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

Privacy-Preserving Stealthy Attack Detection in Multi-Agent Control Systems

This paper develops a glocal (global-local) attack detection framework to detect stealthy cyber-physical attacks, namely covert attack and zero-dynamics attack, against a class of multi-agent control systems seeking average consensus. The detection structure consists of a global (central) observer and local observers for the multi-agent system partitioned into clusters. The proposed structure addresses the scalability of the approach and the privacy preservation of the multi-agent system's state information. The former is addressed by using decentralized local observers, and the latter is achieved by imposing unobservability conditions at the global level. Also, the communication graph model is subject to topology switching, triggered by local observers, allowing for the detection of stealthy attacks by the global observer. Theoretical conditions are derived for detectability of the stealthy attacks using the proposed detection framework. Finally, a numerical simulation is provided to validate the theoretical findings.Comment: to appear in IEEE CD

Learning When to Use Adaptive Adversarial Image Perturbations against Autonomous Vehicles

The deep neural network (DNN) models for object detection using camera images are widely adopted in autonomous vehicles. However, DNN models are shown to be susceptible to adversarial image perturbations. In the existing methods of generating the adversarial image perturbations, optimizations take each incoming image frame as the decision variable to generate an image perturbation. Therefore, given a new image, the typically computationally-expensive optimization needs to start over as there is no learning between the independent optimizations. Very few approaches have been developed for attacking online image streams while considering the underlying physical dynamics of autonomous vehicles, their mission, and the environment. We propose a multi-level stochastic optimization framework that monitors an attacker's capability of generating the adversarial perturbations. Based on this capability level, a binary decision attack/not attack is introduced to enhance the effectiveness of the attacker. We evaluate our proposed multi-level image attack framework using simulations for vision-guided autonomous vehicles and actual tests with a small indoor drone in an office environment. The results show our method's capability to generate the image attack in real-time while monitoring when the attacker is proficient given state estimates

Load capacity improvements in transcriptional systems using discrete-time L1-adaptive control

DNA-based circuits relying on predictable thermodynamics and kinetics of DNA strand interactions impart flexibility in synthesizing synthetic biological constructs and in coupling these circuits to in vivo processes [1, 2, 6, 7]. Here, we focus on the synthetic Kim-Winfree oscillator network, illustrated in Fig. 1(i), which is a simple but effective coupled oscillator system in which two DNA switches SW1 and SW2 are coupled through activator and inhibitor blocks realized by RNA signals and auxiliary DNA species (see [3]). A typical experimental realization is closed in the sense that once the operation starts, we do not either add any chemicals, especially NTP fuel, externally into the wet-lab apparatus or remove any chemicals, especially waste products, from the apparatus. Within the closed system, the oscillations are bound to die out sooner or later diminishing NTP fuel eventually stops supporting the production of RNA signals and accumulating waste products clog down the toeholds and, as a result, adversely affect the signal propagation. Furthermore, the oxidation effects and the pH variations tend to deactivate the enzymes. Loading poses an additional challenge since it increases the order and the uncertainty of the system indeed, these oscillators have recently been used in [8] to drive conformational changes of a DNA nanomechanical device called DNA tweezers. We show how L1-adaptive control can be used to mitigate these effects

L1-Optimal Control of Variable-Speed Variable-Pitch Wind Turbines

The fast-growing technology of large scale wind turbines demands control systems capable of enhancing both the efficiency of capturing wind power, and the useful life of the turbines. Control based on L1 performance is an approach to deal with persistent exogenous disturbances which have bounded magnitude (L1-norm) such as realistic wind disturbances and turbulence profiles. In this study, we use a linear matrix inequality (LMI) approach for solution of the L1-optimal control problem. We develop an LPV model of a variable-speed variable-pitch (VS-VP) wind turbine in the transition region (between power point tracking and power regulation regimes). Then, we derive an LPV L1-optimal controller using LMI methods. We also develop an efficient method for computing the L1-norm of a closed-loop system. As the control synthesis problem is non-convex, we use the proposed method to design optimal output feedback controllers for a linear model of a wind turbine at different operating points using genetic algorithm (GA) optimization. The locally optimized controllers were interpolated using a gain-scheduled technique with guaranteed stability. The controller is tested with comprehensive simulation studies on a 5 MW wind turbine using FAST software. The proposed controller was compared with a well-tuned PI controller. The results show improved power quality, and decrease in the fluctuations of generator torque and rotor speed

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.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Detection of Stealthy Adversaries for Networked Unmanned Aerial Vehicles*

A network of unmanned aerial vehicles (UAVs) provides distributed coverage, reconfigurability, and maneuverability in performing complex cooperative tasks. However, it relies on wireless communications that can be susceptible to cyber adversaries and intrusions, disrupting the entire network's operation. This paper develops model-based centralized and decentralized observer techniques for detecting a class of stealthy intrusions, namely zero-dynamics and covert attacks, on networked UAVs in formation control settings. The centralized observer that runs in a control center leverages switching in the UAVs' communication topology for attack detection, and the decentralized observers, implemented onboard each UAV in the network, use the model of networked UAVs and locally available measurements. Experimental results are provided to show the effectiveness of the proposed detection schemes in different case studies.Comment: to appear at the 2022 Int'l Conference on Unmanned Aircraft Systems (ICUAS