Dual sensing-actuation artificial muscle based on polypyrrole-carbon nanotube composite

Dual sensing artificial muscles based on conducting polymer are faradaic motors driven by electrochemical reactions, which announce the development of proprioceptive devices. The applicability of different composites has been investigated with the aim to improve the performance. Addition of carbon nanotubes may reduce irreversible reactions. We present the testing of a dual sensing artificial muscle based on a conducting polymer and carbon nanotubes composite. Large bending motions (up to 127 degrees) in aqueous solution and simultaneously sensing abilities of the operation conditions are recorded. The sensing and actuation equations are derived for incorporation into a control system.The research was supported by European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641822

Performance-driven control of nano-motion systems

The performance of high-precision mechatronic systems is subject to ever increasing demands regarding speed and accuracy. To meet these demands, new actuator drivers, sensor signal processing and control algorithms have to be derived. The state-of-the-art scientific developments in these research directions can significantly improve the performance of high-precision systems. However, translation of the scientific developments to usable technology is often non-trivial. To improve the performance of high-precision systems and to bridge the gap between science and technology, a performance-driven control approach has been developed. First, the main performance limiting factor (PLF) is identified. Then, a model-based compensation method is developed for the identified PLF. Experimental validation shows the performance improvement and reveals the next PLF to which the same procedure is applied. The compensation method can relate to the actuator driver, the sensor system or the control algorithm. In this thesis, the focus is on nano-motion systems that are driven by piezo actuators and/or use encoder sensors. Nano-motion systems are defined as the class of systems that require velocities ranging from nanometers per second to millimeters per second with a (sub)nanometer resolution. The main PLFs of such systems are the actuator driver, hysteresis, stick-slip effects, repetitive disturbances, coupling between degrees-of-freedom (DOFs), geometric nonlinearities and quantization errors. The developed approach is applied to three illustrative experimental cases that exhibit the above mentioned PLFs. The cases include a nano-motion stage driven by a walking piezo actuator, a metrological AFM and an encoder system. The contributions of this thesis relate to modeling, actuation driver development, control synthesis and encoder sensor signal processing. In particular, dynamic models are derived of the bimorph piezo legs of the walking piezo actuator and of the nano-motion stage with the walking piezo actuator containing the switching actuation principle, stick-slip effects and contact dynamics. Subsequently, a model-based optimization is performed to obtain optimal drive waveforms for a constant stage velocity. Both the walking piezo actuator and the AFM case exhibit repetitive disturbances with a non-constant period-time, for which dedicated repetitive control methods are developed. Furthermore, control algorithms have been developed to cope with the present coupling between and hysteresis in the different axes of the AFM. Finally, sensor signal processing algorithms have been developed to cope with the quantization effects and encoder imperfections in optical incremental encoders. The application of the performance-driven control approach to the different cases shows that the different identified PLFs can be successfully modeled and compensated for. The experiments show that the performance-driven control approach can largely improve the performance of nano-motion systems with piezo actuators and/or encoder sensors

An inverse Prandtl–Ishlinskii model based decoupling control methodology for a 3-DOF flexure-based mechanism

A modified Prandtl–Ishlinskii (P–I) hysteresis model is developed to form the feedforward controller for a 3-DOF flexure-based mechanism. To improve the control accuracy of the P–I hysteresis model, a hybrid structure that includes backlash operators, dead-zone operators and a cubic polynomial function is proposed. Both the rate-dependent hysteresis modeling and adaptive dead-zone thresholds selection method are investigated. System identification was used to obtain the parameters of the newly-developed hysteresis model. Closed-loop control was added to reduce the influence from external disturbances such as vibration and noise, leading to a combined feedforward/feedback control strategy. The cross-axis coupling motion of the 3-DOF flexure-based mechanism has been explored using the established controller. Accordingly, a decoupling feedforward/feedback controller is proposed and implemented to compensate the coupled motion of the moving platform. Experimental tests are reported to examine the tracking capability of the whole system and features of the controller. It is demonstrated that the proposed decoupling control methodology can distinctly reduce the coupling motion of the moving platform and thus improve the positioning accuracy and trajectory tracking capability

Modeling of macro fiber composite actuated laminate plates and aerofoils

© 2019 Sage Publications . The final, definitive version of this paper has been published in the Journal of Intelligent Material Systems and Structures by Sage Publications Ltd. All rights reserved. It is available at: https://doi.org/10.1177/1045389X19888728This article investigates the modeling of macro fiber composite-actuated laminate plates with distributed actuator patches. The investigation details an analytical and finite element modeling, with experimental validation of the bending strain and deflection of an epoxy E-glass fiber composite laminate. An analytical approach is also developed to estimate the plate deflection from the experimental strain measurements. The analytical method uses direct integration of single dimensional plate bending moments obtained by strain-induced shear moments from the macro fiber composite actuators. Finite element analysis software was used with the composite laminate modeled in ANSYS ACP. The results from both analytical and numerical models show good agreement with the experimental results, with strain values agreeing within 20 ppm and the maximum difference in deflection not exceeding 0.1 mm between models. Finally, an application of the analytical model for developing morphing aerofoil designs is demonstrated.Peer reviewe

Electrical coupling analysis of 2D time-multiplexing memory actuators exhibiting asymmetric butterfly hysteresis

We present the modeling and analysis of electrical coupling in a hysteretic deformable mirror with 2D memory piezoac- tuators, which are made of a purposely-designed piezomaterial sandwiched between electrodes arranged crosswise and actuated by a multiplexing approach. Using a modified Miller model to describe the memory effect which is based on the ferroelectric domain switching processes, the proposed framework is used to simulate the electric field dependence of the strain in the piezoelectric material that exhibits asymmetric butterfly loops with remnant deformation through the finite element method. The desired butterfly memory effect in the material is obtained by modifying the saturated dipole polarization curve in the Miller model. The proposed method allows us to numerically investigate the electrical coupling between actuators in more detail and correspondingly understand their influence to the mirror facesheet

From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

The design, hysteresis modeling and control of a novel SMA-fishing-line actuator

Fishing line can be combined with shape memory alloy (SMA) to form novel artificial muscle actuators which have low cost, are lightweight and soft. They can be applied in bionic, wearable and rehabilitation robots, and can reduce system weight and cost, increase power-to-weight ratio and offer safer physical human-robot interaction. However, these actuators possess several disadvantages, for example fishing line based actuators possess low strength and are complex to drive, and SMA possesses a low percentage contraction and has high hysteresis. This paper presents a novel artificial actuator (known as an SMA-fishing-line) made of fishing line and SMA twisted then coiled together, which can be driven directly by an electrical voltage. Its output force can reach 2.65N at 7.4V drive voltage, and the percentage contraction at 4V driven voltage with a 3N load is 7.53%. An antagonistic bionic joint driven by the novel SMA-fishing-line actuators is presented, and based on an extended unparallel Prandtl-Ishlinskii (EUPI) model, its hysteresis behavior is established, and the error ratio of the EUPI model is determined to be 6.3%. A Joule heat model of the SMA-fishing-line is also presented, and the maximum error of the established model is 0.510mm. Based on this accurate hysteresis model, a composite PID controller consisting of PID and an integral inverse (I-I) compensator is proposed and its performance is compared with a traditional PID controller through simulations and experimentation. These results show that the composite PID controller possesses higher control precision than basic PID, and is feasible for implementation in an SMA-fishing-line driven antagonistic bionic joint