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This study focuses on stabilizing a bidirectional inductive wireless power transfer (WPT) system using an event‐triggered approach. It is only assumed the inductor currents on both the primary and pickup sides are measurable, and they are sent synchronously to the controller via a digital channel. To estimate the unmeasured states and maintain plant stability, a full‐order state observer and an observer‐based controller have been developed. The control parameters are optimized through a genetic algorithm to achieve the desired output response. An emulation methodology is then applied to create an output‐based event‐triggering condition. This condition ensures the stability of the closed‐loop system even in the presence of communication constraints. To prevent Zeno sampling, a minimal time interval between two transmissions is enforced using time‐regularization techniques. Furthermore, the performance of the event‐triggered controller is enhanced by solving a linear matrix inequality condition, which further reduces the number of transmission instances. The methodology offers a systematic and optimal design for the bidirectional inductive WPT system. It eliminates the need for manual tuning of control parameters, which is particularly beneficial given the system's complex nature. To address both continuous‐time and discrete‐time dynamics, the entire system is represented as a hybrid dynamical system, making it more intuitive for networked control systems. The efficiency of this approach is assessed through numerical simulations of a WPT system, demonstrating its effectiveness. The results show that the average intertransmission interval has been increased from 0.0799 to 0.1782 s, that is, the proposed event‐triggering strategy reduced the number of transmissions to more than 50% compared with conventional periodic sampling. [ABSTRACT FROM AUTHOR]

This paper considers the problem of robust stabilization of linear time‐invariant systems with respect to unmodelled dynamics and structure uncertainties. To that end, a methodology to find the nearest negative imaginary system for a given non‐negative imaginary system is presented first. Then, this result is employed to construct a near optimal linear quadratic Gaussian controller achieving desired performance measures. The problem is formulated using port‐Hamiltonian method and the required conditions are defined in terms of linear matrix inequalities. The technique is presented using the fast gradient method to solve the problem systematically. The designed controller satisfies a negative imaginary property and guarantees a robust feedback loop. The effectiveness of the approach is demonstrated by a simulation on a numerical example. [ABSTRACT FROM AUTHOR]

Autonomous robots require control tuning to optimize their performance, such as optimal trajectory tracking. Controllers, such as the Proportional–Integral–Derivative (PID) controller, which are commonly used in robots, are usually tuned by a cumbersome manual process or offline data-driven methods. Both approaches must be repeated if the system configuration changes or becomes exposed to new environmental conditions. In this work, we propose a novel algorithm that can perform online optimal control tuning (OCTUNE) of a discrete linear time-invariant (LTI) controller in a classical feedback system without the knowledge of the plant dynamics. The OCTUNE algorithm uses the backpropagation optimization technique to optimize the controller parameters. Furthermore, convergence guarantees are derived using the Lyapunov stability theory to ensure stable iterative tuning using real-time data. We validate the algorithm in realistic simulations of a quadcopter model with PID controllers using the known Gazebo simulator and a real quadcopter platform. Simulations and actual experiment results show that OCTUNE can be effectively used to automatically tune the UAV PID controllers in real-time, with guaranteed convergence. Finally, we provide an open-source implementation of the OCTUNE algorithm, which can be adapted for different applications. [ABSTRACT FROM AUTHOR]

This study evaluates the use of Negative Imaginary (NI) theory to model and control forces in external prestressing cables used in retrofitting bridge superstructures. The study analytically and experimentally applied the concept to assess the system robustness and its overall performance. A small-scale prototype was built to represent an aged bridge superstructure member. The NI control system was proven to behave robustly and successfully performed as desired. The system was also compared to two other schemes on the prototype from the structural performance perspective. These schemes are the 'No-prestressing' and 'Passive prestressing' schemes. The actively controlled system was able to limit the superstructure deflections and stresses under the applied vehicular live load compared to the other two schemes. [ABSTRACT FROM AUTHOR]

Understanding the collaboration between physical systems and human is an essential task in man-machine systems. This area of research has been significantly explored in the recent years with the focus on the machine side. Much less attention has been directed to the other side in the man-machine systems, which is the human. The aim of this review is to discuses the major directions and challenges in man-machine systems from control theory perspective with a focus on human modeling and human attributes in the man-machine system. Four directions has been identified; these are: 1) Understanding the attributes and the limitation of the human operator; 2) Categorizing the human-in-the-loop applications and derive generic bounders for each category; 3) Building a realistic application-based model for the human behavior; and 4) Integrating the human models into a formal control synthesis methodology. We surveyed the contribution in each of the four challenges and pointed out the limitation in the proposed ideas. Particular attention is given to the model identification of the human behavior. [ABSTRACT FROM AUTHOR]

