Dynamic Modeling and Control System Design for Shape Memory Alloy Actuators

Shape memory alloy (SMA) is a type of smart material which remembers its original state. It is light weight and small, and known to provide high contraction force with low noise. Its application has wide range from robotics to medical science. One of its potential applications in space is a supporting system of membrane structure that can be used as synthetic aperture radar (SAR) antenna to achieve high flatness. It exhibits nonlinear phenomena called hysteresis when it's electrically heated. Hysteresis is a nonlinear phenomenon that refers to the dependence of a physical system on the environment. Hysteresis in SMA causes a major difficulty in control system design. Un-modeled or poorly modeled hysteresis introduces inaccuracy in tracking and the performance of the system. Experimental test bench is constructed for one set of SMA actuators that resembles the membrane structure's supporting system. Hysteresis is obtained by running open loop test with the test bench. Dynamic model of the SMA wires is developed using classical Preisach model and modified Maxwell model. Then the inverse model is implemented in feed-forward loop to compensate for nonlinear hysteresis. Simple feedback controllers are added to correct the modeling errors. Experimental results reveal that the error is significantly reduced when comparing feedback controller with hybrid feedback and feed-forward controller

A Methodology Towards Comprehensive Evaluation of Shape Memory Alloy Actuators for Prosthetic Finger Design

Presently, DC motors are the actuator of choice within intelligent upper limb prostheses. However, the weight and dimensions associated with suitable DC motors are not always compatible with the geometric restrictions of a prosthetic hand; reducing available degrees of freedom and ultimately rendering the prosthesis uncomfortable for the end-user. As a result, the search is on-going to find a more appropriate actuation solution that is lightweight, noiseless, strong and cheap. Shape memory alloy (SMA) actuators offer the potential to meet these requirements. To date, no viable upper limb prosthesis using SMA actuators has been developed. The primary reasons lie in low force generation as a result of unsuitable actuator designs, and significant difficulties in control owing to the highly nonlinear response of SMAs when subjected to joule heating. This work presents a novel and comprehensive methodology to facilitate evaluation of SMA bundle actuators for prosthetic finger design. SMA bundle actuators feature multiple SMA wires in parallel. This allows for increased force generation without compromising on dynamic performance. The SMA bundle actuator is tasked with reproducing the typical forces and contractions associated with the human finger in a prosthetic finger design, whilst maintaining a high degree of energy efficiency. A novel approach to SMA control is employed, whereby an adaptive controller is developed and tuned using the underlying thermo-mechanical principles of operation of SMA wires. A mathematical simulation of the kinematics and dynamics of motion provides a platform for designing, optimizing and evaluating suitable SMA bundle actuators offline. This significantly reduces the time and cost involved in implementing an appropriate actuation solution. Experimental results show iii that the performance of SMA bundle actuators is favourable for prosthesis applications. Phalangeal tip forces are shown to improve significantly through bundling of SMA wire actuators, while dynamic performance is maintained owing to the design and implementation of the selected control strategy. The work is intended to serve as a roadmap for fellow researchers seeking to design, implement and control SMA bundle actuators in a prosthesis design. Furthermore, the methodology can also be adopted to serve as a guide in the evaluation of other non-conventional actuation technologies in alternative applications

Shape Memory Alloy Actuators and Sensors for Applications in Minimally Invasive Interventions

Reduced access size in minimally invasive surgery and therapy (MIST) poses several restriction on the design of the dexterous robotic instruments. The instruments should be developed that are slender enough to pass through the small sized incisions and able to effectively operate in a compact workspace. Most existing robotic instruments are operated by big actuators, located outside the patient’s body, that transfer forces to the end effector via cables or magnetically controlled actuation mechanism. These instruments are certainly far from optimal in terms of their cost and the space they require in operating room. The lack of adequate sensing technologies make it very challenging to measure bending of the flexible instruments, and to measure tool-tissue contact forces of the both flexible and rigid instruments during MIST. Therefore, it requires the development of the cost effective miniature actuators and strain/force sensors. Having several unique features such as bio-compatibility, low cost, light weight, large actuation forces and electrical resistivity variations, the shape memory alloys (SMAs) show promising applications both as the actuators and strain sensors in MIST. However, highly nonlinear hysteretic behavior of the SMAs hinders their use as actuators. To overcome this problem, an adaptive artificial neural network (ANN) based Preisach model and a model predictive controller have been developed in this thesis to precisely control the output of the SMA actuators. A novel ultra thin strain sensor is also designed using a superelastic SMA wire, which can be used to measure strain and forces for many surgical and intervention instruments. A da Vinci surgical instrument is sensorized with these sensors in order to validate their force sensing capability

Neural Network Direct Control with Online Learning for Shape Memory Alloy Manipulators

New actuators and materials are constantly incorporated into industrial processes, and additional challenges are posed by their complex behavior. Nonlinear hysteresis is commonly found in shape memory alloys, and the inclusion of a suitable hysteresis model in the control system allows the controller to achieve a better performance, although a major drawback is that each system responds in a unique way. In this work, a neural network direct control, with online learning, is developed for position control of shape memory alloy manipulators. Neural network weight coefficients are updated online by using the actuator position data while the controller is applied to the system, without previous training of the neural network weights, nor the inclusion of a hysteresis model. A real-time, low computational cost control system was implemented; experimental evaluation was performed on a 1-DOF manipulator system actuated by a shape memory alloy wire. Test results verified the effectiveness of the proposed control scheme to control the system angular position, compensating for the hysteretic behavior of the shape memory alloy actuator. Using a learning algorithm with a sine wave as reference signal, a maximum static error of 0.83º was achieved when validated against several set-points within the possible range

