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This article investigates an adaptive tracking control problem for a six degrees of freedom (6-DOF) nonlinear quadrotor unmanned aerial vehicle (UAV) with a variable payload mass. The changing payload introduces time-varying parametric uncertainties into the dynamical model, rendering a static control strategy no longer effective. To handle this issue, two adaptive schemes are developed to maintain the uncertainties in the translational and rotational dynamics. Initially, a virtual proportional derivative (PD) is designed to stabilize the horizontal position; however, due to an unknown and time-varying mass, an adaptive controller is proposed to generate the total thrust of the UAV. Furthermore, an adaptive controller is designed for the rotational dynamics, to handle parametric uncertainties, such as inertia and external disturbance parameters. In both schemes, a standard adaptive scheme using the certainty equivalence principle is extended and designed. A stability analysis was conducted with rigorous analytical proofs to show the performance of our proposed controllers, and simulations were implemented to assess the performance against other existing methods. Tracking fitness and total control efforts were calculated and compared with closed-loop adaptive tracking control (CLATC) and adaptive sliding mode control (ASMC). The results indicated that the proposed design better maintained UAV stability.

Surge is a form of dynamic instability created as an unstable pattern in the flow of fluid and can severely affect centrifugal compressor performance by causing fluctuations in flow and pressure parameters. Due to the heavy and costly damage that the surge may cause in various industrial processes such as petrochemical plants, it is necessary to design an appropriate control system to reduce the effect of this phenomenon. The problem of active surge control of a centrifugal compressor using the throttle control valve (TCV) in the presence of compressor parametric uncertainties as well as large demands on upstream and downstream loads is investigated in this work. The control objective was to design a robust control system that can stabilize the compressor over a wide operating range without knowing the upper bound for the uncertainties and load demand. The controller should also react quickly by generating a smooth control signal without saturating the control input. These objectives are achieved by designing a sliding mode controller along with a nonlinear disturbance observer. The performance of the proposed disturbance observer-based controller is evaluated under various operational and load conditions and the results are compared against fuzzy type 1, conventional sliding mode, and wavelet-based neural network robust adaptive controllers. The results show that the proposed method can tolerate large disturbances without any knowledge on the upper bound of the incident disturbance, both on the downstream pressure and upstream mass flow which is highly desirable in practice. The comparative study proves the efficacy of the proposed method using various performance measures. The study also confirms the superior robust performance and stability of the proposed method in front of matched and mismatched disturbances as well as model uncertainties especially close to the instability boundary. Although choosing a TCV actuator has made the control system design easier, th

Motion planning plays an important role in autonomous manipulators, especially when operating in confined spaces with various constraints. The problem is particularly challenging in unstructured and cluttered environments where there is no a priori information on the surroundings available. In this paper, a method for constrained motion planning of a hydraulically actuated manipulator is proposed within the context of laser cutting applications in nuclear decommissioning. This method enables the robot to move the end-effector to follow the surface of an inspected workpiece, based on the assumption that cutting points are generated from a 3D vision system. The algorithm works in two phases: in the first step, the ConstrainedRRT* motion planning algorithm computes a collision-free path for the manipulator to reach the initial cutting point above the surface of the target object whilst respecting robot kinematic constraints. In the second step, a laser cutting path is generated such that it satisfies the constraints imposed by the user and avoids singularities. The results indicate that the proposed method is robust against a degraded camera with 40 dB SNR under a 10 Gy/h radiation dose. The algorithm's performance is also compared against some well-known approaches in terms of planning time and computational complexity.

The environmental pollution caused by various sources has escalated the climate crisis making the need to establish reliable, intelligent, and persistent environmental monitoring solutions more crucial than ever. Mobile sensing systems are a popular platform due to their cost-effectiveness and adaptability. However, in practice, operation environments demand highly intelligent and robust systems that can cope with an environment’s changing dynamics. To achieve this reinforcement learning has become a popular tool as it facilitates the training of intelligent and robust sensing agents that can handle unknown and extreme conditions. In this paper, a framework that formulates active sensing as a reinforcement learning problem is proposed. This framework allows unification with multiple essential environmental monitoring tasks and algorithms such as coverage, patrolling, source seeking, exploration and search and rescue. The unified framework represents a step towards bridging the divide between theoretical advancements in reinforcement learning and real-world applications in environmental monitoring. A critical review of the literature in this field is carried out and it is found that despite the potential of reinforcement learning for environmental active sensing applications there is still a lack of practical implementation and most work remains in the simulation phase. It is also noted that despite the consensus that, multi-agent systems are crucial to fully realize the potential of active sensing there is a lack of research in this area.