The ground robotics challenge in the Mohammed Bin Zayed International Robotics Challenge required a ground vehicle equipped with a robotic arm to autonomously locate a panel, select a proper size wrench among several options mounted on the panel, and use the wrench to rotate a valve. Autonomy was the critical factor in this challenge, which required the teams to devise algorithms that can operate successfully in a semistructured environment without human supervision. This paper presents the approaches taken by team KAUST to meet this challenge, ranging from in‐house hardware designs to algorithm integration and customization. We separated the whole objective into three interconnected tasks: Navigation, perception, and manipulation. For the navigation task, we developed a basic robotic exploration scheme to find the panel front side where the wrenches were present. For the perception task, we integrated common object detection algorithms with neural networks to identify the proper size wrench precisely. For successful manipulation, we designed and built a custom gripper, which was inspired by the common grasping behavior of a human hand under tight clearance conditions. The modular structure of the proposed approach allowed the team to progress in several subtasks simultaneously. However, the interconnection between the subtasks necessitated a reliable integration framework between these modules for effective implementation. We tuned our algorithms in extensive experimental studies and eventually obtained 10 consecutive successful navigation runs, 96% true wrench detection rate, and high success rate in wrench grasping. Furthermore, successful complete tests proved the reliability and repeatability of our system. [ABSTRACT FROM AUTHOR]

Extracting repeated unknown maneuvers or patterns preformed by a human operator in a cyber-physical systems can lead to better understanding of the behavior of the human operator who is controlling or sharing tasks with dynamical systems. These repeated maneuvers can be extracted by analyzing the inputs and outputs of the human operator using control theory and data mining tools. In this paper, we introduce geometrical shape-based pattern detection approach for input-output data. A pattern or a maneuver is defined as the maximum repeated behavior in time series trajectory data that is generated from the operator's inputs and outputs. A two-phase algorithm is developed in this paper, the first phase consists of trajectory segmentation, creating segment fingerprint, clustering, and symbolic representations. The second phase of the proposed algorithm is pattern extraction phase, which is inherited from the motif finding algorithms in time series data and DNA sequences. [ABSTRACT FROM AUTHOR]

Axiomatic design (AD) theory is a general framework for system design process based on mapping between several domains. In the AD theory, only the functional requirements (system functions) are considered in the design process, since the mapping between requirements and design parameters in AD does not include nonfunctional requirements (system attributes). This paper introduces an extended view of the system design process based on AD theory framework to incorporate nonfunctional requirements into the mapping between the problem domain and the solution domain. A systematic way of including nonfunctional requirements in the design process is presented, and an extended design matrix is introduced, which includes the mapping between a complete set of requirements (functional and nonfunctional) and corresponding design parameters (physical domain). We also present an approach to decide between solutions that satisfy nonfunctional requirements to a varying degree through the employment of the analytical hierarchy process. [ABSTRACT FROM PUBLISHER]

The negative imaginary (NI) property occurs in many important applications. For instance, flexible structure systems with collocated force actuators and position sensors can be modelled as negative imaginary systems. In this study, a data‐driven controller synthesis methodology for NI systems is presented. In this approach, measured frequency response data of the plant is used to construct the controller frequency response at every frequency by minimising a cost function. Then, this controller response is used to identify the controller transfer function using system identification methods. [ABSTRACT FROM AUTHOR]

Robust control design for quantum systems has been recognized as a key task in the development of practical quantum technology. In this paper, we present a systematic numerical methodology of sampling-based learning control (SLC) for control design of quantum systems with uncertainties. The SLC method includes two steps of training and testing. In the training step, an augmented system is constructed using artificial samples generated by sampling uncertainty parameters according to a given distribution. A gradient flow-based learning algorithm is developed to find the control for the augmented system. In the process of testing, a number of additional samples are tested to evaluate the control performance, where these samples are obtained through sampling the uncertainty parameters according to a possible distribution. The SLC method is applied to three significant examples of quantum robust control, including state preparation in a three-level quantum system, robust entanglement generation in a two-qubit superconducting circuit, and quantum entanglement control in a two-atom system interacting with a quantized field in a cavity. Numerical results demonstrate the effectiveness of the SLC approach even when uncertainties are quite large, and show its potential for robust control design of quantum systems. [ABSTRACT FROM AUTHOR]

The negative imaginary (NI) property is exhibited by many systems such as flexible structures with force actuators and position sensors and can be used to prove the robust stability of flexible structure control systems. In this paper, we derive methods to check for the NI and strict negative imaginary (SNI) properties in both the single-input single-output as well as multi-input multi-output cases. The proposed methods are based on spectral conditions on a corresponding Hamiltonian matrix obtained for a given system transfer function matrix. Under certain conditions, a given transfer function matrix satisfies the NI property if and only if the corresponding Hamiltonian matrix has no pure imaginary eigenvalues with odd multiplicity. It is also shown that a given transfer function matrix satisfies the SNI property if and only if the corresponding Hamiltonian matrix has no eigenvalues on the imaginary axis, except at the origin. The results of this paper are applied to check the NI property in two nanopositioning applications. [ABSTRACT FROM PUBLISHER]