Neuro-Fuzzy Controller for SMAs for a Morphing Wing Application

Significant advances in smart material actuators have taken place in the past decade. The holy grail of actuator research is an architecture that can generate high displacement and force throughout a broad frequency range while not consuming a significant amount of electrical power. The large appeal of using smart material actuators stems from their high mechanical energy density. However, all smart material actuators generally have at least one shortcoming involving either mechanical stroke, force, or frequency capability. Whenever speed is a consideration, piezoelectric actuation is the most commonly employed. The purpose of this paper is to review the most current trends in piezoelectric actuation architectures. The paper does not present the theoretical details of each actuator, but instead strives to highlight the novel concepts used in each design to overcome the stroke limitation of the material.

Nanorobotics have been successfully applied to investigate the physical and biomedical properties in the nano scale. As the center piece of nanorobotcis Scanning probe microscope (SPM) has been widely used to measure the topography information of sample surface as well as serving as a manipulation and sensing tool to modify and measure the mechanical and physical properties of sample. However, the manipulation accuracy of the SPM based nanorobot has been considered as a serious issue because of the hysteresis, creep and thermal drift of its piezoelectric actuator. Generally, creep is linear, which is easy to model; thermal drift is unpredictable, however, it is slow and has an ultimate balance state, thus easy to reduce. Unlike creep characteristic, hysteresis is nonlinear and exists ubiquitously in smart material actuators and seriously affects behaviors of them. Thus cancelation of it will significantly improve motion precision using actuator such as piezoelectric actuators. In this research, we proposed a novel hysteresis modeling and compensation strategy: Polynomial Prandtl-Ishlinskii (PPI) operator superposition model, and its inverse compensator. The property of PPI model is that, it possesses a similar modeling capacity as Preisach model, more important, it can be represented as compressive as the classical PI model. The PPI inverse compensator is then combined with a PID controller, named PPI Inverse Aided PID (PIA-PID) controller, to reduce the output error caused by hysteresis, creep and some unknown drift of our atomic force microscope (AFM) based nanorobot.

This paper presents a smart way to actuate and control the airfoil shape of a morphing wing in the open-loop architecture. The actuation system uses smart material actuators such as shape memory alloys, disposed in two parallel actuation lines, and its control is performed with a Mamdani-type fuzzy logic proportional derivative (PD) controller. The morphing wing project description, its actuation system structure, and the control design and validation are highlighted in this paper. The results obtained by both numerical simulation and experimental validation (bench tests and wind-tunnel tests) are presented as part of the control design and validation. An analysis of the wind flow characteristics, based on the information provided by the pressure sensors mounted on the flexible skin of the morphing wing in the wind-tunnel tests, is included as a supplementary validation; the transition between laminar and turbulent flows is real-time visualized, and the aerodynamic efficiency of the controlled morphing wing is evaluated.

Hysteresis phenomenon occurs in all smart material-based sensors and actuators, such as shape memory alloys, piezoceramics and magnetostrictive actuators (Bank & Smith, 2000; Tan & Baras, 2004). In order to study this phenomenon, different models were proposed (Brokate & Sprekels, 1996; Visintin, 1994). Normally, hysteresis models are classified into two categories, physics-based model such as Jiles-Atherton model (Jiles & Atherton, 1986) and phenomenology-based model such as Preisach operator (Brokate & Sprekels, 1996; Visintin, 1994) and Duhem model (Visintin, 1994). From control systems point of view, hysteresis is generally non-differentiable, nonlinear, and unknown. As a result, systems with hysteresis usually exhibit undesirable inaccuracies or oscillation and even instability. Mitigating the effect of hysteresis becomes necessary and important, thus it has received increasing attention in recent years (Tao & Kokotovic, 1995, Su, et al, 2000, Su, et al, 2005). Many of these studies are related to modeling of hysteresis and their control issues. With the development of artificial intelligent (AI), AI is being applied to dealing with nonlinearities in systems (Ge & Wang, 2002). Only a few studies have been carried out by using NN to tackle hysteresis modeling and compensation (Makaveev, et al, 2002; Kuczmann & Ivanyi, 2002; Beuschel et al, 1998; Zhao & Tan, 2006). In the paper (Makaveev, et al, 2002), a NN model is used to describe the hysteresis behavior in different frequencies with the knowledge of some properties of magnetic materials, such as loss separation property to allow the separate treatment of quasi-static and dynamic hysteretic effects. Beuschel et al used (Beuschel et al, 1998) a modified Luenberger observer and NN are used to identify a general model of hysteresis. These researches demonstrate that NN can work as an unknown function approximator to describe the characteristics of hysteresis. Recently, two papers (Zhao & Tan, 2006; Lin et al 2006) applied the approximation property of NN to coping with the identification of Preisach-type hysteresis in piezoelectric actuator, and the hysteresis estimation problem for piezo-positioning mechanism based on hysteresis friction force function, respectively. It should be noted that the aforementioned results share a common assumption that the output of hysteresis is measurable. In practical systems, smart actuators are integrated into the systems, which makes the measurement of output of hysteresis hard. Hence it is a challenge to design an observer for

