Muscle driven devices are controlled or powered with muscle activation. These devices open up the possibility of offering patients with limited muscle function to automatically control assistive devices - for instance exoskeletons - with input from their own muscles. This solution would help a number of patient groups suffering from common conditions, such as spinal cord injuries, stroke and cerebral palsy. To use muscle activation as input it is necessary to have a way to communicate with the mus- cles. Electromyography (EMG) is a technology used to gain information about muscle function and activation. It is performed by measuring and analyzing electrical signals conducted by the muscles during activation. Activation and activation level can be seen from analyzed EMG signal. EMG signals are frequently measured and analyzed afterwards, however, to use it as a controlling an assistive devices, real time analysis is necessary. In this thesis real time acquisi- tion and analysis of EMG was performed. The measured signal was used as an input to control a simple MATLAB computer game. The EMG of a muscle of the forearm, Brachioradialis, was measured with Myon Aktos sys- tem. The measured signal was written to a server as soon as the measurements were acquired. MATLAB was used to connect to the server and performing the signal analysis. The analysis was kept simple in order to limit delay. The result showed that it was possible to acquire real time signal with this method. The delay was negligible, both for the testing and for the game play. Showing that it is possible to play a game with muscle activation supports the idea of a motor that can be controlled automatically with muscle input. Future work should focus on understanding movement intent with respect to EMG and on analyzing multiple signals from different muscles at the same time.

The use of real-time control (RTC) as an adaptation technique for improving existing stormwater systems has been gaining attention in recent years for its ability to enhance water quality and quantity treatment. A case study RTC retrofit of seven existing detention basins was simulated for a small (162 ha), urbanized watershed in Blacksburg, VA. Two heuristic, reactive control algorithms were tested and compared for their ability to improve hydraulic conditions at each detention basin and the watershed outlet through manipulation of an actuated valve, under various permutations of RTC retrofitting (single facility, multiple facilities, etc.). Change in peak flow during 24-hour design storms was assessed. RTC only reduced peak flows at some of the facilities for storms with a return period of 2 years or less. For larger storms, RTC maintained or increased peak flow rates. During a 15-year simulation with historic precipitation data, total duration of erosive flows was reduced for most facility retrofit simulations; however, the duration of high intensity flows increased, or remained unchanged. This result was also reflected at the watershed outlet. / Master of Science / Stormwater management helps protect natural waterways from the harmful impacts of human development. A growing field of research is investigating the potential for “smart” technologies to improve the efficiency of existing stormwater facilities. This study investigates the application of a “smart” stormwater retrofit, known as real-time control (RTC), to existing stormwater management facilities located in a small case study watershed. The RTC system is composed of hypothetical internet-connected sensors and control valves which control flows at several points within the test watershed. Two control algorithms were tested, and compared to the current conditions (scenario with no RTC), for a large range of storm events. Results of this study found that RTC would lead to improved stream health for most rainfall events, but could potentially worsen conditions for the largest, most rare storm events. In addition, RTC was found to be much more effective at some points in the watershed than other points. Prediction of where RTC will be most effective should be the focus of future research.

No / Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorithm so as to exploit special features of the hardware and avoid associated architecture shortcomings. This paper presents an investigation into the analysis and design mechanisms that will lead to reduction in the execution time in implementing real-time control algorithms. The proposed mechanisms are exemplified by means of one algorithm, which demonstrates their applicability to real-time applications. An active vibration control (AVC) algorithm for a flexible beam system simulated using the finite difference (FD) method is considered to demonstrate the effectiveness of the proposed methods. A comparative performance evaluation of the proposed design mechanisms is presented and discussed through a set of experiments.

No description available.

No description available.

This thesis synthesizes general and scalable distributed nonlinear control algorithms with application to legged robots. It explores both naturally decentralized problems in legged locomotion, such as the collaborative control of human-lower extremity prosthesis and the decomposition of high-dimensional controllers of a naturally centralized problem into a net- work of low-dimensional controllers while preserving equivalent performance. In doing so, strong nonlinear interaction forces arise, which this thesis considers and sufficiently addresses. It generalizes to both symmetric and asymmetric combinations of subsystems. Specifically, this thesis results in two distinct distributed control algorithms based on the decomposition approach. Towards synthesizing the first algorithm, this thesis presents a formal foundation based on de- composition, Hybrid Zero Dynamics (HZD), and scalable optimization to develop distributed controllers for hybrid models of collaborative human-robot locomotion. This approach con- siders a centralized controller and then decomposes the dynamics and parameterizes the feedback laws to synthesize local controllers. The Jacobian matrix of the Poincaré map with local controllers is studied and compared with the centralized ones. An optimization problem is then set up to tune the parameters of the local controllers for asymptotic stability. It is shown that the proposed approach can significantly reduce the number of controller parameters to be optimized for the synthesis of distributed controllers, deeming the method computationally tractable. To evaluate the analytical results, we consider a human amputee with the point of separation just above the knee and assume the average physical parameters of a human male. For the lower-extremity prosthesis, we consider the PRleg, a powered knee-ankle prosthetic leg, and together, they form a 19 Degrees of Freedom (DoF) model. A multi-domain hybrid locomotion model is then employed to rigorously assess the performance of the afore-stated control algorithm via numerical simulations. Various simulations involving the application of unknown external forces and altering the physical parameters of the human model unbeknownst to the local controllers still result in stable amputee loco- motion, demonstrating the inherent robustness of the proposed control algorithm. In the later part of this thesis, we are interested in developing distributed algorithms for the real-time control of legged robots. Inspired by the increasing popularity of Quadratic programming (QP)-based nonlinear controllers in the legged locomotion community due to their ability to encode control objectives subject to physical constraints, this thesis exploits the idea of distributed QPs. In particular, this thesis presents a formal foundation to systematically decompose QP-based centralized nonlinear controllers into a network of lower-dimensional local QPs. The proposed approach formulates a feedback structure be- tween the local QPs and leverages a one-step communication delay protocol. The properties of local QPs are analyzed, wherein it is established that their steady-state solutions on periodic orbits (representing gaits) coincide with that of the centralized QP. The asymptotic convergence of local QPs' solutions to the steady-state solution is studied via Floquet theory. Subsequently, to evaluate the effectiveness of the analytical results, we consider an 18 DoF quadrupedal robot, A1, as a representative example. The network of distributed QPs mentioned earlier is condensed to two local QPs by considering a front-hind decomposition scheme. The robustness of the distributed QP-based controller is then established through rigorous numerical simulations that involve exerting unmodelled external forces and intro- ducing unknown ground height variations. It is further shown that the proposed distributed QPs have reduced sensitivity to noise propagation when compared with the centralized QP. Finally, to demonstrate that the resultant distributed QP-based nonlinear control algorithm translates equivalently well to hardware, an extensive set of blind locomotion experiments on the A1 robot are undertaken. Similar to numerical simulations, unknown external forces in the form of aggressive pulls and pushes were applied, and terrain uncertainties were introduced with the help of arbitrarily displaced wooden blocks and compliant surfaces. Additionally, outdoor experiments involving a wide range of terrains such as gravel, mulch, and grass at various speeds up to 1.0 (m/s) reiterate the robust locomotion observed in numerical simulations. These experiments also show that the computation time is significantly dropped when the distributed QPs are considered over the centralized QP. / Doctor of Philosophy / Inspiration from animals and human beings has long driven the research of legged loco- motion and the subsequent design of the robotic counterparts: bipedal and quadrupedal robots. Legged robots have also been extended to assist human amputees with the help of powered prostheses and aiding people with paraplegia through the development of exoskeleton suits. However, in an effort to capture the same robustness and agility demonstrated by nature, our design abstractions have become increasingly complicated. As a result, the en- suing control algorithms that drive and stabilize the robot are equivalently complicated and subjected to the curse of dimensionality. This complication is undesirable as failing to compute and prescribe a control action quickly destabilizes and renders the robot uncontrollable. This thesis addresses this issue by seeking nature for inspiration through a different perspective. Specifically, through some earlier biological studies on cats, it was observed that some form of locality is implemented in the control of animals. This thesis extends this observation to the control of legged robots by advocating an unconventional solution. It proposes that a high-dimensional, single-legged agent be viewed as a virtual composition of multiple, low-dimensional subsystems. While this outlook is not new and forms precedent to the vast literature of distributed control, the focus has always been on large-scale systems such as power networks or urban traffic networks that preserve sparsity, mathematically speaking. On the contrary, legged robots are underactuated systems with strong interaction forces acting amongst each subsystem and dense mathematical structures. This thesis considers this problem in great detail and proposes developments that provide theoretical stability guarantees for the distributed control of interconnected legged robots. As a result, two distinctly different distributed control algorithms are formulated. We consider a naturally decentralized structure appearing in the form of a human-lower extremity prosthesis to synthesize distributed controllers using the first control algorithm. Subsequently, the resultant local controllers are rigorously validated through extensive full- order simulations. In order to validate the second algorithm, this thesis considers the problem of quadrupedal locomotion as a representative example. It assumes for the purposes of control synthesis that the quadruped is comprised of two subsystems separated at the geometric center, resulting in a front and hind subsystem. In addition to rigorous validation via numerical simulations, in the latter part of this thesis, to demonstrate that distributed controllers preserve practicality, rigorous and extensive experiments are undertaken in indoor and outdoor settings on a readily available quadrupedal robot A1.

