A digital motor controller for student lab use is designed, built, and tested. The controller uses an encoder for position measurement, and an H-bridge to drive the electromechanical plant. A user interface is created to enhance usability of the device. The user interface is able to display key parameters of the control algorithm as well as the state of the system. It is also used to modify the gains and sample rate of the control algorithm. The design of the system is refined, and 10 units are built for the EE472 Digital Controls Lab. The lab manuals for the first 4 experiments are revised to match and support the new system. The possible future for the project is described with some suggestions for improving the system.

In recent years, multi-robot systems have been widely used in many applications such as warehouse inventory tracking and automatic search and rescue operations. Probability roadmap (PRM) is a typical path planning algorithm that can determine an optimal trajectory once the robot start and goal positions are specified. However, when the number of robots in the system increases, it converges slowly and may even fail to find the solution. In this thesis, a fuzzy inference system is proposed and combined with the probability roadmap algorithm for robot path planning. Computer simulation results in five different environments show this approach is very effective to reduce computation cost in most cases for multi-robot systems of various sizes. It is able to reduce the number of collision checks by at least 27% with a trade off of increased average path length of 12% at the most. It is also noticed that the proposed fuzzy system is not advantageous when combined with the sub-dimension expansion algorithm. More research will be conducted in the future to further improve the performance of the proposed fuzzy inference system.

The focus of this thesis is to research, analyze, and design a reliable and economical control system for the Cal Poly Wind Energy Capture System (WECS). A dynamic permanent magnet generator model is adopted from [1] and [2] and combined with an existing wind turbine model to create a non-linear time varying model in MATLAB. The model is then used to analyze potentially harmful electrical disturbances, and to define safe operating limits for the WECS. An optimal operating point controller utilizing a PID speed loop is designed with combined optimization criteria and the final controller design is justified by comparing performance measures of energy efficiency and mitigation of mechanical loads. The report also discusses implications for a WECS when blade characteristics are mismatched with the generator. Finally, possible ways to improve the performance of the Cal Poly WECS are addressed.

Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system.

In order for an aircraft to remain in stable flight, the center of gravity (CG) of an aircraft must be located in front of the center of lift (CL). As the center of gravity moves rearward, pitch stability decreases and the sensitivity to control input increases. This increase in sensitivity is known as pitch gain variance. Minimizing the pitch gain variance results in an aircraft with consistent handling characteristics across a broad range of center of gravity locations. This thesis focuses on the development and testing of an open loop computer simulation model and a closed loop control system to minimize pitch axis gain variation as center of gravity changes. DATCOM and MatLab are used to generate the open loop aircraft flight model; then a closed loop PD (proportional-derivate) controller is designed based on Ziegler-Nichols closed loop tuning methods. Computer simulation results show that the open loop control system exhibited unacceptable pitch gain variance, and that the closed loop control system not only minimizes gain variance, but also stabilizes the aircraft in all test cases. The controller is also implemented in a Scorpio Miss 2 radio controlled aircraft using an onboard microprocessor. Flight testing shows that performance is satisfactory.

<p>This thesis presents a method for the design of a predictive controller with variable step sizes.Predictive methods such as receding horizon control (or model predictive control) use aa fixed sampling frequency when updating the inputs. In the proposed method, the switchingtimes are incorporated into an optimization problem, thus resulting in anadaptive step-size control process. The controller with variable timesteps is shown to require less tuning and to reduce the number of expensive model evaluations.An alternate solution approach had to be developed to accommodate the new problem formulation.The controller's stability is proven in a context that does not require terminal cost or constraints.The thesis presents examples that compare the performance of the variable switching time controllerwith the receding horizon method with a fixed step size. This research opens many roads for futureextension of the theoretical work and practical applications of the controller.</p> / Doctor of Science (PhD)

<p>Have a nice day :)</p> / <p>This thesis presents a unified framework for coordination and control of human-in-the-loop asymmetric semi-autonomous robotic systems. It introduces a highly general teleoperation system configuration involving any number of operators, haptic interfaces, and robots with possibly different degrees of mobility. The proposed framework allows for mixed teleoperation/autonomous control of user-defined subtasks by establishing position/force tracking as well as kinematic constraints among relevant <em>teleoperation control frames</em>. Three layers of velocity-based autonomous subtasks at different priority levels with respect to human teleoperation are integrated into the control system design. The control strategy is hierarchical comprising of a high-level teleoperation coordinating controller and low-level joint velocity controllers. A Lyapunov-based adaptive joint-space velocity controller is presented as one candidate for the low-level control. The approach utilizes idempotent, generalized pseudoinverse and weighting matrices, as well as a soft-switching rank changing algorithm in order to achieve new performance objectives that are defined for such asymmetric semi-autonomous teleoperation systems. A detailed analysis of system performance and stability is presented. The proposed framework constitutes the most general formulation and solution for the teleoperation control problem to date. It yields many interesting and useful system configurations never studied before, in addition to those already considered in the literature. In particular, seven system configurations arising from the proposed teleoperation architecture are analyzed and studied in detail. Experimental results are provided to demonstrate the desired system response in these configurations. Moreover, human factors experiments are carried out to assess operator(s) performance in maneuverability and grasping under various teleoperation system configurations. The results show statistically significant performance improvement in teleoperation of a nonholonomic mobile robot and telegrasping using a twin-armed manipulator.</p> / Doctor of Philosophy (PhD)

