Power factor correction (PFC) circuits are widely utilized as alternatives to standard rectifier front ends, which typically suffer from very low power factor. As power quality standards are increased, interest in these PFC circuits has increased. A nonlinear controller is proposed for a Dual-Boost Bridgeless PFC circuit based on a Lyapunov stability analysis. The proposed scheme controls the inductor current and output voltage in the presence of unknown switching disturbances and an unknown current source load. A typical current source load driven by these circuits is a Voltage Source Inverter (VSI) with an RL load, which is used in the implementation of this PFC. Simulation results show a near-unity power factor at the source.

The de source in a standard single-phase dc:ac inverter experiences significant ripple current due to pulsations in the instantaneous power of the ac sink. Maintaining a constant dC bus in the presence of this ripple typically requires either a large bus capacitance, which is costly and will be highly stressed, or some additional circuitry to act as an energy buffer. In this work a buffer is proposed which consists of a boost converter in parallel with the single-phase grid-tied inverter. An instantaneous ripple current is calculated using information from the inverter controller, and a negated current is given as a reference to the boost converter. By separately considering the ac and dc dynamics of the boost converter, a novel controller is designed which achieves the input current reference tracking while also balancing the buffer capacitor. This control design is motivated by a Lyapunov stability analysis, which proves convergence of the relevant error signals. Simulation results are also provided to further validate the design.

The common refrigeration cycle requires a compressor unit to transition the working gas from low to high pressure. In applications where noise and vibration mitigation are desired there is motivation to achieve high fidelity speed regulation. This speed control is made difficult by the fact that as the gas is compressed it exerts a highly nonlinear force against the compressor piston. Though this effect is common to all compressors, the present work considers a rolling piston type rotary compressor. A nonlinear controller is proposed which utilizes the known geometries of a rolling piston compressor, as well as the thermodynamic properties of the working gas, to develop an adaptive load torque observer. This observer acts as a feedforward term in the control input to effectively cancel out the speed ripple induced by the load torque. This design is motivated by a Lyapunov-based stability analysis which proves that the combined controller and observer scheme reduces speed error to zero asymptotically. Simulation results provide further validation.

A visual servo tracking controller is developed in this paper for a monocular camera system mounted on an underactuated wheeled mobile robot (WMR) subject to nonholonomic motion constraints (i.e., the camera-in-hand problem). A prerecorded image sequence (e.g., a video) of three target points is used to define a desired trajectory for the WMR. By comparing the target points from a stationary reference image with the corresponding target points in the live image and the prerecorded sequence of images, projective geometric relationships are exploited to construct Euclidean homographies. The information obtained by decomposing the Euclidean homography is used to develop a kinematic controller. A Lyapunov-based analysis is used to develop an adaptive update law to actively compensate for the lack of depth information required for the translation error system. Experimental results are provided to demonstrate the control design.

Several factors must be considered for robotic task execution in the presence of a fault, including: detection, identification, and accommodation for the fault. In this paper, a nonlinear observer is used to identify a class of actuator faults once the fault has been detected by some other method. Advantages of the proposed fault-identification method are that it is based on the nonlinear dynamic model of a robot manipulator (and hence, can be extended to a number of general Euler Lagrange systems), it does not require acceleration measurements, and it is independent from the controller. A Lyapunov-based analysis is provided to prove that the developed fault observer converges to the actual fault. Experimental results are provided to illustrate the performance of the identification method.

Several factors must be considered for robotic task execution in the presence of a fault, including: detection, identification, and accommodation for the fault. In this paper, a prediction error based dead-zone residual function and nonlinear observers are used to detect and identify a class of actuator faults. Advantages of the proposed fault detection and identification methods are that they are based on the nonlinear dynamic model of a robot manipulator (and hence, can be extended to a number of general Euler Lagrange systems), they do not require acceleration measurements, and they are independent from the controller. A Lyapunov-based analysis is provided to prove that the developed fault observer converges to the actual fault.

A visual servo tracking controller is developed in this paper for a monocular camera system mounted on an underactuated wheeled robot (WMR) subject nonholonomic motion constraints (i.e., the camera-in-hand problem). A prerecorded image sequence (e.g., a video) of three target points is used to define a desired trajectory for the WMR. By comparing the target points from the prerecorded sequence with the corresponding target points in the live image, projective geometric relationships are exploited to construct a Euclidean homography. The information obtained by decomposing the Euclidean homography is used to develop a kinematic controller. A Lyapunov-based analysis is used to develop an adaptive update law to actively compensate for the lack of depth information required for the translation error system.