Modeling and Analysis of Power Processing Systems (MAPPS). Volume 1: Technical report

Computer aided design and analysis techniques were applied to power processing equipment. Topics covered include: (1) discrete time domain analysis of switching regulators for performance analysis; (2) design optimization of power converters using augmented Lagrangian penalty function technique; (3) investigation of current-injected multiloop controlled switching regulators; and (4) application of optimization for Navy VSTOL energy power system. The generation of the mathematical models and the development and application of computer aided design techniques to solve the different mathematical models are discussed. Recommendations are made for future work that would enhance the application of the computer aided design techniques for power processing systems

Transient stability emergency control combining open-loop and closed-loop technique

peer reviewedAn on-line transient stability emergency control approach is proposed, which couples an open-loop and a closed-loop emergency control technique. The open-loop technique uses on-line transient stability assessment in order to adapt the settings of automatic system protection schemes to the current operating conditions. On the other hand, the closed-loop technique uses measurements in order to design and trigger countermeasures, after the contingency has actually happened, then to continue monitoring in a closed-loop fashion. The approach aims at combining advantages of event-based and measurement-based system protection schemes, namely, speed of action and robustness with respect to uncertainties in system modeling. It can also comply with economic criteria

Modeling and analysis of power processing systems: Feasibility investigation and formulation of a methodology

A review is given of future power processing systems planned for the next 20 years, and the state-of-the-art of power processing design modeling and analysis techniques used to optimize power processing systems. A methodology of modeling and analysis of power processing equipment and systems has been formulated to fulfill future tradeoff studies and optimization requirements. Computer techniques were applied to simulate power processor performance and to optimize the design of power processing equipment. A program plan to systematically develop and apply the tools for power processing systems modeling and analysis is presented so that meaningful results can be obtained each year to aid the power processing system engineer and power processing equipment circuit designers in their conceptual and detail design and analysis tasks

Control techniques for power system stabilisation

The conventional PSS was first proposed earlier based on a linear model of the power system to damp the low frequency oscillations in the system. But they are designed to be operated under fixed parameters derived from the system linearized model. Due to large interconnection of power system to meet the load demand brings in deviations of steady-state and non-linearity to power system. The main problem is that PSS includes the locally measured quantities only neglecting the effect of nearby generators. This is the reason for the advent of Wide area monitoring for strong coupling between the local modes and the inter-area modes which would make the tuning of local PSSs for damping all modes nearly impossible when there is no supervisory level controller. Wide area control addresses these problems by proposing smart topology changes and control actions. Dynamic islanding and fast load shedding are schemes available to maintain as much as possible healthy transmission system. It is found that if remote signals from one or more distant locations of the power system can be applied to local controller design, system dynamic performance can be enhanced. In order to attain these goals, it is desirable to systematically build a robust wide area controller model within an autonomous system framework

Adaptive and Robust Braking-Traction Control Systems

The designs of commercial Anti-Lock Braking Systems often rely on assumptions of a torsionally rigid tire-wheel system and heavily rely on hub-mounted wheel speed sensors to manage tire-road slip conditions. However, advancements in high-bandwidth braking systems, in-wheel motors, variations in tire/wheel designs, and loss of inflation pressure, have produced scenarios where the tire\u27s torsional dynamics could be easily excited by the braking system actuator. In these scenarios, the slip conditions for the tire-belt/ring will be dynamically different from what can be inferred from the wheel speed sensors. This dissertation investigates the interaction of tire torsional dynamics with ABS & traction controllers and offers new control designs that incorporate schemes for identifying and accommodating these dynamics. To this end, suitable braking system and tire torsional dynamics simulation models as well as experimental test rigs were developed. It is found that, indeed, rigid-wheel based controllers give degraded performance when coupled with low torsional stiffness tires. A closed-loop observer/nonlinear controller structure is proposed that adapts to unknown tire sidewall and tread parameters during braking events. It also provides estimates of difficult to measure state variables such as belt/ring speed. The controller includes a novel virtual damper emulation that can be used to tune the system response. An adaptive sliding-mode controller is also introduced that combines robust stability characteristics with tire/tread parameter and state estimation. The sliding mode controller is shown to be very effective at tracking its estimated target, at the expense of reducing the tire parameter adaptation performance. Finally, a modular robust state observer is developed that allows for robust estimation of the system states in the presence of uncertainties and external disturbances without the need for sidewall parameter adaptation

Switching Adaptive Concurrent Learning Control for Powered Rehabilitation Machines with FES