Position control of a shape memory alloy actuator using a four-term bilinear PID controller

Shape memory alloy (SMA) actuators have a number of appealing features, such as their low weight or their high force-to-weight ratio, that make them a potential alternative to traditional actuation technologies in fields such as space applications, surgical devices or wearable robotics. In this paper, a type of bilinear controller consisting of a conventional PID controller cascaded with a bilinear compensator, known as BPID, is proposed. Bilinear controllers are a subset of nonlinear controllers, which is why the BPID may be a promising alternative to control the position of a SMA actuator. Nonlinear control techniques are commonly applied to control SMA actuators, because of their nonlinear behavior caused by thermal hysteresis. The BPID controller is simpler and easier to implement than other nonlinear control strategies, which makes it a very appealing candidate to control SMA actuators. The performance of the BPID controller has been compared with other two controllers, a conventional PID and a commuted feed-forward PIPD, controlling a real SMA actuator. To this end, a set of five tests has been defined, in which the controlled actuator must follow a series of position references. From these tests, the position and error of the actuator have been plotted, and a series of metrics has been computed to have quantitative measurements of the performance of the three controllers. It is shown that, in most of the experiments, the BPID has a better performance than the other two tested controllers, especially tracking step references. However, the power consumption is slightly higher when the actuator is controlled with this strategy, although-the difference is minimal. Also, the BPID imposes greater energy variations to the SMA actuator, which might affect its service life. Overall, the BPID controller has proved to be a viable alternative to control SMA actuators.The research leading to these results has received funding from the STAMAS (Smart technology for artificial muscle applications in space) project, funded by the European Union’s Seventh Framework Programme for Research (FP7) (grant number 312815), and from the RoboHealth (DPI2013-47944-C4-3-R) Spanish research project

Design and Implementation of Micro Mechatronic Systems- SMA Drive Polymer Microgripper

A micromechatronic gripper was designed, fabricated, and tested with the proposed control system. By following realization axioms, the microgripper system including a polyurethane (PU) gripper mechanism and shape memory alloy (SMA) actuator was designed and developed. The micromechatronic gripper system was realized with cross-sectional area of (π/4) × 5002 μm2 for clean room operation. A synergetic operation of SMA actuator for driving microgripper mechanism was investigated in visual-based control. By incorporating with inverse Preisach compensator, an explicit self-tuning controller through Ziegler-Nichols criterion was selected for controlling the self-biased SMA actuator. The application of the gripper system for gripping and transporting a glass particle of 30 μm was tested

Adaptive inverse modeling of a shape memory alloy wire actuator and tracking control with the model

It is well known that the Preisach model is useful to approximate the effect of hysteresis behavior in smart materials, such as piezoactuators and Shape Memory Alloy(SMA) wire actuators. For tracking control, many researchers estimate a Preisach model and then compute its inverse model for hysteresis compensation. However, the inverse of its hysteresis behavior also shows hysteresis behavior. From this idea, the inverse model with Kransnoselskii-Pokrovskii(KP) model, a developed version of Preisach model, can be used directly for SMA position control and avoid the inverse operation. Also, we propose another method for the tracking control by approximating the inverse model using an orthogonal polynomial network. To estimate and update the weight parameters in both inverse models, a gradient-based learning algorithm is used. Finally, for the SMA position control, PID controller, adaptive controllers with KP model and adaptive nonlinear inverse model controller are compared experimentally

Controller Gain Optimization for Position Control of an SMA Wire

There has been an increasing interest in the field of `smart structures' and `smart materials'. In constructing smart structures, a class of materials called smart materials are often used as sensors and actuators. An example of a smart material is shape memory alloy (SMA). A common actuator configuration uses an SMA wire with a constant load. The non-linear input-output behaviour of SMAs, known as hysteresis, made them difficult to model and control. The research in this thesis examines the effect of PID-controller gain optimization on SMA wire control at different frequencies of operation. A constant-load SMA wire actuator with a PID-controller is used in the study. Heat is applied to the wire using an input electric current. The system is cooled through convection with the surrounding area. The lack of active cooling prevents the system from operating at high frequencies. Three different cost functions are proposed for various applications. The Preisach model is chosen to model the hysteretic behaviour of the SMA wire contraction. Varying material properties such as electrical resistance and heat capacities are modelled to give a more accurate representation of the system's physical behaviour. Simulations show that by optimizing the controller gain values, the bandwidth of the system is improved. An interesting observation is made in the heating cycle of the SMA wire. In order to achieve faster cooling, overshoot is observed at low frequencies. This is a result of the system hysteresis. The system hysteresis allows different input signals to achieve the same output value. Since the rate of cooling is proportional to the temperature above ambient, better cooling is achieved by reaching a higher temperature. The error caused by the overshoot is compensated by the better cooling phase, which is not actively controlled

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