feedrates using modeled velocity and acceleration constraints is proposed. The new control structure including the novel feedrate optimization algorithm is verified experimentally on a high speed biaxial table.

This article is devoted to simultaneous displacement and force self-sensing using a single piezoelectric stack actuator. Values of displacement and force are estimated from measurements of charge and applied voltage. To calibrate the self-sensing algorithm, a parameter identification procedure is presented that accounts for hysteresis under varying displacement and force conditions using a standard Prandtl–Ishlinskii hysteresis operator. Quasi-static validation experiments are carried out on a custom-built test rig that shows low average estimation errors of $0.5 \%$ and $-0.4\%$ with standard deviations of $2.7\%$ and $3.1\%$ in displacement and force, respectively.

This study describes a new self-sensing technique for piezoelectric actuators that estimates both displacement and force from voltage and charge measurements. The novelty of this technique lies in the use of a constitutive model with nonlinear capacitance and an exponential hysteresis model. The advantage of the presented self-sensing technique over similar techniques in the literature is that it only requires one parameter identification experiment and its hysteresis model is computationally efficient. The control performance of the self-sensing technique is evaluated in a sensorless displacement and force control structure. Validation experiments are carried out on a two degree of freedom test setup that mimics micro-positioners by generating simultaneously varying displacements and forces. Less than 3% absolute average errors are obtained for both displacement and force.

This paper describes a new method for estimating the tip displacement of piezoelectric benders. Two resistive strain gauges are fabricated within the top and bottom electrodes using an acid etching process. These strain gauges are employed in a half bridge electrical configuration to measure the surface resistance change, and estimate the tip displacement. Experimental validation shows a 1.1% maximum difference between the strain sensor and a laser triangulation sensor. Using the presented method, a damping-integral control structure is designed to control the tip displacement of the integrated bender.