Background: Treadmill walking should closely simulate overground walking for research validation and optimal skill transfer. Traditional fixed-speed treadmill (FS) walking may not simulate natural walking because of the fixed belt speed and lack of visual cues. Self-paced (SP) treadmill walking, especially feedback controlled SP treadmill walking, enables close-to-real-time belt speed changes with users' speed changes. Different sensitivity levels of SP treadmill feedback determine how fast the treadmill respond to user's speed change. Few studies have examined the differences between FS and SP treadmill walking, or the difference between sensitivity levels of SP treadmills, and their methods were questionable because of averaging kinematics and kinetics parameters, and failing to examine directly treadmill and subjects' speed data. This study compared FS with two SP modes with variation of treadmill speed and user's speed as dependent variables. Method: Thirteen young healthy subjects participated. Subjects walked on a motorized split-belt treadmill under FS, high sensitivity SP (SP-H) and low sensitivity SP (SP-L) conditions at normal walking speed. Root mean square error (RMSE) for subject's pelvis global speed (Vpg), pelvis speed with respect to treadmill speed (Vpt), and treadmill speed (Vtg) data were collected for all trials. Results: Significant condition effects were found between FS and the two SP modes in all RMSE values (p < 0.001). The two sensitivity levels of SP had similar speed patterns. Large subject × condition interaction effects were found for all variables (p < 0.001). Only small subject effects were found. Conclusions: The results of the study reveal different walking patterns between FS and SP. However, the two sensitivity levels failed to differ much. More habituation time may be needed for subjects to learn to optimally respond to the SP algorithm. Future work should include training subjects for more natural responses, applying a feed-forward algorithm, and testing the effect of optic flow on FS and SP speed variation. / text

<p> The purpose of this project was to develop an Adaptive Control Algorithm for a CNC milling machine. The milling machine is controlled by a 2100A Hewlett Packard mini-computer. The Adaptive Control Software has to operate in unison with an already available Numerical Control Software. Both these programmes are stored in the computer and the computer operates on them with the aid of the interrupt pulses received from the Time Base Generator located in the Controller.</p> <p> The Adaptive Control Software should be capable of optimising the milling process, that is enabling the milling machine to operate at the highest feed-rate without violating or overriding the maximum permissible values of the horizontal force and torque acting on the cutter. These maximum values of the force and torque are determined from the tool strength and capacities of the servo drives and spindle motor. Further, the machine should be able to arrive at the above feed-rate in the shortest possible time interval without causing cyclic variations in the feed-rate which could lead to an unstable system. The programme should be able to obtain ten samples of the parameters per revolution of the spindle. The feed-rate thus obtained (after comparing with the maximum and minimum feed-rates of the machine and making any corrections, if necessary) should be stored in a memory location accessible to the Numerical Control Programme. The instantaneous values of the force and torque are transmitted to the computer via the transducers attached on the spindle of the machine and the Analog-to-Digital Processor, therefore, the Adaptive Control Software will have to communicate with the Analog-to-Digital Processor in order to receive the values of the forces and torque. Thus the above mentioned requirements will have to be met by this piece of software. With this end in view, the following algorithm was developed.</p> <p> The algorithm consists of two portions, namely, the Data Reading Routine and the Policy Routine. The former accepts the two horizontal forces (which are phase shifted by 90°) and the torque acting on the cutter by communicating with the Analog-to-Digital Processor. However, all these three parameters are received through the same channel from the Analog-to-Digital Processor as such a method of identifying the variables was necessary. For this purpose, the Data Reading Routine consists of software capable of communicating with the Analog-to-Digital Processor at time intervals of 10 m.sec. and receiving the data in a digital form, decoding the input and ascertaining which input parameter was received. The Policy Routine has two modes of operation viz., the constraint and optimizing modes. This routine ascertains the critical error and arrives at the new feed-rate depending on the Policy used. After checking the value of this feed-rate with the maximum and minimum feed-rates available on the machine (and corrections made if necessary), the suitable value of this feed-rate is stored in a memory location accessible to the Numerical Control programme. This gives the general structure of the Adaptive Control Algorithm developed in this project.</p> / Thesis / Master of Engineering (MEngr)