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.

This paper presents a wireless diaphragm actuator and focuses on a lumped parameter grey-box model capable of simulating actuation displacement. Here, modelling aims to provide further insight into this smart material actuator and help facilitate design into new implantable applications. Model parameters were calibrated to a prototype actuator using experimental data and a genetic algorithm optimisation. The model was validated against experimental data and showed a good ability to simulate both static and dynamic diaphragm displacement.

Atomic Force microscopy (AFM) is a powerful technology for observing and developing the micro/nano world, which has been bringing tremendous revolution opportunities to various fields. An AFM's maneuverability can be enhanced via modification into a nanorobotic system with its scanning probe working as the end-effector. The probe's spatial precision can easily reach to nanometer level, which is commonly actuated by smart materials, typically the piezoceramics. However, instinctive hysteretic characteristics ubiquitously exist in smart material actuators, which degrade their controllable positioning accuracy, especially for the open-loop cases. As common phenomena, input-output hysteretic relations of integrated AFM systems are generally complicated, caused by actuators' specific characteristics. Since the AFM based nanomanipulation requires slow operations to prevent damage from samples and the sharp probe itself, commonly only hysteresis at low frequency (typically less than 10Hz) need to be reduced. To precisely represent and further reduce generalized hysteretic effects at low frequency, this paper proposes the equivalent representation of the classical Preisach model with analytical inversion. The contributions of this paper are: it is the first time that the exact inversion is established for the Preisach model with generalized discrete representation; furthermore, while maintaining modeling accuracy, the new discrete Preisach model significantly reduces model complexity compared to the traditional Preisach model. Numerical verification was conducted to demonstrate the effectiveness of the proposed discrete Preisach model and its analytical inversion.

In this paper, tactile response characteristics in medical haptic interface are investigated to characterize the feeling of contact between the finger skin and the organic tissue when a finger is dragged over tissue. In order to represent the tactile feeling, a prototype tactile display incorporating Magneto-Rheological (MR) fluid has been developed. Tactile display devices simulate the finger's skin to feel the sensations of contact such as compliance, curvature and friction. Thus, the tactile display provides the surface information of organic tissue to the surgeon using different actuating mechanisms ranging from the conventional mechanical motor to the smart material actuators. In order to investigate the compliance feeling of human finger's touch, vertical force responses of the tactile display under the various magnetic fields have been assessed. Also, frictional resistive force responses of the tactile display are investigated to simulate the action of finger's dragging. From the results, different tactile feelings are observed as the applied magnetic field is varied and arrayed magnetic poles combinations. This research gives a smart technology of tactile displaying.

