Robust adaptive sampled-data control design for MIMO systems: Applications in cyber-physical security

This dissertation extends the L1 adaptive control theory to sampled-data (SD) framework. Multi-input multi-output non-square (underactuated) systems are considered with different sampling rates for inputs and outputs. The sampled-data framework allows to address non-minimum phase systems, subject to less restrictive assumptions as compared to continuous time framework. It is shown that the closed-loop system can recover the response of a continuous-time reference system as the sampling time of the SD controller tends to zero. In this thesis, the L1 sampled data adaptive controller is integrated with the Simplex fault-tolerant architecture for resilient control of cyber-physical systems (CPSs). Detection and mitigation of zero-dynamics attacks are addressed and validated in flight tests of a quadrotor in Intelligent Robotics Laboratory of UIUC. The experiments show that the multirate L1 controller can e effectively detect stealthy zero-dynamics attacks and recover the stability of the perturbed system, where the single-rate conventional L1 adaptive controller fails. From the perspective of applications, the dissertation considers navigation and control of autonomous vehicles and proposes a two-loop framework, in which the high-level reference commands are limited by a saturation function, while the low-level controller tracks the reference by compensating for disturbances and uncertainties. A class of nested, uncertain, multi-input multi-output (MIMO) systems subject to reference command saturation, possibly with non-minimum phase zeros, is considered. Robust stability and performance of the overall closed-loop system with command saturation and multirate L1 adaptive controller are analyzed. Finally, a systematic analysis and synthesis method is proposed for the optimal design of filters in the L1 adaptive output-feedback structure, where the lowpass filter is the key to the trade-off between the performance and robustness of the closed-loop system. An optimization problem is formulated using the constraint on the input time-delay margin and a cost-function based on mixed L1/H2-norm performance measure. The optimization problem can be efficiently solved using linear/quadratic programming. We note that the framework of this dissertation and the multi-loop problem formulation of navigation and control of autonomous systems provide suitable synthesis and analysis tools for autonomous cyber-physical systems (CPSs), including self-driving cars, unmanned aerial vehicles (UAVs), and industrial/medical robots, to name just a few. The SD design facilitates the implementation of control laws on digital computers in CPSs, where the input/output signals are available at discrete time instances with different sampling rates

Data-Driven Architecture to Increase Resilience In Multi-Agent Coordinated Missions

The rise in the use of Multi-Agent Systems (MASs) in unpredictable and changing environments has created the need for intelligent algorithms to increase their autonomy, safety and performance in the event of disturbances and threats. MASs are attractive for their flexibility, which also makes them prone to threats that may result from hardware failures (actuators, sensors, onboard computer, power source) and operational abnormal conditions (weather, GPS denied location, cyber-attacks). This dissertation presents research on a bio-inspired approach for resilience augmentation in MASs in the presence of disturbances and threats such as communication link and stealthy zero-dynamics attacks. An adaptive bio-inspired architecture is developed for distributed consensus algorithms to increase fault-tolerance in a network of multiple high-order nonlinear systems under directed fixed topologies. In similarity with the natural organisms’ ability to recognize and remember specific pathogens to generate its immunity, the immunity-based architecture consists of a Distributed Model-Reference Adaptive Control (DMRAC) with an Artificial Immune System (AIS) adaptation law integrated within a consensus protocol. Feedback linearization is used to modify the high-order nonlinear model into four decoupled linear subsystems. A stability proof of the adaptation law is conducted using Lyapunov methods and Jordan decomposition. The DMRAC is proven to be stable in the presence of external time-varying bounded disturbances and the tracking error trajectories are shown to be bounded. The effectiveness of the proposed architecture is examined through numerical simulations. The proposed controller successfully ensures that consensus is achieved among all agents while the adaptive law v simultaneously rejects the disturbances in the agent and its neighbors. The architecture also includes a health management system to detect faulty agents within the global network. Further numerical simulations successfully test and show that the Global Health Monitoring (GHM) does effectively detect faults within the network

Adaptive output feedback stabilization for nonlinear systems with unknown polynomial-of-output growth rate and sensor uncertainty

summary:In this paper, the problem of adaptive output feedback stabilization is investigated for a class of nonlinear systems with sensor uncertainty in measured output and a growth rate of polynomial-of-output multiplying an unknown constant in the nonlinear terms. By developing a dual-domination approach, an adaptive observer and an output feedback controller are designed to stabilize the nonlinear system by directly utilizing the measured output with uncertainty. Besides, two types of extension are made such that the proposed methods of adaptive output feedback stabilization can be applied for nonlinear systems with a large range of sensor uncertainty. Finally, numerical simulations are provided to illustrate the correctness of the theoretical results

Current challenges and future trends in the field of communication architectures for microgrids

[EN] The concept of microgrid has emerged as a feasible answer to cope with the increasing number of distributed renewable energy sources which are being introduced into the electrical grid. The microgrid communication network should guarantee a complete and bidirectional connectivity among the microgrid resources, a high reliability and a feasible interoperability. This is in a contrast to the current electrical grid structure which is characterized by the lack of connectivity, being a centralized-unidirectional system. In this paper a review of the microgrids information and communication technologies (ICT) is shown. In addition, a guideline for the transition from the current communication systems to the future generation of microgrid communications is provided. This paper contains a systematic review of the most suitable communication network topologies, technologies and protocols for smart microgrids. It is concluded that a new generation of peer-to-peer communication systems is required towards a dynamic smart microgrid. Potential future research about communications of the next microgrid generation is also identified.This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (ERDF) under Grant ENE2015-64087-C2-2. This work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant BES-2013-064539.Marzal-Romeu, S.; Salas-Puente, RA.; González Medina, R.; Garcerá, G.; Figueres Amorós, E. (2018). Current challenges and future trends in the field of communication architectures for microgrids. Renewable and Sustainable Energy Reviews. 82(2):3610-3622. https://doi.org/10.1016/j.rser.2017.10.101S3610362282

Security of cyber-physical systems: A control-theoretic perspective

Motivated by the attacks on control systems through the cyber (digital) part, we study how signal attacks injected through actuators and/or sensors affect control system stability and performance. We ask the questions: What are the different types and scenarios of signal attacks? When are the attacks stealthy and unbounded? How to compute the worst stealthy bounded attacks? How to defend against such attacks through controller design? How to identify and estimate signal attacks before significant performance loss happens? We answer the above questions in this thesis using tools from control theory. We show that it is necessary to use a sampled-data framework to accurately assess the vulnerabilities of control systems. In addition, we show that the most lethal attacks are related to the structure of the system (location of zeros and poles, number of inputs and outputs). We show that dual rate control is a powerful tool to defend against these vulnerabilities, and we provide a related controller design. Furthermore, we show that the worst stealthy bounded attacks can be computed by an iterative linear program, and we show how to lessen their effects through iterative controller design. Finally, we study the trade-off between control and estimation of signal attacks and provide several controller designs utilizing the power of dual rate sampling