In order to be economically competitive, various control systems are used in large scale wind turbines. These systems enable the wind turbine to work efficiently and produce the maximum power output ...at varying wind speed. In this paper, an adaptive control based on radial-basis-function neural network (NN) is proposed for different operation modes of variable-speed variable-pitch wind turbines including torque control at speeds lower than rated wind speeds, pitch control at higher wind speeds and smooth transition between these two modes The adaptive NN control approximates the nonlinear dynamics of the wind turbine based on input/output measurements and ensures smooth tracking of the optimal tip-speed-ratio at different wind speeds. The robust NN weight updating rules are obtained using Lyapunov stability analysis. The proposed control algorithm is first tested with a simplified mathematical model of a wind turbine, and then the validity of results is verified by simulation studies on a 5 MW wind turbine simulator.
This article studies novel attack and defense strategies, based on a class of stealthy attacks, namely the zero-dynamics attack (ZDA), for multiagent control systems. ZDA poses a formidable security ...challenge since its attack signal is hidden in the null space of the state-space representation of the control system and hence it can evade conventional detection methods. An intuitive defense strategy builds on changing the aforementioned representation via switching through a set of carefully crafted topologies. In this article, we propose realistic ZDA variations where the attacker is aware of this topology-switching strategy, and hence employs the following policies to avoid detection: first, pause, update, and resume ZDA according to the knowledge of switching topologies; and second, cooperate with a concurrent stealthy topology attack that alters network topology at switching times, such that the original ZDA is feasible under the corrupted topology. We first systematically study the proposed ZDA variations, and then develop defense strategies against them under the realistic assumption that the defender has no knowledge of attack starting, pausing, and resuming times and the number of misbehaving agents. Particularly, we characterize conditions for detectability of the proposed ZDA variations, in terms of the network topologies to be maintained, the set of agents to be monitored, and the measurements of the monitored agents that should be extracted, while simultaneously preserving the privacy of the states of the nonmonitored agents. We then propose an attack detection algorithm based on the Luenberger observer, using the characterized detectability conditions. We provide numerical simulation results to demonstrate our theoretical findings.
This article presents a command limiting method for attitude protection on aerial vehicles equipped with rate control augmentation systems.The method is developed based on exponential potential ...functions. To avoid excessive corrections, a switched command limiting control law is proposed. Stability analysis is provided for the closed-loop system. Phase portrait analysis on a linear plant with the proposed method is conducted. Simulation examples of the bank angle protection on a nonlinear transport aircraft model are presented to verify the design.
This paper develops a sampled-data (SD) controller for uncertain multi-input multi-output (MIMO) systems, possibly with non-minimum phase zeros, using the L1 adaptive control architecture. The ...proposed controller compensates for disturbances and uncertainties within the bandwidth of a low-pass filter. Sufficient conditions for robust stability are obtained for the closed-loop system with SD controller, where the input/output signals are held constant over a sampling period. It is shown that the hybrid closed-loop system can recover the performance of a continuous-time reference system as the sampling time of the SD controller tends to zero. Simulation examples are provided to validate the theoretical findings.
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 themselves. l 1 ...-Optimal control is an approach to deal with persistent exogenous disturbances which have bounded magnitude (l ∞ -norm) such as realistic wind disturbances and turbulence profiles. In this brief, we develop an efficient method to compute the l 1 -norm of a system. As the control synthesis problem is nonconvex, we use the proposed method to design the optimal output feedback controllers for a linear model of a wind turbine at different operating points using genetic algorithm optimization. The locally optimized controllers are interpolated using a gain-scheduled technique with guaranteed stability. The controller is tested with comprehensive simulation studies on a 5 MW wind turbine using fatigue, aerodynamics, structures, and turbulence (FAST) software. The proposed controller is compared with a well-tuned proportional-integral (PI) controller. The results show improved power quality, and decrease in the fluctuations of generator torque and rotor speed.
Summary
Autonomous systems are rapidly becoming an integrated part of the modern life. Safe and secure navigation and control of these systems present significant challenges in the presence of ...uncertainties, physical failures, and cyber attacks. In this paper, we formulate a navigation and control problem for autonomous systems using a multilevel control structure, in which the high‐level reference commands are limited by a saturation function, whereas the low‐level controller tracks the reference by compensating for disturbances and uncertainties. For this purpose, we consider a class of nested, uncertain, multiple‐input–multiple‐output systems subject to reference command saturation, possibly with nonminimum phase zeros. A multirate output‐feedback
L1 adaptive controller is developed as the low‐level controller. The sampled‐data (SD) design of this controller facilitates the direct implementation on digital computers, where the input/output signals are available at discrete time instances with different sampling rates. In addition, stealthy zero‐dynamics attacks become detectable by considering a multirate SD formulation. Robust stability and performance of the overall closed‐loop system with command saturation and multirate
L1 adaptive control are analyzed. Simulation scenarios for navigation and control of a fixed‐wing drone under failures/attacks are provided to validate the theoretical findings.
Deep neural network (DNN) models are widely used in autonomous vehicles for object detection using camera images. However, these models are vulnerable to adversarial image perturbations. Existing ...methods for generating these perturbations use the image frame as the decision variable, resulting in a computationally expensive optimization process that starts over for each new image. Few approaches have been developed for attacking online image streams while considering the physical dynamics of autonomous vehicles, their mission, and the environment. To address these challenges, we propose a multi-level stochastic optimization framework that monitors the attacker's capability to generate adversarial perturbations. Our framework introduces a binary decision attack/not attack based on the attacker's capability level to enhance its effectiveness. We evaluate our proposed framework using simulations for vision-guided autonomous vehicles and actual tests with a small indoor drone in an office environment. Our results demonstrate that our method is capable of generating real-time image attacks while monitoring the attacker's proficiency given state estimates.
This paper addresses efficient feasibility evaluation of possible emergency landing sites, online navigation, and path following for automatic landing under engine-out failure subject to turbulent ...weather. The proposed Multi-level Adaptive Safety Control framework enables unmanned aerial vehicles (UAVs) under large uncertainties to perform safety maneuvers traditionally reserved for human pilots with sufficient experience. In this framework, a simplified flight model is first used for time-efficient feasibility evaluation of a set of landing sites and trajectory generation. Then, an online path following controller is employed to track the selected landing trajectory. We used a high-fidelity simulation environment for a fixed-wing aircraft to test and validate the proposed approach under various weather uncertainties. For the case of emergency landing due to engine failure under severe weather conditions, the simulation results show that the proposed automatic landing framework is robust to uncertainties and adaptable at different landing stages while being computationally inexpensive for planning and tracking tasks.
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