High efficiency and torque density in permanent magnet synchronous motors (PMSMs) have contributed to their increasing popularity. Nonetheless, these advantages are compromised by higher vibration levels resulting from the torque ripple issue and magnetic flux density in the stator, causing magnetic forces on the stator surface. In this study, a new smart shape for the stator winding is proposed which reduces unwanted torque vibration and the overall magnetic flux density while keeping the same motor efficiency. The proposed windings shape is designed based on the auxetic principle and a locally resonant mechanism (LRM). Afterward, the proposed and original PMSM models are compared by looking at the average torque, total losses, torque ripple, flux density, output power, and motor efficiency under different speed operating conditions. In addition, the sensitivity analyses of the proposed model reveal the influence of auxetic structural parameters and initial mechanical angle on the system’s performance, which can be utilized to control the physical and mechanical properties of the system. According to the results, the designed model reduces torque ripple and magnetic flux density in the stator region by 41.38% and 4.70%, respectively, while the motor efficiency remains unaffected. The present work offers a potentially robust and affordable solution for regulating the vibration behavior of electric motors without impacting power efficiency.

The real‑time unknown parameter estimation and adaptive tracking control problems are investigated in this paper for a six degrees of freedom (6‑DOF) of under‑actuated quadrotor unmanned aerial vehicle (UAV). A virtual proportional derivative (PD) controller is designed to maintain the translational dynamics. Two adaptive schemes are developed to handle the attitude dynamics of the UAV with several unknown parameters. In the beginning, a classical adaptive scheme (CAS) using the certainty equivalence principle is proposed and designed. The idea is to design a controller for an ideal situation by assuming the unknown parameters were known. Then the unknown parameters are replaced by their estimation. A theoretical analysis is provided to ensure the trajectory tracking of the adaptive controller. However, an inherent drawback of this scheme is that there is no guarantee for the estimated parameters to converge to the actual values. To address this issue, a new adaptive scheme (NAS) is developed as the next step by adding a continuously differentiable function to the control structure. The proposed technique guarantees handling of the parametric uncertainties with an appropriate design manifold. A rigorous analytical proof, numerical simulation analyses, and experimental validation are presented to show the effectiveness of the proposed control design.

On-orbit service spacecraft with redundant actuators need to overcome orbital and attitude coupling when performing proximity maneuvers. In addition, transient/steady-state performance is required to fulfill the user-defined requirements. To these ends, this paper introduces a fixed-time tracking regulation and actuation allocation scheme for redundantly actuated spacecraft. The coupling effect of translational and rotational motions is described by dual quaternion. Based on this, we propose a non-singular fast terminal sliding mode controller to guarantee fixed-time tracking performance in the presence of external disturbances and system uncertainties, where the settling time is only dependent on user-defined control parameters rather than initial values. The unwinding problem caused by the redundancy of dual quaternion is handled by a novel attitude error function. Moreover, optimal quadratic programming is incorporated into null space pseudo-inverse control allocation that ensures the actuation smoothness and never violates the maximum output capability of each actuator. Numerical simulations on a spacecraft platform with symmetric thruster configuration demonstrate the validity of the proposed approach.

This book chapter looks to address how upcoming technology can be used to improve the efficiency of decommissioning processes within the nuclear industry. Challenges associated with decommissioning are introduced with a brief overview of the previous efforts and current practices of nuclear decommissioning. A high-level cyber-physical architecture for nuclear decommissioning applications is then proposed by drawing upon recent technological advances in the realm of Industry 4.0 such as internet of things, sensor networks, and increased use of data analytics and cloud computing approaches. In the final section, based on demands and proposals from industry, possible applications within the nuclear industry are identified and discussed.

