Dissertations / Theses on the topic 'Control engineering, mechatronics and robotics'

Several algorithms for multi-robot coordination assume that the communication network of a team of mobile robots is connected, so that information can be exchanged between any two robots in the team. The network topology is often state-dependent, and thus the robots may move in a way that causes the network to become disconnected. This dissertation proposes continuous time control laws that preserve the connectivity for both undirected and directed mobile robot networks, which can be used along with additional task-dependent control actions. An additional aim of the dissertation is to provide control algorithms to achieve multi-robot coordination methods using vision-based feedback. Feedback control laws are presented that achieve tracking of desired relative positions between a pair of non-holonomic mobile robots using relative position measurements only. Issues pertaining to vision-based implementation of the connectivity control laws are discussed, and solutions are presented.

Mobile robots are un-manned systems and must be able to overcome the obstacles they face without human intervention. In this research we have proposed a novel retractable mechanism which converts a linear hydraulic motion into expansion of six claws at each wheel. In order to control the expansion, optimal control theory was applied and a constrained optimization problem was defined using different Simulink toolboxes and applied to the mechanism modeled in SimHydraulics/SimMechanics. The main benefit of performing both hydraulic and mechanical system simulation and also control design in the same environment is to eliminate mathematical approximation of the model and using existing Simulink simulation tools for simulation and control. Systems were modeled and controller optimization was performed. Simulation results are illustrated at the end, which show the method's versatility and reliability for controlling the mechanism operation.

Cables are used in various fields like construction, sports, communication, and transportation. Two important fields where cables are used for this research are transportation and high-voltage power lines. In the field of transportation, cables are used in cable-riding systems like cable cars, tramways, and gondola lifts. Vibration is induced in these systems. These vibrations are undesirable.

High-voltage power line cables are important part of our lives. Inspection of these power lines is necessary to eliminate the risk of power outages. Earlier power lines were inspected by skilled humans by crawling along the power lines. This inspection task was replaced by cable riding inspection robots to eliminate the risk to human life.

This research explains various inspection robots and problems in these types of cable-riding systems. In this research, to determine the vibrations in the cable due to riding load, a new simple cable-mass system was developed. This new cable-mass system makes it easy to develop a control system to reduce the cable vibrations.

To inspect the power lines, HiBot developed an inspection robot called Expliner that travels along the live power lines. For uninterrupted inspection operation, Expliner traverses obstacles on the power lines with its intelligent acrobatic mode. In this mode, Expliner is subjected to rocking oscillation. This rocking oscillation is also induced in the cables cars. This rocking is undesirable and is unsafe.

This research introduces a method to reduce rocking in cable-riding systems. This method, called input shaping, is used to run simulations for Expliner traveling along curved cables. This research develops an input shaper to reduce rocking in cable-riding robots like Expliner.

We present the background and motivation for ground vehicle autonomy, and focus on uses for space-exploration. Using a simple design example of an autonomous ground vehicle we derive the equations of motion. After providing the mathematical background for nonlinear systems and control we present two common methods for exactly linearizing nonlinear systems, feedback linearization and backstepping. We use these in combination with three adaptive control methods: model reference adaptive control, adaptive sliding mode control, and extremum-seeking model reference adaptive control. We show the performances of each combination through several simulation results. We then consider disturbances in the system, and design nonlinear disturbance observers for both single-input-single-output and multi-input-multi-output systems. Finally, we show the performance of these observers with simulation results.

The communications and electronic systems that comprise a distributed control architecture for a robotic manipulator tie the high level control and motion planning to the electromechanical components. Custom solutions to this problem can be expensive in terms of time, cost, and maintenance. The integration of commercial off the shelf (COTS) motion controllers, combined with a robust communication standard, offers the potential to reduce the costs and development times for new robots. This thesis demonstrates an implementation of this architecture using commercial controllers and the CANopen communications bus on two existing dexterous robots. Testing is conducted to quantify the single joint performance of these modules. Additionally, the implementation of the system on a second robot arm was conducted in order to test the flexibility of the system for use with different actuators and feedback.

Compliance in legged robotics has recently become favored over rigid leg members. The energy cost associated with rigid legged walking is high, and speeds are typically decreased with terrain changes. The use of compliant legs can provide stable walking while enabling higher maximum speeds.