The advent of synchronized phasor measurements allows the problem of real time prediction of instability and control to be considered. The use of direct methods for these on-line applications is assessed. The classical representation of a power system allows the use of two reference frames: Center of angle and one machine as reference. Formulae allowing transition between the two reference frames are derived. It is shown that the transient energy in both formulations is the same, and that line resistances do not dampen system oscillations. Examples illustrating the mathematical characterization of the region of attraction, exit point, closest u.e.p. and controlling u.e.p. methods are presented. Half-dimensional systems (reduced-order systems) are discussed. The general expression for the gradient system which accounts for transfer conductances is derived without making use of the infinite bus assumption. Examples illustrating the following items are presented: a) Effect of the linear ray approximation on the potential energy (inability to accurately locate the u.e.p.’s); b) Comparison of Kakimoto’s and Athay’s approach for PEBS crossing detection; c) BCU method and; d) One·parameter transversality condition. It is illustrated that if the assumption of the one-parameter transversality condition is not satisfied, the PEBS and BCU methods may give incorrect results for multi-swing stability. A procedure to determine if the u.e.p. found by the BCU method lies on the stability boundary of the original system is given. This procedure improves the BCU method for off~line applications when there is time for a hybrid approach (direct and conventional), but it does not improve it for on-line applications due to the following: a) It is time consuming and b) If it finds that the u.e.p. does not belong to the stability boundary it provides no information concerning the stability/instability of the system. / Ph. D. / incomplete_metadata

The use of optical fiber sensors is increasing widely in industry, civil, medicine, defense and research. Among different categories of these sensors is the Extrinsic Fabry-Perot interferometer (EFPI) sensor which is inherently simple and requires only modest amount of interface electronics. These advantages make it suitable for many practical applications. Investigating a cost-effective, reliable and repeatable method for optical fiber sensor fabrication is challenging work. In this thesis, a system for controlled fabrication of Fabry-Perot optical fiber sensors is developed and presented as the first attempt for the long-term goal of automated EFPI sensor fabrication. The sensor fabrication control system presented here implements a real-time control of a carbon dioxide (CO₂) laser as sensor bonding power, an optical fiber white light interferometric subsystem for real-time monitoring and measurement of the air gap separation in the Fabry-Perot sensor probe, and real-time control of a piezoelectric (PZT) motion subsystem for sensor alignment. The design of optoelectronic hardware and computer software is included. A large number of sensors are fabricated using this system and are tested under high temperature and high pressure. This system as a prototype system shows the potential in automated sensor fabrication. / Master of Science

No / This paper presents an investigation into the impact of data dependencies in real-time high performance sequential and parallel processing. An adaptive active vibration control algorithm is considered to demonstrate the impact of data dependencies in real-time computing. The algorithm is analysed in detail to explore the inherent data dependencies. To minimize the impact of data dependencies, an investigation into reducing memory access in sequential computing is provided. The impact of data dependencies with various interconnections is also explored and demonstrated in real-time parallel processing through a set of experiments.

During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis. This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range. / MS / During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis. This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this iv factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range.