abstract: The students of Arizona State University, under the mentorship of Dr George Karady, have been collaborating with Salt River Project (SRP), a major power utility in the state of Arizona, trying to study and optimize a battery-supported grid-tied rooftop Photovoltaic (PV) system, sold by a commercial vendor. SRP believes this system has the potential to satisfy the needs of its customers, who opt for utilizing solar power to partially satisfy their power needs. An important part of this elaborate project is the development of a new load forecasting algorithm and a better control strategy for the optimized utilization of the storage system. The built-in algorithm of this commercial unit uses simple forecasting and battery control strategies. With the recent improvement in Machine Learning (ML) techniques, development of a more sophisticated model of the problem in hand was possible. This research is aimed at achieving the goal by utilizing the appropriate ML techniques to better model the problem, which will essentially result in a better solution. In this research, a set of six unique features are used to model the load forecasting problem and different ML algorithms are simulated on the developed model. A similar approach is taken to solve the PV prediction problem. Finally, a very effective battery control strategy is built (utilizing the results of the load and PV forecasting), with the aim of ensuring a reduction in the amount of energy consumed from the grid during the “on-peak” hours. Apart from the reduction in the energy consumption, this battery control algorithm decelerates the “cycling aging” or the aging of the battery owing to the charge/dis-charges cycles endured by selectively charging/dis-charging the battery based on need. ii The results of this proposed strategy are verified using a hardware implementation (the PV system was coupled with a custom-built load bank and this setup was used to simulate a house). The results pertaining to the performances of the built-in algorithm and the ML algorithm are compared and the economic analysis is performed. The findings of this research have in the process of being published in a reputed journal. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2018

A series of tests were performed on nickel-titanium alloy wire, also known as nitinol, to determine the plausibility of designing an actuator using this wire as the method of actuation. These tests have been designed to fully characterize how the wire behaves under steady state and transient conditions allowing for a specific wire selection to be made given known actuator specifications which will result in an efficient design. The wire transient data can be used to design a controller which reduces the actuation time. The research done for the overall project covers a wide scope including wire hysteresis, nitinol transition temperature, variable wire resistance, wire actuation as a function of current and pull force, cable fabrication, and wire actuation control to optimize performance. Using these test results, a prototype actuator has been designed using nitinol wire. It has been determined that an actuator can be efficiently designed using this material.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

This study describes the development of operational strategies for an exhaust air heat pump system that supplies space heating and domestic hot water. The system combines photovoltaic power production with two different storage types. These are electrical storage using batteries and thermal storage in using a domestic hot water tank and in form of the thermal capacity of the building. The investigation of the control strategies is carried out for a detailed single family house model in Sweden in the simulation software TRNSYS. The overall aim of the control strategies is to improve the performance of the energy system in terms of self-consumption, self-sufficiency, final energy and seasonal performance factor. Three algorithms are developed and compared to a base case without additional control. The first algorithm only uses the thermal storage in the hot water tank and the building. The second uses only the battery to store the photovoltaic electricity. The third control algorithm combines both storage types, electrical and thermal. The simulation results show that for the studied system the energetic improvement is higher with the use of electrical storage compared to using thermal storage. The biggest improvement however is reached with the third algorithm, using both storage types in combination. For the case of a photovoltaic-system with 9 kW and battery store with 10.8 kWh and a 180 l hot water store the self-consumption reaches up to 51% with a solar fraction of 41 %. The reduction in final energy consumption for this case is 3057 kWh (31 %) with the heat pump having a seasonal performance factor of 2.6. The highest self-consumption is reached with a photovoltaic-system of 3 kW and battery store with 3.6 kWh, which comes to 71 %.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

A Wheelchair-Mounted Robotic Arm (WMRA) was designed to aid people with limited or no upper-limb usage to accomplish activities of daily living (ADLs). The primary objective of this research was to enhance the performance of the WMRA by improving the communication protocols and functions between the hardware and software used for its control. Previously, the control algorithm of the robotic arm was tested in simulation and in the physical arm. These implementations required a combination of Matlab and C++ language and introduced some software instability under Windows operating system. To improve the performance of the WMRA, the programs for hardware control were separated from the ones intended for simulation. The control algorithm of the arm was rewritten using C++ language to facilitate the communication with the controller boards and to make the system more stable and reliable. As a result, the communication delays were decreased since the interfaces between different programs is no longer needed. Preliminary tests were performed to demonstrate the stability and reliability of the new control algorithm. The overall response of the control implementation was enhanced and the algorithm routines and optimization procedures achieved the same goals with more efficiency. Accuracy and repeatability tests were performed, and data was collected and analyzed.