Binyan Xu

Topic

An Integrated Fault-tolerant Model Predictive Control Framework for UAV Systems

Department of Mechanical Engineering

Date & location

• Thursday, July 4, 2024

• 9:30 A.M.

• Virtual Defence

Reviewers

Supervisory Committee

• Dr. Yang Shi, Department of Mechanical Engineering, University of Victoria (Co-Supervisor)

• Dr. Afzal Suleman, Department of Mechanical Engineering, UVic (Co-Supervisor)

• Dr. Pan Agathoklis, Department of Electrical and Computer Engineering, UVic (Outside Member) 

External Examiner

• Prof. Hugh H.T. Liu, Institute for Aerospace Studies, University of Toronto 

Chair of Oral Examination

• Dr. Rana El-Sabaawi, Department of Biology, UVic 

Abstract

The application of unmanned aerial vehicles (UAVs) has considerably expanded over the past few decades, driven by their flexibility, efficiency, cost-effectiveness, and distinct advantages in executing tasks within dangerous and inaccessible environments. As the demand for UAVs grows, so does the expectation for their autonomy and reliability. Therefore, there is a need to enhance the efficiency and safety of UAV control systems. 

This dissertation proposes the development of innovative control strategies applicable to both individual and multi-agent systems, aiming to effectively address control challenges in UAV applications, such as complex dynamics, inherent constraints, unexpected faults, and resource limitations. To achieve this objective, a unified framework to effectively integrate model predictive control (MPC) with fault tolerant control (FTC) is proposed, with the primary focus on identifying and ad dressing theoretical and practical challenges associated with this integration. 

The dissertation starts by providing a comprehensive introduction and systematic literature review, highlighting unresolved issues and gaps in fault-tolerant model predictive control (FTMPC). Essential mathematical preliminaries, including models and necessary theorems, are also discussed.

Next, a novel adaptive fault-tolerant MPC method for fault-tolerant tracking control of constrained nonlinear systems is presented. This design integrates an adaptive fault estimator into the Lyapunov-based MPC framework, thereby ensuring closed loop control performance and system stability in the presence of actuator faults with reduced computational complexity. 

The FTMPC framework is further extended by applying it to the trajectory tracking control problem of UAVs with input constraints and actuator faults. To tackle the unique UAV control challenges, it presents the design and stability analysis of a dual loop, dual-rate hierarchical UAV control system. By implementing MPC only to the outer-loop at a slower sampling rate, it significantly reduces the computational demands of solving the MPC problem while maintaining the rapid response capabilities of the inner loop. Furthermore, the dual-sampling-rate issue is rigorously evaluated in the closed-loop analysis using singular perturbation theory, providing important guidelines for selecting control parameters based on the sampling frequency. 

Furthermore, the fault-tolerant formation control problem of a multi-UAV system interconnected through a directed communication graph is investigated. With the developed adaptive distributed Lyapunov-based MPC method, the formation tracking control objective is achieved with partially known leader information and unexpected actuator faults. This design also significantly reduces communication and computational burdens by requiring only a single round of calculation and communication per control update.

 Finally, unknown communication faults between agents in a nonlinear multi-agent system are addressed, instead of only considering the actuator faults that only affect individual local agents. To this end, a novel adaptive distributed observer-based DMPC method is developed, enhancing the resilience of distributed formation tracking in the presence of communication faults. This strategy is able to simplify the complexity of local MPC design by decomposing the original formation tracking control problem into several fully localized tracking control problems.