256 research outputs found
Event-triggering architectures for adaptive control of uncertain dynamical systems
In this dissertation, new approaches are presented for the design and implementation of networked adaptive control systems to reduce the wireless network utilization while guaranteeing system stability in the presence of system uncertainties. Specifically, the design and analysis of state feedback adaptive control systems over wireless networks using event-triggering control theory is first presented. The state feedback adaptive control results are then generalized to the output feedback case for dynamical systems with unmeasurable state vectors. This event-triggering approach is then adopted for large-scale uncertain dynamical systems. In particular, decentralized and distributed adaptive control methodologies are proposed with reduced wireless network utilization with stability guarantees.
In addition, for systems in the absence of uncertainties, a new observer-free output feedback cooperative control architecture is developed. Specifically, the proposed architecture is predicated on a nonminimal state-space realization that generates an expanded set of states only using the filtered input and filtered output and their derivatives for each vehicle, without the need for designing an observer for each vehicle. Building on the results of this new observer-free output feedback cooperative control architecture, an event-triggering methodology is next proposed for the output feedback cooperative control to schedule the exchanged output measurements information between the agents in order to reduce wireless network utilization. Finally, the output feedback cooperative control architecture is generalized to adaptive control for handling exogenous disturbances in the follower vehicles.
For each methodology, the closed-loop system stability properties are rigorously analyzed, the effect of the user-defined event-triggering thresholds and the controller design parameters on the overall system performance are characterized, and Zeno behavior is shown not to occur with the proposed algorithms --Abstract, page iv
Robust Distributed Stabilization of Interconnected Multiagent Systems
Many large-scale systems can be modeled as groups of individual dynamics, e.g., multi-vehicle systems, as well as interconnected multiagent systems, power systems and biological networks as a few examples. Due to the high-dimension and complexity in configuration of these infrastructures, only a few internal variables of each agent might be measurable and the exact knowledge of the model might be unavailable for the control design purpose. The collective objectives may range from consensus to decoupling, stabilization, reference tracking, and global performance guarantees. Depending on the objectives, the designer may choose agent-level low-dimension or multiagent system-level high-dimension approaches to develop distributed algorithms. With an inappropriately designed algorithm, the effect of modeling uncertainty may propagate over the communication and coupling topologies and degrade the overall performance of the system. We address this problem by proposing single- and multi-layer structures. The former is used for both individual and interconnected multiagent systems. The latter, inspired by cyber-physical systems, is devoted to the interconnected multiagent systems. We focus on developing a single control-theoretic tool to be used for the relative information-based distributed control design purpose for any combinations of the aforementioned configuration, objective, and approach. This systematic framework guarantees robust stability and performance of the closed-loop multiagent systems. We validate these theoretical results through various simulation studies
Design, Implementation and Testing of Advanced Control Laws for Fixed-wing UAVs
The present PhD thesis addresses the problem of the control of small fixed-wing Unmanned
Aerial Vehicles (UAVs). In the scientific community much research is dedicated to the study
of suitable control laws for this category of aircraft. This interest is motivated by the several
applications that these platforms can perform and by their peculiarities as dynamical systems.
In fact, small UAVs are characterized by highly nonlinear behavior, strong coupling between
longitudinal and latero-directional planes, and high sensitivity to external disturbances and
to parametric uncertainties. Furthermore, the challenge is increased by the limited space
and weight available for the onboard electronics. The aim of this PhD thesis is to provide a
valid confrontation among three different control techniques and to introduce an innovative
autopilot configuration suitable for the unmanned aircraft field.
Three advanced controllers for fixed-wing unmanned aircraft vehicles are designed and
implemented: PID with H1 robust approach, L1 adaptive controller and nonlinear backstepping
controller. All of them are analyzed from the theoretical point of view and validated
through numerical simulations with a mathematical UAV model. One is implemented on a
microcontroller board, validated through hardware simulations and tested in
flight.
The PID with H1 robust approach is used for the definition of the gains of a commercial
autopilot. The proposed technique combines traditional PID control with an H1 loop
shaping method to assess the robustness characteristics achievable with simple PID gains.
It is demonstrated that this hybrid approach provides a promising solution to the problem
of tuning commercial autopilots for UAVs. Nevertheless, it is clear that a tradeoff between
robustness and performance is necessary when dealing with this standard control technique.
The robustness problem is effectively solved by the adoption of an L1 adaptive controller
for complete aircraft control. In particular, the L1 logic here adopted is based on piecewise
constant adaptive laws with an adaptation rate compatible with the sampling rate of an autopilot
board CPU. The control scheme includes an L1 adaptive controller for the inner loop,
while PID gains take care of the outer loop. The global controller is tuned on a linear decoupled
aircraft model. It is demonstrated that the achieved configuration guarantees satisfying
performance also when applied to a complete nonlinear model affected by uncertainties and parametric perturbations.
The third controller implemented is based on an existing nonlinear backstepping technique.
