Shape memory alloy (SMA) actuators are compact and have high force-to-weight ratios, making them strong candidates to actuate robots, exoskeletons, and prosthetics. To optimize speed and energy consumption, SMA actuators have been embedded in an NxN vascular network that can deliver electric and thermofluidic energy to the each actuator. The scalable architecture of the vascular network allows for 2N control devices (valves, transistors) to be shared amongst N2actuators, so that as the number of actuators increases, the number of required control devices scales at a smaller rate. This Network Array Architecture (NAA) allows for each actuator to be controlled individually or in discrete subarrays. However, not all combinations of actuators can be activated simultaneously; therefore in general, a sequence of control commands will be need to be executed in order to achieve the desired actuation. By treating each actuator as having a binary state, the combined states of the actuator array can be represented by graph theory, where states are nodes and the transitions between states are graph edges. By properly weighting the costs of the transitions, graph search techniques can be used to find optimal sets of control commands for desired state changes. This paper formulates the control of NAA actuators systems as a graph theory problem, and characterizes the ability of search algorithms to optimize a weighted combination of speed and energy usage, while minimizing computational cost.

Shape memory alloy (SMA) actuators are compact and have high force-to-weight ratios, making them strong candidates to actuate robots, exoskeletons, and prosthetics. However, these actuators are thermomechanical in nature and slow cooling rates can limit their performance. To improve the convective cooling, SMA wires have been embedded in vascular networks allowing cold fluid to pass across the actuators to cool/extend them. The vascular network can also deliver hot fluid to heat and contract the wire. In addition to the fluidic network, an electrical network operating in parallel can resistively heat the SMA to produce contraction. To minimize the weight and size of the vascular and electrical networks, a scalable N[N architecture has been implemented that allows for 2N control devices (valves, transistors) to be shared amongst N2actuators. This Network Array Architecture (NAA) allows each actuator to be controlled individually or in discrete subarrays. However, this architecture does not allow all combinations of actuators to be activated simultaneously; therefore in general a sequence of control commands will need to be executed in order to achieve the complete actuation. In order to find an optimal sequence of control commands, graph theory algorithms have been implemented. By treating each actuator's state as binary (fully contracted or extended), the collected states of an actuator array can be represented as nodes of the graph and the control commands as the graph edges. By properly weighting the costs of the graph edges, search algorithms can be used to find an optimal set of control commands for desired state changes. NAA results in a multi-graph that has 2NA—Nnodes and is highly interconnected. While initial work in this area established the basic validity of a graph search approach for NAA control, this article presents a scalable method of determining the optimal set of controls to minimize the operating cost (time and energy) and computational search cost.

The matrix vasoconstriction device (MVD) is a scalable mechanism than can control the fluid flow through a vascular network with n2wet shape memory alloy (SMA) actuators using 2n+2 constrictors (control elements). This vascular network delivers hot and cold fluid to conductively heat and cool shape memory alloy (SMA) muscles embedded in compliant vessels. The MVD mimics smooth muscles found in biological systems by constricting fluidic vessels to prevent flow. When released, the MVD does not add any fluidic resistance to the system, which has reduced the effective fluid resistance of the vascular network to 20% of the previous vascular system controlled by solenoid valves, therefore increasing the flow rate by a factor of 5. With the MVD's increased flow rates, a cycling rate of one Hz has been achieved for a single actuator. The MVD has (2n - 1)2possible configurations, which allow fluid flow through a single or multiple vessels at the intersection of one or more released row and column constrictors. As the number of released vessels increases, the total fluid resistance of the system decreases and the total flow through the system increases. Releasing all the constrictors, the flow through a 4 times 4 array can produce 3.5 actuations per unit time (where the unit time is the time necessary to drive a single actuator). This paper examines the operation of the MVD as it controls the ternary fluid flow (hot, cold, no flow) to an n times n array of wet SMA actuators operating independently and operating columns of actuators that are connected mechanically series with each other

This paper presents a new control method to drive an array of wet Shape Memory Alloy actuators utilizing a Matrix Manifold and Valve system (MMV). The MMV architecture allows a vast DOF robotic system to be controlled using limited number of resources. Using biological inspiration, the robotic system contains a network of “blood vessels” transporting fluid to the various “muscle” actuators. In general, an array of N2wet actuators can be controlled by 2N+2 fluidic valves, resulting in a scalable architecture. This would allow robots to contain a vast number of actuators like the human body, and be controlled in a scalable manner. The initial prototype contains a 4x4 array of 16 actuators controlled by 10 solenoid valves, where 25% of the actuators can be activated at any one time. Since only a subset of the actuators can be activated at any time, various methods of scheduling the resources (i.e. valves) are investigated, as well as various ways to define the error or difference between the desired and actual states of the array. The fluidic impedance of the system is taken into account in order to optimize the control. Results of simulation show that new scheduler options and definitions of error improve the performance of the system by a factor of 10 when operating the array near its maximum capacity.