Interfacing robotic devices with humans presents significant control challenges, as the control algorithms governing these machines must accommodate for the inherent variability among individuals. This requirement necessitates the system’s ability to adapt to changes in the environment, particularly in the context of human-in-the-loop applications, wherein the system must identify specific features of the human interacting with the machine. In the field of rehabilitation, one promising approach for exercise-based rehabilitation involves the integration of hybrid rehabilitation machines, combining robotic devices such as motorized bikes and exoskeletons with functional electrical stimulation (FES) applied on lower-limb muscles. This integrated approach offers the potential for repetitive training, reduced therapist workload, improved range of motion, and therapeutic benefits. However, conducting prolonged rehabilitation sessions to maximize functional recovery using these hybrid machines imposes several difficulties. Firstly, the design and analysis of adaptive controllers are motivated, but challenges exist in coping with the inherent switching effects associated with hybrid machines. Notably, the transitions between gait phases and the dynamic switching of inputs between active lower-limb muscles and electric motors and their incorporation in the control design remain an open problem for the research community. Secondly, the system must effectively compensate for the influence of human input, which can be viewed as an external disturbance in the closed-loop system during rehabilitation. Robust methods for understanding and adapting to the variations in human input are critical for ensuring stability and accurate control of the human-robot closed-loop system. Lastly, FES-induced muscle fatigue diminishes the human torque contribution to the rehabilitation task, leading to input saturation and potential instabilities as the duration of the exercise extends. Overcoming this challenge requires the development of control algorithms that can adapt to variations in human performance by dynamically adjusting the control parameters accordingly. Consequently, the development of rehabilitative devices that effectively interface with humans requires the design and implementation of control algorithms capable of adapting to users with varying muscle and kinematic characteristics. In this regard, adaptive-based control methods provide tools for addressing the uncertainties in human-robot dynamics within exercise-based rehabilitation using FES, while ensuring stability and robustness in the human-robot closed-loop system. This dissertation develops adaptive controllers to enhance the effectiveness of exercise-based rehabilitation using FES. The objectives include the design and evaluation of adaptive control algorithms that effectively handle the switching effects inherent in hybrid machines, adapt to compensate for human input, and account for input saturation due to muscle fatigue. The control designs leverage kinematic and torque feedback and ensure the stability of the human-robot closed-loop system. These controllers have the potential to significantly enhance the practicality and effectiveness of assistive technologies in both clinical and community settings. In Chapter 1, the motivation to design switching adaptive closed-loop controllers for motorized FES-cycling and powered exoskeletons is described. A survey of closed-loop kinematic control methods related to the tracking objectives in the subsequent chapters of the dissertation is also introduced. In Chapter 2, the dynamic models for cycling and bipedal walking are described: (i) a stationary FES-cycling model with nonlinear dynamics and switched control inputs are introduced based on published literature. The muscle stimulation pattern is defined based on the kinematic effectiveness of the rider, which depends on the crank angle. (ii) A phase-dependent bipedal walking system model with switched dynamics is introduced to control a 4-degrees-of-freedom (DoF) lower-limb exoskeleton assuming single stance support. Moreover, the experimental setup of the cycle-rider and lower-limb exoskeleton system are described. Chapter 3 presents a switched concurrent learning adaptive controller for cadence tracking using the cycle-rider model. The control design is decoupled for the muscles and electric motor. An FES controller is developed with minimal parameters, capable of generating bounded muscle responses with an adjustable saturation limit. The electric motor controller employs an adaptive-based method that estimates uncertain parameters in the cycle-rider system and leverages the muscle input as a feedforward term to improve the tracking of crank trajectories. The adaptive motor controller and saturated muscle controller are implemented in able-bodied individuals and people with movement disorders. Three cycling trials were conducted to demonstrate the feasibility of tracking different crank trajectories with the same set of control parameters across all participants. The developed adaptive controller requires minimal tuning and handles rider uncertainty while ensuring predictable and satisfactory performance. This result has the potential to facilitate the widespread implementation of adaptive closed-loop controllers for FES-cycling systems in real clinical and home-based scenarios. Chapter 4 presents an integral torque tracking controller with anti-windup compensation, which achieves the dual objectives of kinematic and torque tracking (i.e., power tracking) for FES cycling. Designing an integral torque tracking controller to avoid feedback of high-order derivatives poses a significant challenge, as the integration action in the muscle loop can induce error buildup; demanding high FES input on the muscle. This can cause discomfort and accelerate muscle fatigue, thereby limiting the practical utility of the power tracking controller. To address this issue, this chapter builds upon the adaptive control for cadence tracking developed in Chapter 3 and integrates a novel torque tracking controller that allows for input saturation in the FES controller. By doing so, the controller achieves cadence and torque tracking while preventing error buildup. The analysis rigorously considers the saturation effect, and preliminary experimental results in able-bodied individuals demonstrate its feasibility. In Chapter 5, a switched concurrent learning adaptive controller is developed to achieve kinematic tracking throughout the step cycle for treadmill-based walking with a 4-DoF lower-limb hybrid exoskeleton. The developed controller leverages a phase-dependent human-exoskeleton model presented in Chapter 2. A multiple-Lyapunov stability analysis with a dwell time condition is developed to ensure exponential kinematic tracking and parameter estimation. The controller is tested in two able-bodied individuals for a six-minute walking trial and the performance of the controller is compared with a gradient descent classical adaptive controller. Chapter 6 highlights the contributions of the developed control methods and provides recommendations for future research directions

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms