Smart Material-based Actuators Research Articles

Adaptive Neural Digital Control of Hysteretic Systems With Implicit Inverse Compensator and Its Application on Magnetostrictive Actuator.

Hysteresis is a complex nonlinear effect in smart materials-based actuators, which degrades the positioning performance of the actuator, especially when the hysteresis shows asymmetric characteristics. In order to mitigate the asymmetric hysteresis effect, an adaptive neural digital dynamic surface control (DSC) scheme with the implicit inverse compensator is developed in this article. The implicit inverse compensator for the purpose of compensating for the hysteresis effect is applied to find the compensation signal by searching the optimal control laws from the hysteresis output, which avoids the construction of the inverse hysteresis model. The adaptive neural digital controller is achieved by using a discrete-time neural network controller to realize the discretization of time and quantizing the control signal to realize the discretization of the amplitude. The adaptive neural digital controller ensures the semiglobally uniformly ultimately bounded (SUUB) of all signals in the closed-loop control system. The effectiveness of the proposed approach is validated via the magnetostrictive-actuated system.

Dynamic RC operator-based hysteresis model of MR dampers

Nonlinear hysteretic mechanical behavior is a major obstacle to the efficient applications of vibration/shock and precision control systems with smart material-based actuators, such as magnetorheological (MR), magnetostrictive, piezoelectric and shape memory alloy actuators. In this paper, a dynamic resistor-capacitor (RC) operator (dRCO)-based hysteresis model(dRCOM) is proposed and thoroughly investigated for precise control of such actuators. The proposed dRCO links the RC operator (RCO) with the input rate, which enables the RCO the dynamic description performance. Employing the principle of graph neural network, the input data is preprocessed by the dRCO, and the neural network model is constructed for the parameter identification of the MR damper model. Experimental tests of the damping force characteristics of a commercial MR damper under different displacement excitations and applied currents are carried out. Correspondingly, mean square errors (MSEs) are compared between the tests and the outputs of the Bouc–Wen model, basic RCO-based hysteresis model (RCOM), and dRCOM. As a result, the dRCOM predicts MR damper more accurately than the other two. Furthermore, simulation and experimental tests of MR damper force tracking using different hysteresis models are also conducted to demonstrate the superiority of the dRCOM.

Bioinspired thermal/light-tunable actuators based on predesigned tilted liquid crystal actuators

Smart material-based actuators have attracted much interest because of their envisioned applications in the fields of soft robotics, sensors, and artificial muscles. In this study, we fabricated liquid crystal (LC) actuators via polydopamine (PDA) assistance based on controlling the order parameters. The irregular aggregation of PDA particles coated on the LC film surface was evaluated. To synthesize LC actuators, a predesigned glass sample cell filled with tilt-arranged LCs was fabricated. The LC mixtures were polymerized by 254 nm UV irradiation in glass sample cells. The polymerized LC films showed reversible bending and helical motions depending on predesigned molecular arrangement of the films. This phenomenon was attributed to the phase transition from monodomain to isotropic LC films triggered by temperature and near-infrared light exposure. Adding of chiral dopant shows much higher curling efficiency on thermal stimulation. A hand-shaped actuator with gripping ability was prepared from the synthesized LC networks. The near-infrared light sensitivity of the LC networks was further enhanced using PDA coated on the surface. The observed results suggested that the synthesized LC actuators effectively converted thermal energy and photo-energy to mechanical power. These predesigned LC actuators hold potential for applications as artificial muscles and in micro robotics. A series of thermally tunable liquid crystal elastomeric films was synthesized. Reversible shape variation controlled by temperature or light was achieved based on the order parameter control via the assistance of polydopamine.

High-bandwidth modeling for rate-dependent hysteresis nonlinearity using a standard Prandtl–Ishlinskii model

Rate-dependent hysteresis of piezoelectric stack actuators (PSAs) is known to cause oscillations and instabilities. This paper introduces a novel electro-mechanical coupling model that can accurately characterize the high-bandwidth rate-dependent hysteresis of a PSA. The proposed model contains three components to fully represent the output response of the system, namely, an electric model that is used to describe the electric dynamic behaviors of the PSA and its power amplifier, a hysteresis model that is applied to represent the inverse piezoelectric effect, and a mechanical model that is able to characterize the mechanical dynamic behavior of the PSA. It should be noted that, in the mechanical model, the force generated by the inverse piezoelectric effect of piezoelectric wafers is considered a distributed force and the dynamic model of the PSA is regarded as a longitudinal vibration straight bar with a uniform cross section. Through this proposed model, high modeling accuracy is achieved well with high-bandwidth varying frequencies (1–1000 Hz) as well as varying amplitudes. This paper provides a simple and effective novel method for the high-bandwidth modeling of smart material-based actuators with rate-dependent hysteresis nonlinearity.

Artificial Neural Network Hysteresis Operators for the Identification of Hammerstein Hysteretic Systems

Abstract This paper introduces explicit neural representations of fundamental hysteresis operators such as the play and stop operators. The hysteresis neurons are represented by recurrent artificial neural networks (ANN) using classical activation functions and are trained with gradient-based learning algorithms. The hysteresis neurons are combined into a single ANN hysteretic layer which, from a mathematical point of view, is equivalent to a classical Prandtl-Ishlinskii model expression, but has all the advantages of ANNs. One such benefit is the flexibility it offers to be combined with other ANN layers to obtain various complex model structures. In this paper, this is illustrated by combining it with a linear recurrent neural network to obtain a Hammerstein model structure, which is often considered for the characterization of systems driven by a class of smart material-based actuators. The input nonlinearity in the Hammerstein model is represented by a Prandtl-Ishlinskii ANN hysteretic layer while the linear dynamics are captured using a linear recurrent ANN representing a single-input multiple-output linear time-invariant state-space model. The identification approach of the Hammerstein model with a hysteretic layer is illustrated both numerically and experimentally.

Development of a combined Prandtl Ishlinskii–Preisach model

The Prandtl-Ishlinskii (PI) model and the Preisach model are commonly used models to predict the hysteretic behaviors in smart materials-based actuators. The advantages of the PI model are that (1) the structure of the model is simple, which facilitates the implementation of the model; (2) the analytical inverse of the model is available. The disadvantage of the PI model is that it can only describe the symmetric hysteresis effects. For the Preisach model, it is capable of describing various types of hysteretic behaviors, however, the model is too complicated to implement numerically. To merge the advantages of both models, a combined Prandtl Ishlinskii–Preisach (CPIP) model is proposed in this paper. The CPIP is not a simple combination of the two models. The discrete empirical interpolation method (DEIM) is applied to determine the structure of the CPIP model by selecting the dominated elementary operators. The CPIP model preserves the advantages of the PI model and the Preisach model, and is easy to implement numerically in the Matlab/Simulink environment. Experimental results validate the proposed CPIP model.

Pseudoextended Bouc–Wen Model and Adaptive Control Design With Applications to Smart Actuators

Smart material-based actuators are known to exhibit the hysteresis behavior, and most of them are rate-dependent, i.e., the level of hysteresis depends closely on the rate of input excitation frequency. This behavior is undesirable and has to be eliminated in order to facilitate high-precision applications. Therefore, there is a need for the comprehensive and robust approach to model and control the hysteresis effects. This paper proposes a new model modification to the phenomenological class of the hysteresis model. In this paper, the special case of the Bouc–Wen model is used as the basis for developing the modified one, and its establishment is realized in the discrete-time domain. Through the identification method called “extended particle swarm optimization,” it is verified that the proposed model is able to capture the dynamic and hysteretic behavior of a class of smart material-based actuators with a relatively good accuracy. Then, a robust adaptive control is synthesized based on the proposed model in order to eliminate the hysteresis effects. It is shown that the formulated adaptive controller guarantees the stability of the closed-loop control system. Experimental results show that the proposed control scheme mitigates the hysteresis effects efficiently.