Inspired by biological principals in insectoid locomotion, a physical model for energy efficient limb design was explored. A compliant leg structure was designed and replaced the rigid leg members of an insectoid robot. Velocity and ground reaction force data was gathered for both rigid and compliant lower leg members at various stiffness values for ideal and complex terrain.

Furthermore, a command was designed to maximize walking speeds. Design parameters of stiffness, command duration, and command spacing are selected. The designed command, combining the optimal value of the design parameters, produced an increased stance departure velocity value and can be implemented in insectoid robots with compliant legs.

Over the years, researchers have been able to prove Brain Computer Interface (BCI) -P300 Speller as an effective communication tool. The first P300 speller was developed by Farwell and Donchin (1988), using the oddball paradigm to evoke a P300 response from a speller matrix. This P300 speller matrix has been a strong basis for studies that aimed at using BCI-P300 protocol for spelling, cursor movement, internet navigation or even control and manipulation of devices. However, application of P300 based BCI to controlling and manipulation of devices often involves the user relating with multiple interfaces. These multiple interfaces could be a distraction or have negative effects on the user (Fazel-Rezai et al. 2012) and as a consequence hinders the evoking of P300 potential and causing inaccurate classification. For this research, a novel P300 control matrix is developed by replacing the alphabets in the traditional P300 speller matrix with arrow images. Then the novel P300 control matrix was investigated to compare the P300 latency and amplitude to that of the traditional P300 speller matrix. The elements in the novel P300 control matrix were in form of arrows facing upward, left, right and downward directions, while elements in the P300 speller matrix were alphabets U, L, R and D for the upward, left, right and downward directions respectively. The participants were presented with a set of randomly sequenced directions, and each participant decides which of the arrows or letters to focus on based on the direction presented to them. Electroencephalography (EEG) was used to record the brainwaves using the international 10-20 system of electrode placement. This research is potentially a more efficient approach for controlling devices using P300-based BCI systems by eradicating the need for multiple interfaces associated with BCI-robotic control systems that are based on P300 speller.

Prior work at Marquette University developed the Marquette Prosthesis, an active transtibial prosthesis that utilized a torsional spring and a four-bar mechanism. The controls for the Marquette Prosthesis implemented a finite state control algorithm to determine the state of gait of the amputee along with two lower level controllers, a PI moment controller to control the moment during stance and a PID position controller to control the position during stance. The Marquette Prosthesis was successful in mimicking the gait profile presented by Winter. However, after completing human subject testing, the Marquette Prosthesis was insufficient in trying to match the gait profile of those who varied from this textbook stride.

Active transtibial prostheses typically apply finite state control algorithms that struggle with cadence and gait variability of the amputee. Recent work in artificial neural networks (ANN) have shown the possibility to predict the user's intent which can be used as an input signal in an improved controller. The Marquette Prosthesis II was developed that uses a stiffness controller to control the relationship between the position and torque of the ankle. A model of the improved Marquette Prosthesis II was developed in Simulink to ensure that the stiffness controller was robust enough and that this type of control was possible with the limitations of the Marquette Prosthesis, i.e., the link lengths, torsional spring and motor. The mechanical system of the Marquette Prosthesis was then changed such that the spring was in series between the motor and four-bar mechanism to establish a relationship between the motor position, torque of the spring and four-bar mechanism. The control hardware was selected and the stiffness controller was implemented on the Marquette Prosthesis II. The Marquette Prosthesis II control algorithm was tested and validated to show that this approach is feasible.

Control of robot manipulators can be greatly improved with the use of velocity and torque feedforward control. However, the effectiveness of feedforward control greatly relies on the accuracy of the model. In this study, kinematics and dynamics analysis is performed on a six axis arm, a Delta2 robot, and a Delta3 robot. Velocity feedforward calculation is performed using the traditional means of using the kinematics solution for velocity. However, a neural network is used to model the torque feedforward equations. For each of these mechanisms, we first solve the forward and inverse kinematics transformations. We then derive a dynamic model. Later, unlike traditional methods of obtaining the dynamics parameters of the dynamics model, the dynamics model is used to infer dependencies between the input and output variables for neural network torque estimation. The neural network is trained with joint positions, velocities, and accelerations as inputs, and joint torques as outputs. After training is complete, the neural network is used to estimate the feedforward torque effort. Additionally, an investigation is done on the use of neural networks for deriving the inverse kinematics solution of a six axis arm. Although the neural network demonstrated outstanding ability to model complex mathematical equations, the inverse kinematics solution was not accurate enough for practical use.