In this work, we consider the problem of controlling the end effector position of a continuum manipulator through local stiffness changes. Continuum manipulators offer the advantage of continuous deformation along their lengths, and recent advances in smart material actuators further enable local compliance changes, which can affect the manipulator's bulk motion. However, leveraging local stiffness change to control motion remains lightly explored. We build a kinematic model of a continuum manipulator as a sequence of segments consisting of symmetrically arranged springs around the perimeter of every segment, and we show that this system has a closed form solution to its forward kinematics. The model includes common constraints such as restriction of torsional or shearing movement. Based on this model, we propose a controller on the spring stiffnesses for a single segment and provide provable guarantees on convergence to a desired goal position. The results are verified in simulation and compared to physical hardware.

A survey on actuators-driven surgical robots

High-precision positioning technology is the core support in the field of modern precision manufacturing and micro-nano operation, and its performance directly determines the processing accuracy of high-end equipment. In this paper, a solution based on neural network iterative learning control is proposed to solve the problem of hysteresis nonlinearity in smart material actuators. This method combines the strong nonlinear approximation ability of neural network with the memory characteristic of iterative learning control, and realizes the adaptive compensation of complex dynamic systems by adjusting the controller parameters online. The control strategy can effectively suppress the influence of hysteresis nonlinearity on the positioning accuracy, and shows superior tracking performance under different frequency signals and load conditions. The experimental results show that, compared with the traditional iterative learning control method, the neural network iterative learning control has a significant improvement in tracking accuracy, robustness and convergence speed, which provides a new technical approach for the practical application of high-precision positioning system.

Photovoltaic electricity is seen as an important source of renewable energy. The photovoltaic array is an unstable source of power since the peak power point depends on the temperature and the irradiation level. This paper presents a simulation of PV system (PV panel, boost inverter and a resistance load) using MATLAB. To prove the efficiency of this system we applied two methods of MPPT. The different steps of the design of these algorithms of MPPT are presented together with its simulation. The performance comparison between Neuro-fuzzy controller and Incremental conductance method (IncCond) has been carried out which shown the effectiveness of Neuro-fuzzy logic controller to draw much energy and a fast response against change in working conditions.

Hysteresis is a nonlinear phenomenon, which exists in smart actuators, is usually undesirable and influences the performance of actuator. Therefore, it is necessary to establish a precise hysteresis model to eliminate hysteresis effects. A novel adaptive filter is proposed to model the rate-dependent hysteresis nonlinearity in a Giant Magnetostrictive Actuator. In the proposed filter, Generalized Play Operators are combined with linear delayed adaptive transversal filter to compose a new serial structure of adaptive filter model. Variable step LMS algorithm is used to adjust the weights value. In the end, the proposed adaptive filter is applied to model the rate dependent hysteresis of Giant Magnetostrictive Actuator. Experimental results show that the proposed Generalized Play Operator adaptive filter can describe the rate-dependent hysteresis behaviors including different single frequency input signal and multi-frequency composite input signal. The modeling results are agreed with experimental data well, the root mean square error is less than 0.5073 micron and the relative error is less than 3%.

Optimization of system capacity electric power transmission systems requires a reliable power flow controller. The power flow controllers must be able to control the level of electrical voltage and active and reactive power flow without reducing the level of stability and security of the transmission system. Latest technology in the control of power flow is a Unified Power Flow Controller (UPFC). The entire transmission line parameters are impedance, voltage, and phase angle can be controlled simultaneously by the UPFC. The method used in the conventional algorithms based UPFC is still firmly with logic. These algorithms have difficulties to electric power transmission systems multimachine very dynamic, i.e. systems that are experiencing rapid changes in the electrical load from time to time. Therefore, in this study was developed based on neuro-fuzzy method is applied to the adaptive UPFC for adaptively controlling the power flow in electric power transmission systems multimachine very dynamic. In this study, three phase fault is applied to the multimachine system. The results are taken to be consideration of PI and neuro-fuzzy controllers. The PI and neuro-fuzzy controllers show nearly same results but there is a low overshoot occurred during the fault in the neuro-fuzzy controllers results. According to results that UPFC improves the system performance under the transient and the normal conditions. However, it can control the power flow in the transmission line, effectively.

Nonlinear vibration absorption for a flexible arm via a virtual vibration absorber