Inverse Compensator for A Simplified Discrete Preisach Model Using Model-Order Reduction Approach

The classical Preisach model, which is built by the superposition of a great number of relay operators, is one of the most popular models to represent the hysteretic behaviors in various applications, such as the smart materials-based actuators. However, the construction of the inverse compensator for the classical Preisach model is very challenging for some reasons, first, the analytical inverse of the classical Preisach model is not available, and, second, due to a huge amount of the relay operators the implementation of the inverse compensator is troublesome and causes heavy computational burden. To overcome these drawbacks, a simplified discrete Preisach model is developed in this paper. The simplified model has an explicit expression with respect to the input of the model, thus it is simple to construct its inverse compensator using the inverse multiplicative structure approach. To reduce the computational effort in implementing the inverse compensator, the model-order reduction method is employed to reduce the complexity of the inverse compensator. Experimental tests are carried out to validate the effectiveness of the proposed approach.

Operator-Based Control System Analysis and Design for Nonlinear System with Input and Output Constraints

Smart material-based actuators and sensors have been widely used in practice owing to their various advantages. However, in the working process of these actuators and sensors, their output responses always deduce non-smooth nonlinear constraints. The constraint resulting from the actuator is called the input constraint and the constraint caused by the sensor is called the output constraint. These input and output constraints may induce inaccuracies and oscillations, severely degrading system performance. Therefore, the input and output constraints brought about by actuators and sensors should be considered in control system design. In this paper, system analysis for a nonlinear system with input and output constraints will be considered. The effect from the input constraint to the internal signal in the control system will be discussed. Moreover, the influence of the output constraint on the whole system will be studied. Further, the sufficient conditions for maintaining the stability of the system are obtained. Then, by using the robust right coprime factorization approach, an operator-based internal model like control structure is proposed for mitigating the input and output constraints. Finally, the effectiveness of the proposed design scheme will be confirmed through numerical simulation.

Inverse Compensation of Hysteresis Using Krasnoselskii-Pokrovskii Model

The Krasnoselskii-Pokrovskii (KP) model, as one of the popular operator-based hysteresis models, is commonly used to describe the hysteresis nonlinearities, especially in the smart materials-based actuators. Due to the complex formulation of the KP model, it is a great challenge to construct the inverse for the KP model. In this paper, an inverse multiplicative structure (IMS) is employed to find the inverse of the KP model. The merit of IMS is that this approach is simple to implement and the detailed knowledge of the hysteresis model is not required. However, the IMS technique cannot be directly applied to construct the inverse compensator for the KP model due to its complexity. Toward this problem, a new expression of the KP model is developed, where the input variable is expressed explicitly. With this new expression, the KP model can fit into IMS. Experiments are conducted to validate the effectiveness of the developed inverse compensator on a piezoelectric platform.

Internal model-based feedback control design for inversion-free feedforward rate-dependent hysteresis compensation of piezoelectric cantilever actuator

This study proposes a new rate-dependent feedforward compensator for compensation of hysteresis nonlinearities in smart materials-based actuators without considering the analytical inverse model. The proposed rate-dependent compensator is constructed with the inverse multiplicative structure of the rate-dependent Prandtl–Ishlinskii (RDPI) model. The study also presents an investigation for the compensation error when the proposed compensator is applied in an open-loop feedforward manner. Then, an internal model-based feedback control design is applied with the proposed feedforward compensator to a piezoelectric cantilever actuator. The experimental results illustrate that the proposed feedforward–feedback control scheme can be used in micro-positioning motion control applications to enhance the tracking performance of the piezoelectric cantilever actuator under different operating conditions.

