Hysteresis Compensation Techniques in Piezoelectric Actuation
Summary
Piezoelectric actuators offer exceptional resolution, rapid response and substantial force in precision positioning applications, yet their inherent hysteresis markedly degrades accuracy and repeatability. Hysteresis compensation techniques seek to characterise and counteract this nonlinear, rate-dependent effect through a combination of advanced modelling and control strategies. Phenomenological hysteresis models—such as the Prandtl–Ishlinskii, Bouc–Wen and Duhem variants—provide invertible descriptions of the input–output loop, enabling effective feedforward controllers. Complementary feedback schemes, including proportional–integral–derivative cascaded with model inverses, sliding mode control and active disturbance rejection, enhance robustness against unmodelled dynamics and external perturbations. Data-driven and adaptive approaches, notably those based on artificial neural networks or single-neuron learning rules, offer online tuning to accommodate drift, rate effects and creep. Iterative learning control and observer-based compensation further refine accuracy in repetitive trajectories. Together, these techniques have driven sub-nanometre positioning in atomic force microscopy, high-speed lithography, optical fibre alignment and biomedical micro-assembly, underscoring their global significance in emerging nanotechnology and precision engineering domains.
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Hysteresis Compensation Techniques in Piezoelectric Actuation publication trend
The graph below shows the total number of articles in hysteresis compensation techniques in piezoelectric actuation across all publications each year (not limited to Nature Index journals).
Technical terms
Hysteresis: A nonlinear, path-dependent relationship between input voltage and output displacement, producing a characteristic looped response that varies with loading history.
Feedforward compensation: A control strategy that applies the inverse of a hysteresis model to the actuator input, counteracting predicted nonlinearities before they occur.
Prandtl–Ishlinskii model: A phenomenological hysteresis representation composed of weighted backlash operators, capable of capturing both rate-independent and rate-dependent behaviours.
Bouc–Wen model: A differential equation-based hysteresis model featuring adjustable parameters that govern loop shape, smoothness and memory effects.
Sliding mode control: A robust control methodology employing discontinuous corrective action to drive system states onto a predefined sliding surface, ensuring fast convergence and disturbance rejection.
Neural network compensation: A data-driven approach using adaptive learning algorithms to model and invert complex hysteresis dynamics in real time, accommodating drift and non-stationarity.
References
- Design, modeling and control of high-bandwidth nano-positioning stages for ultra-precise measurement and manufacturing: a survey. International Journal of Extreme Manufacturing (2024).
- A review of nonlinear hysteresis modeling and control of piezoelectric actuators. AIP Advances (2019).
- Modeling and Identification of the Rate-Dependent Hysteresis of Piezoelectric Actuator Using a Modified Prandtl-Ishlinskii Model. Micromachines (2017).
- Nonlinear Hysteresis Modeling of Piezoelectric Actuators Using a Generalized Bouc–Wen Model. Micromachines (2019).
- A Modified Duhem Model for Rate-Dependent Hysteresis Behaviors. Micromachines (2019).
- Hysteresis Compensation and Sliding Mode Control with Perturbation Estimation for Piezoelectric Actuators. Micromachines (2018).
- On the Disturbance Rejection of a Piezoelectric Driven Nanopositioning System. IEEE Access (2020).
- Feedforward Compensation Analysis of Piezoelectric Actuators Using Artificial Neural Networks with Conventional PID Controller and Single-Neuron PID Based on Hebb Learning Rules. Energies (2020).
- Single-Neuron Adaptive Hysteresis Compensation of Piezoelectric Actuator Based on Hebb Learning Rules. Micromachines (2020).
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