A scheme for longitudinal and latero-directional control based on the combination of
PID for the outer loop and backstepping for the inner loop is proposed. Satisfying results are
achieved also when the nonlinear aircraft model is perturbed by parametric uncertainties. A
confrontation among the three controllers shows that L1 and backstepping are comparable
in terms of nominal and robust performance, with an advantage for L1, while the PID is
always inferior.
The backstepping controller is chosen for being implemented and tested on a real fixed-wing
RC aircraft. Hardware-in-the-loop simulations validate its real-time control capability
on the complete nonlinear model of the aircraft adopted for the tests, inclusive of sensors
noise. An innovative microcontroller technology is employed as core of the autopilot system,
it interfaces with sensors and servos in order to handle input/output operations and it
performs the control law computation. Preliminary ground tests validate the suitability of
the autopilot configuration. A limited number of flight tests is performed. Promising results
are obtained for the control of longitudinal states, while latero-directional control still needs
major improvements
Robust fault tolerant control of induction motor system
Research into fault tolerant control (FTC, a set of techniques that are developed to increase plant availability and reduce the risk of safety hazards) for induction motors is motivated by practical concerns including the need for enhanced reliability, improved maintenance operations and reduced cost. Its aim is to prevent that simple faults develop into serious failure. Although, the subject of induction motor control is well known, the main topics in the literature are concerned with scalar and vector control and structural stability. However, induction machines experience various fault scenarios and to meet the above requirements FTC strategies based on existing or more advanced control methods become desirable. Some earlier studies on FTC have addressed particular problems of 3-phase sensor current/voltage FTC, torque FTC, etc. However, the development of these methods lacks a more general understanding of the overall problem of FTC for an induction motor based on a true fault classification of possible fault types.In order to develop a more general approach to FTC for induction motors, i.e. not just designing specific control approaches for individual induction motor fault scenarios, this thesis has carried out a systematic research on induction motor systems considering the various faults that can typically be present, having either “additive” fault or “multiplicative” effects on the system dynamics, according to whether the faults are sensor or actuator (additive fault) types or component or motor faults (multiplicative fault) types.To achieve the required objectives, an active approach to FTC is used, making use of fault estimation (FE, an approach that determine the magnitude of a fault signal online) and fault compensation. This approach of FTC/FE considers an integration of the electrical and mechanical dynamics, initially using adaptive and/or sliding mode observers, Linear Parameter Varying (LPV, in which nonlinear systems are locally decomposed into several linear systems scheduled by varying parameters) and then using back-stepping control combined with observer/estimation methods for handling certain forms of nonlinearity.In conclusion, the thesis proposed an integrated research of induction motor FTC/FE with the consideration of different types of faults and different types of uncertainties, and validated the approaches through simulations and experiments
Output feedback sliding mode control for time delay systems
This Thesis considers Sliding Mode Control (SMC) for linear systems subjected to uncertainties and delays using output feedback. Delay is a natural phenomenon in many practical systems, the effect of delay can be the potential cause -of performance deterioration or even instability. To achieve better control performance, SMC with output feedback is considered for its inherent robustness feature and practicality for implementation. In highlighting the main results, firstly a novel output feedback SMC design is presented which formulates the problem into Linear Matrix Inequalities (LMIs). The efficiency of the design is compared with the the existing literature in pole assignment. eigenstructure assignment and other LMI methods, which either require more constraints on system structures or are computationally less tractable. For systems with timevarying Slate delay, the method is extended to incorporate the delay effect in the controUer synthesis. Both sliding surface and controller design are formulated as LMI problems. For systems with input/output delays and disturbances. the robustness of SMC is degraded with arbitrarily small delay appearing in the high frequency switching component of the controller. To solve the problem singular perturbation method is used to achieve bounded performance which is proportional to the magnitudes of delay, disturbance and switching gain. The applied research has produced two practical implementation studies. Firstly it relates to the pointing control of an autonomous vehicle subjected to external disturbances and friction resulting from the motion of the vehicle crossing rough terrain. The second implementation concerns the attitude control of a flexible spacecraft with respect to roil, pitch and yaw attitude angles
Direct Adaptive Control for a Trajectory Tracking UAV
This research focuses on the theoretical development and analysis of a direct adaptive control algorithm to enable a fixed-wing UAV to track reference trajectories while in the presence of persistent external disturbances. A typical application of this work is autonomous flight through urban environments, where reference trajectories would be provided by a path planning algorithm and the vehicle would be subjected to significant wind gust disturbances. Full 6-DOF nonlinear and linear UAV simulation models are developed and used to study the performance of the direct adaptive control system for various scenarios. A stability proof is developed to prove convergence of the direct adaptive control system under certain conditions. Specific adaptive controller implementation details are provided, including the use of a sensor blending algorithm to address the non-minimum phase properties of the UAV models. The robustness of the adaptive system pertaining to the amount of modeling error that can be accommodated by the controller is studied, and the disturbance rejection capabilities and limitations of the controllers are also analyzed. The overall results of this research demonstrate that the direct adaptive control algorithm can enable trajectory tracking in cases where there are both significant uncertainties in the external disturbances and considerable error in the UAV model
- …