Hysteresis is a nonlinearity exhibited by a wide class of smart materials, such as piezoelectrics and shape memory alloys, and it presents challenges in the control of smart material-actuated systems (for example, piezo-based nanopositioning systems). Existing methods for hysteresis compensation typically require an explicit model of the hysteresis, which tends to be high-dimensional operators and entails significant complexity in model identification and inversion. In this paper a novel hysteresis compensation method based on extended-high-gain observers and dynamic inversion is presented, which does not assume any specific hysteresis model. An extended high-gain observer is used to estimate the hysteresis output as well as other unknown dynamics of the system model, and then dynamic inversion is implemented to cancel the effect of hysteresis. With a mild assumption on the system and the input nonlinearity, the analysis of the closed-loop system under output feedback shows fast performance recovery to the trajectories of a target system, and that the tracking error converges exponentially to zero. Simulation results are presented to support the efficacy of the proposed approach.

A majority of stroke patients suffer from the loss of effective motor function, which compromises their ability to control grasping motion. Hand rehabilitation is therefore important to improve their motor function and quality of life in activities of daily living (ADLs). In this initial work, we present the design and development of a partial hand exoskeleton actuated by shape memory alloy (SMA) spring actuators. The SMA spring actuators are cooled by forced convection and the individual joints of the finger are actuated via tendons. In this design, pre-tension in the passive springs enables the restoration of the original configuration when the SMA springs are not actuated. To address the slow cooling rate of SMA springs that limits the control performance, we developed a cooling unit for each SMA spring actuator. We utilized computer vision to identify an object and provide 3-D coordinates of the optimal grasping points on the object. We then utilized vision-based control to move the fingertips towards the grasping points. The experimental results showed that each individual joint was able to return to its original configuration significantly faster as well as to follow a sinusoidal trajectory with the proposed cooling strategy.

In this paper, new design of micro-gripper with shape memory alloy (SMA) actuator is presented. Double SMA actuators were used to enhance the performance of the micro-gripper by using hinge mechanisms; the little displacement of the SMA wire is converted into larger displacement of the tips of the micro-gripper. Stainless steel (St 304) was used as a main material of the gripper structure. Shape memory alloy (Ni-Ti) wires were used as actuators. Finite element model analysis (FEA) using ANSYS software package was used to simulate displacement and stress analysis on the micro gripper. Finally a comparison between the enhanced design and the initial one showed better results in terms of increasing the gripper stroke and reducing the stress on the gripper joints.

The aim of this study is to develop a compact haptic glove that can present a variety of grasping sensations. This paper proposes a mechanism of compressing a finger joint to induce friction torque between the link and joint. In order to reduce weight and produce greater force, shape memory alloys were chosen as an actuator. The result of an experiment showed a linear relationship between the compressing force of a finger joint and friction torque, and suggested the effectiveness of the proposed mechanism. The prototype system suggested the proposed device is small and lightweight compared to the conventional device.

In this paper, we present an optimal design and control algorithm for multi-input binary-segmented Shape Memory Alloy (SMA) actuator arrays applied to a multi-degree-of-freedom (DOF) robot manipulator as it tracks a desired trajectory. The multi-DOF manipulator used for this paper is a 3-DOF-robot finger. A multi-input binary-segmented SMA actuator drives each DOF. SMA wires are embedded into a compliant vessel, such that both electric and fluidic (hot/cold) input can be applied to the actuators. By segmenting the SMA actuators, each segment can be controlled in a binary fashion (fully contracted/extended) to create a set of discrete displacements for each joint of the manipulator. To design the number of segments and length of each segment, an algorithm is developed to optimize the workspace. To optimize the workspace, it is desired to have a uniform distribution of reachable points in Cartesian space. Moreover, the number of neighbors (points that can be reached just by one control command from the current configuration) and the computational cost are important in workspace optimization. Graph theory search techniques based on the A* algorithm are employed to develop the control algorithm. A path-cost function is proposed to optimize the cost, which is a combination of actuation time, energy usage, and kinematic error. The kinematic error is estimated as the deviation between the actual and desired trajectory. The performance of the search algorithm and cost function are validated through simulation.

It has been shown that a set of multi-input (electrothermal and thermofluidic inputs) Shape Memory Alloy (SMA) actuators implemented into a Network Array Architecture (NAA) and treated like binary actuators can be represented by graph theory, such that the actuator configurations are represented as graph nodes, and transitions between states as graph edges. However, to achieve a desired actuation, a set of sequential control commands is required. A search algorithm was originally developed to identify a set of sequential control commands to go from a start node to a destination node with minimum path cost, where the cost function is a weighted combination of actuation time and energy. The original algorithm only considered one destination at a time, optimizing the present cost with no regard to any future costs. The aim of the current work is to modify the existing algorithm to control these SMA actuator arrays over a trajectory (multi-destination search problem), and take advantage of future destination knowledge to optimize the path cost. To achieve this goal, a sub-search algorithm and modified performance function are developed. For each heating control command, the sub-search algorithm compiles required information from future nodes. Then, this information is used by the modified performance function to estimate the future path cost. The modified performance function is designed to estimate the future path cost, while computing the current path cost. Therefore, the modified performance function will identify the sequence of operations with a minimum total path cost. The results show that the modified algorithm has a total path cost that is up to 30% less than the original algorithm total cost.