<p>In this thesis, the problem of estimating the clutter spatial intensity function for the multitarget tracking algorithms has been considered. In many scenarios, after the signal detection process, measurement points provided by the sensor (e.g., sonar, infrared sensor, radar) are not distributed uniformly in the surveillance region as assumed by most tracking algorithms. On the other hand, in order to obtain accurate results, the multitarget tracking algorithm requires information about clutter’s spatial intensity. Thus, non-homogeneous clutter spatial intensity has to be estimated from the measurement set and the tracking filter’s output. Also, in order to take advantage of existing tracking algorithms, it is desirable for the clutter estimation method to be integrated into the tracker itself. In this thesis, the clutter is modeled by a non-homogeneous Poisson point (NHPP) process with a spatial intensity function g(z). To calculate the value of the clutter spatial intensity, all we need to do is estimating g(z). First, two new methods for joint spatial clutter intensity estimation and multitarget tracking using the Probability Hypothesis Density (PHD) Filter are presented. Then, based on NHPP process, multitarget multi-Bernoulli processes and set calculus, the approximated Bayesian method is extended to joint the non–homogeneous clutter background estimation and multitarget tracking with standard multitarget tracking algorithms, like the Multiple Hypothesis Tracking (MHT) and the Joint Integrated Probabilistic Data Association (JIPDA) tracker. Finally, a kernel density method is proposed for the clutter spatial intensity estimation problem. Simulation results illustrate the performance of the above algorithms, both in terms of the false track number and the true track initialization speed. All proposed algorithms show the ability to improve the performance of the multitarget tracker in the presence of slowly time varying non–homogeneous clutter background.</p> / Doctor of Philosophy (PhD)

<p>Driver Assistance Systems like Adaptive Cruise Control (ACC) can help prevent accidents by reducing the workload on the driver. However, this can only be accomplished if the driver can rely on the system to perform safely even in the presence of faults.</p> <p>In this thesis we develop an Adaptive Cruise Control model that will be used to investigate Hybrid Systems Fault Diagnosis techniques. System Identification is performed upon an electric motor to obtain its transfer function. This electric motor belongs to a 1/10th scale RC car that is being used as part of a test bench for the Adaptive Cruise Control system. The identified model is then used to design a hybrid controller which will switch between a set of LQR controllers to create an example Adaptive Cruise Controller. The model of the controller is then used to generate fixed point code for implementation on the testbed and validation against the model controller. Finally a detailed hazard analysis of the resulting system is performed using Leveson's STPA.</p> / Master of Applied Science (MASc)

<p>Robotic platforms have several characteristics such as speed and precision that make them enticing for use in medical procedures. Companies such as Intuitive Medical and Titan Medical have taken advantage of these features to introduce surgical robots for minimally invasive procedures. Such robots aim to reduce procedure and patient recovery times. Current technology requires platforms to be master-slave manipulators controlled by a surgeon, effectively converting the robot into an expensive surgical tool. Research into the interaction between robotic platforms and deformable objects such as human tissue is necessary in the development of autonomous and semi-autonomous surgical systems. This thesis investigates a class of robust linear controllers based on a worst case performance measure known as the $H_{\infty}$ norm, for the purpose of performing so called Indirect Deformable Object Manipulation (IDOM). This task allows positional regulation of regions of interest in a deformable object without directly interacting with them, enabling tasks such as stabilization of tumors during biopsies or automatic suturing. A complete approach to generating linear $H_{\infty}$ based controllers is presented, from derivation of a plant model to the actual synthesis of the controller. The introduction of model uncertainty requires $\mu$ synthesis techniques, which extend $H_{\infty}$ designs to produce highly robust controller solutions. In addition to $H_{\infty}$ and $\mu$ synthesis designs, the thesis presents an approach to design an optimal PID controller with gains that minimize the $H_{\infty}$ norm of a weighted plant. The three control approaches are simulated performing set point regulation in $\text{MATLAB}^{TM}$'s $simulink$. Simulations included disturbance inputs and noises to test stability and robustness of the approaches. $H_{\infty}$ controllers had the best robust performance of the controllers simulated, although all controllers simulated were stable. The $H_{\infty}$ and PID controllers were validated in an experimental setting, with experiments performed on two different deformable synthetic materials. It was found that $H_{\infty}$ techniques were highly robust and provided good tracking performance for a material that behaved in a relatively elastic manner, but failed to track well when applied to a highly nonlinear rubber compound. PID based control was outperformed by $H_{\infty}$ control in experiments performed on the elastic material, but proved to be superior when faced with the nonlinear material. These experimental findings are discussed and a linear $H_{\infty}$ control design approach is proposed.</p> / Master of Applied Science (MASc)