This paper presents a neural network motion tracking control methodology for piezo-actuated flexure-based micro-/nanomanipulation mechanisms. In particular, the radial basis function neural networks are adopted for function approximations. The control objective is to track desired motion trajectories in the presence of unknown system parameters, nonlinearities including the hysteresis effect, and external disturbances. In this study, a lumped-parameter dynamic model that combines the piezoelectric actuator and the micro-/nanomechanism is established for the formulation of the proposed approach. The stability of the control methodology is analyzed, and the convergence of the position-and velocity-tracking errors to zero is proven theoretically. A precise tracking performance in following a desired motion trajectory is demonstrated in the experimental study. An important advantage of this control approach is that no prior knowledge is required for not only the system parameters, but also for the thresholds and weights of the neural networks in the physical realization of the control system. This control methodology is very suitable for the implementation of high-performance flexure-based micro-/nanomanipulation control applications.

Neural network motion tracking control of piezo-actuated flexure-based mechanisms for micro-/nanomanipulation

Soft robotics is a rapidly evolving field where robots are fabricated using highly deformable materials and usually follow a bioinspired design. Their high dexterity and safety make them ideal for applications such as gripping, locomotion, and biomedical devices, where the environment is highly dynamic and sensitive to physical interaction. Pneumatic actuation remains the dominant technology in soft robotics due to its low cost and mass, fast response time, and easy implementation. Given the significant number of publications in soft robotics over recent years, newcomers and even established researchers may have difficulty assessing the state of the art. To address this issue, this article summarizes the development of soft pneumatic actuators and robots up until the date of publication. The scope of this article includes the design, modeling, fabrication, actuation, characterization, sensing, control, and applications of soft robotic devices. In addition to a historical overview, there is a special emphasis on recent advances such as novel designs, differential simulators, analytical and numerical modeling methods, topology optimization, data-driven modeling and control methods, hardware control boards, and nonlinear estimation and control techniques. Finally, the capabilities and limitations of soft pneumatic actuators and robots are discussed and directions for future research are identified.

Soft Pneumatic Actuators: A Review of Design, Fabrication, Modeling, Sensing, Control and Applications

Analytical investigation of a nanosatellite panel surface temperatures for different altitudes and panel combinations

This letter introduces a robust adaptive control methodology for piezo-actuated flexure-based micro/nano manipulation mechanisms. This special control approach is established for tracking of the desired complex motion trajectories with system uncertainties. Further, the control methodology is formulated to accommodate not only the system uncertainties but also nonlinearities including the hysteresis effect and external disturbances in the motion of a 3-degrees-of-freedom (3-DOF) XYZ micro/nano manipulator. Adaptive fuzzy sliding mode control (AFSMC) is developed for the formulation of the control methodology. The fuzzy logic component and adaptation law employed in the controller are used to approximate the system uncertainties and compensate for the effect of nonlinear terms in the system. The resulting control strategy is devised using a sliding mode control scheme. The asymptotic tracking ability of the designed controller is proved by using Lyapunov-based stability analysis. Furthermore, an experimental study is performed on the 3-DOF compliant monolithic parallel micro/nano manipulator and the results confirm that the designed control approach produces high-precision motion tracking and smaller tracking errors compared to a model-free control technique, i.e. Proportional-Integral-Derivative (PID) controller.

Adaptive Fuzzy Sliding Mode Control for High-Precision Motion Tracking of a Multi-DOF Micro/Nano Manipulator

The piezoelectric-actuated compliant microgripper (PEACM) plays an essential role in the application fields such as biomedical engineering, microelectronics, and optical engineering. As compared with other categories of grippers, PEACM exhibits the advantages of high accuracy of displacement, large power to weight ratio, low energy consumption, and fast response speed. This paper reviews the recent advances on performance indices, classification, structural composition, optimization and modeling method, and control of PEACM. First, the gripper's performance indices and classifications are elaborated, which is beneficial to determine the design goal. Then, the compliant mechanisms adopted in the microgripper design are discussed, including the flexible hinge, displacement amplifier, and guiding mechanism. In addition, the optimization and modeling methods of the microgripper are presented. Popular types of position/force sensors and different displacement/force control strategies employed in the microgripper are surveyed. Moreover, the prospect on future development trend of the PEACM is discussed. The paper provides the reader with an overview of the recent advances on PEACM design and also a guideline on further development of the microgripper.

Tilok Kumar Das

Papers

A fuzzy disturbance observer based control approach for a novel 1-DOF micropositioning mechanism

Adaptive Fuzzy Sliding Mode Control for High-Precision Motion Tracking of a Multi-DOF Micro/Nano Manipulator

Development and control of a large range XYΘ micropositioning stage

A novel compliant piezoelectric actuated symmetric microgripper for the parasitic motion compensation

Design, modeling, and control of a large range 3-DOF micropositioning stage