This paper presents the modelling of an actuator based on Magnetic Shape Memory Alloys (MSMA). The actuation principle relies on the ability of the material to change its shape under the application of a magnetic field. Previous models proposed by authors were based on canonical (symplectic) Hamiltonian modeling and thermodynamics of irreversible processes. These models, though physically cogent, are non-minimal differential algebraic dynamical models and hence less adapted for control purposes. This paper therefore proposes a modified and system-oriented modeling procedure which lends itself naturally to a port-Hamiltonian model. The latter is found to be a minimal realization of the above whereby interconnection between subsystems is clearly visible. Using Lagrange multipliers, constraints which arise due to causality and interconnection are expressed. In the last section, Differential Algebraic Equations (DAE) resulting from previous models are reduced to Ordinary Differential Equations (ODE) and by using coordinate transformations, constraints are decoupled from the system input/output. The resulting model is well-suited for control.

Displacement amplification mechanisms have been a topic of research for piezoelectric actuators for decades to overcome their significantly small strain, but still utilize their high power density, force, and efficiency. This paper further analyzes a nonlinear buckling mechanism to improve its efficiency, defined as the ratio of mechanical work output of the buckling actuator to the mechanical work output of the PZT actuator, as well as, employing two methods, preload and loading conditions, that improve its work output per cycle. This is accomplished by running a numerical analysis of the geometry of the flexure joints in the buckling mechanism which found a maximum mechanical efficiency of 48%. The preload is applied using shape memory alloy wire to exploit the low stiffness of the super elastic regime; which in turn allows a larger work output due to a loading condition supplied by a novel gear design. Finally, a prototype was fabricated to provide a baseline of comparison against these concepts.

This paper presents a conceptual design and preliminary analysis for a biomimetic robotic heart. The purpose of the robotic heart is to distribute hot and cold fluid to robotic muscles composed of wet shape-memory alloy (SMA) actuators. The robotic heart is itself powered by wet SMA actuators. A heart design concept is proposed and the feasibility of self-sustaining motion is investigated through simulation and experiment. The chosen design employs symmetric pumping chambers for hot and cold fluid. Analysis of this design concept shows that there exists a range of design parameters that will allow the heart to output more fluid than it uses. Additionally, it is shown that the heartbeat rate decreases as the system increases in size, and that the number of actuators and their length limit the power output of the pump. Experimental results from a prototype heart agree with the predicted trends from theoretical analysis and simulation.

A wet SMA actuator is characterized by an SMA wire embedded within a compliant fluid-filled tube. Heating and cooling of the SMA wire produce a linear contraction and extension of the wire. Thermal energy can be transferred to and from the wire using combinations of resistive heating and free/forced convection using hot and cold fluid. The goal of this paper is to analyze the speed and efficiency of wet SMA actuators using a variety of control strategies involving different combinations of electrical and thermofluidic inputs. A computational fluid dynamic model is used in conjunction with a temperature-strain model to simulate the thermal response of the wire and compute strains, contraction/extension times and efficiency. The simulations produce cycling rates of up to 5 Hz for electrical heating and fluidic cooling, and up to 2 Hz for fluidic heating and cooling. The results demonstrate efficiencies up to 0.5% for electric heating and up to 0.2% for fluidic heating.

In this work, a Linear Parameter-Varying (LPV) control method is used to compensate the hysteretic behavior of a Shape Memory Alloy (SMA) wire. Controller is implemented on an experimental system which consists of a pre-tension spring and a mass actuated with a thin SMA wire. The hysteretic characteristic of the SMA wire is modeled using the Preisach model and the model is verified both for the major and minor hysteresis loops. The small signal linear gain of the Preisach model is used as a scheduling stiffness variable. The parameter-dependent controller is scheduled based on the real time measurement of the stiffness variable. An H ∞ controller is also synthesized by representing the hysteresis as a parametric uncertainty and comparisons are made with LPV gain scheduling controllers using similar weights for both controllers. Experimental trajectory tracking results show that the LPV Gain Scheduling controller has a better response and the hysteresis uncertainty is compensated for the full range of stiffness variability.

This work develops a probability broadcast feedback controller for an ensemble of stochastically behaving cellular units exhibiting hysteresis. Previous work has developed asymptotically stable control laws for ideal on-off cellular units without any hysteresis or time lag. This work extends previous results by developing an asymptotically stable control law for an ensemble of cells that experience an arbitrary refractory period after a change in output, during which time the cell output is fixed. This refractory period describes the behavior of hysteretic cells such as shape memory alloy (SMA) actuators or biological cell migration. Conditions for stability are obtained using a stochastic Lyapunov function. Simulation of SMA actuators demonstrates the application of the new control law to practical hysteresis loops.

This paper details the development of a Neural Network (NN) controller for a Shape Memory Alloy (SMA) actuated catheter, with potential application to tele-operated cardiac ablation procedures. The robotic catheter prototype consists of a central tubular structure actuated by four SMA tendons. A dual-camera imaging system provides position feedback of the catheter tip. Open loop bending responses are obtained and associated nonlinearities are identified. A NN controller is designed using time-shifted input-output maps generated from randomized open loop measurements. The tracking performance of this NN controller is compared with PI/PID controllers for various reference trajectories.