Modelling and control of piezoelectric actuators by a class of differential equations-based hysteresis models

Hysteresis is a fundamental problem in most smart material-based actuators. This phenomenon limits the performance of systems driven by these actuators. This paper presents a control fusion strategy for hysteretic piezoelectric-based actuators which are described by Bouc-Wen model. A discrete prescribed performance control algorithm is developed and fused into the BW model. To verify the effectiveness of the modelling and control methods, experiments are made on a commercial piezoelectric actuator product. It is shown that the proposed controller guarantees not only the asymptotic stability but also transient performance.

Robust adaptive control for flap positioning system with SMA actuators-inverse Duhem model-based approach

Shape memory alloy (SMA) actuators are a class of smart materials-based actuators, which have some excellent features such as high power-to-weight ratio, large recovery strain and low driving voltages, etc. However, the saturated-type hysteresis non-linearities existing in the SMA actuators will degrade the system performance, and limit the improvement of the positioning precision. Focusing on this task, a robust adaptive control approach for the flap positioning system based on shape memory alloy actuators is addressed in this paper. A differential equation-based hysteresis model, Duhem model, is adopted to describe the saturation and minor loops characteristics in the SMA actuators by choosing the proper slope functions. The inverse of estimated Duhem hysteresis is preceded as the compensation. In this paper, the compensation error, lag effect and the unmodelled measure error are considered, and the proposed control method can stabilise the closed-loop system and ensure the accurate rotation of the flap to the desired angle. Simulations performed on a flap positioning system illustrate and clarify the approach.

Modelling rate-dependent symmetric and asymmetric hysteresis loops of smart actuators

Smart material actuators invariably exhibit hysteresis that may be either symmetric or asymmetric depending upon the actuation principle. Moreover, the shape of hysteresis loop depends on the rate of change of the input. In this study, a generalised rate-dependent Prandtl-Ishlinskii model is proposed to characterise both the symmetric and asymmetric input-output hysteresis effects of smart material-based actuators. The model is realised upon formulation and integration of a generalised rate-dependent play operator. The validity of the generalised model is demonstrated by comparing its displacement responses with the measured symmetric and asymmetric responses obtained for piezoceramic and magnetostrictive actuators under different input frequencies in the 1?200 Hz and 10?100 Hz ranges, respectively. The results suggest that the proposed rate-dependent Prandtl-Ishlinskii model can effectively characterise the symmetric as well asymmetric hysteresis properties of the smart material actuators over a wide range of input frequencies.

Development of High-rate, Adaptive Trailing Edge Control Surface for the Smart Wing Phase 2 Wind Tunnel Model

The DARPA/AFRL/NASA Smart Wing program, led by Northrop Grumman Corporation (NGC) under the DARPA Smart Materials and Structures initiative, addressed the development of smart technologies and demonstration of relevant concepts to improve the aerodynamic performance of military aircraft. In Phase 2, Test 2 of the program, the main objective was to demonstrate high-rate actuation of hingeless, spanwise, and chordwise deformable control surfaces using smart materials-based actuators on a 30% scale, full span wind tunnel model of a proposed NGC uninhabited combat air vehicle (UCAV). A minimum actuation rate of 25° flap deflection in 0.33 s, producing a slew rate of 75°/s, was desired. This slew rate is representative of many operational military aircrafts with hinged control surfaces. Numerous trade studies were performed on a variety of smart materials and flexible structure configurations before arriving at the final trailing edge structure design that consisted of a flexcore center and elastomeric outer skin actuated by high-power ultrasonic motors using an eccentric motion. The trailing edge control surface fitted onto the wind tunnel model comprised 10 eccentric-driven segments connected together by a continuous outer skin and a flexible hinge pin at the trailing edge tip. This pinned configuration allowed the segments partial freedom to rotate about each other, but constrained any lateral motion thus giving a smooth trailing edge shape for nonuniform spanwise deflections. To control the 10 segments of the trailing edge, a VME-based control system with high speed, simultaneously sampled A/D and D/A boards and a dedicated DSP board was developed. This paper describes the analysis and design of the flex structure, ultrasonic motor selection and performance, element and coupon tests to verify analysis, control system development, model integration, and results from the wind tunnel test.