There has been an increase in customer churn over the past few years—customers decide not to continue purchasing products or services from an organization. Customers’ data lie in two categories: soft and hard. The term “hard data” refers to the records generated by various devices and programs, including but not limited to smartphones, computers, sensors, smart meters, fleet management systems, call detail records (CDRs), and consumer bank transaction data. On the other hand, information that is subject to interpretation and viewpoint is known as “soft data.” Fusing these two types of data leads to better customer behaviour analysis. This paper uses a supervised machine learning algorithm, namely a decision tree (DT), and the change mining method to model hard data. K-means clustering, an unsupervised machine learning algorithm, is also used along with the data preprocessing techniques. This paper also considers the Dempster-Shafer theory and other steps for soft data modelling. By fusing soft and hard data, the churn rate of customers compared with each other can be calculated. Besides, the customers’ banking data are leveraged for data modelling. The results show that the banking industry will gain a more dynamic and efficient customer relationship management system by using this model.

Nowadays, mobile robots are becoming an increasingly significant part of daily human life. Humanoid robots, wheeled mobile robots, aerial vehicles, mobile manipulators, and more are examples of mobile robots. As opposed to other robots, they are capable of moving autonomously, with sufficient intelligence to make decisions in response to the perceptions they receive from their environment. In today’s world, cooperative tasks and the ability to control robots via networks make them a component of cyber-physical systems (CPSs). In this study, mobile robots that are acting as a part of CPSs are examined. Data-network burden, signal quantizers, cyber security, delayed transition, and robust performance are some of the challenges they face. A total of three sections are then devoted to addressing these issues in detail. As a first step, the governing equation for mobile robots is explained, and then their robust and resilient behavior of them is examined by establishing the event-based adaptive optimal fast terminal sliding mode control (AOFTSMC) approach for nonlinear uncertain dynamic systems that are subjected to denial-of service (DoS) cyber attacks. In this case, it is assumed that the conveyed signal is being corrupted randomly by an attacker. In this situation, it is essential to design the closed-loop controller parameters in such a way that the performance can be maintained under malicious attacks while the communication resources are preserved. Due to the unrealistic nature of delayed-free communication, the stability analysis is conducted for a general form of uncertain nonlinear delayed input dynamic systems. The quantization effect on the closed-loop control system is then analyzed in conjunction with robust behavior and event-based data transmission. A novel criterion is established to adjust dynamic quantizers’ parameters according to the variation of event-triggering error, enabling the quantizer to be more accurate and facilitating implementation procedu

This paper presents the design of a novel H ∞ -based control framework for state regulation of continuous-time linear systems with completely unknown dynamics. The proposed method solves the regulation problem with the desired convergence rate and simultaneously seeks to attenuate the adverse effect of disturbance on the system. The H ∞ regulation problem assumes a cost function that considers regulation with a guaranteed rate of convergence as well as disturbance attenuation. The problem is then turned into a two-player zero-sum game optimization problem that can be solved by solving the associated algebraic Riccati equation (ARE), which provides a model-based solution. To solve this problem in a model-free way, a novel integral reinforcement learning (IRL) algorithm is designed to learn the solution online without requiring any prior knowledge of the system dynamics. It is shown that the model-free method (i.e., IRL-based method) provides the same solution as the model-based method (i.e., ARE). The effectiveness of the proposed method is ascertained through simulation examples; it is shown that the proposed method effectively addresses the problem for both stable and unstable systems.

In this investigation, an adaptive self-organizing fuzzy sliding mode controller (ASFSC) has been developed for vibration control of an Euler-Bernoulli nanobeam with immoveable ends, imposed by an external centralized force. Considering mid-plane stretching effect, governing ordinary differential equation (ODE) of the considered nanobeam is derived based on non-local strain gradient theory and using Galerkin projection. In order to overcome the problem of choosing inappropriate parameters in self-organizing part, a self-organizing fuzzy sliding mode controller is constructed to modify the parameters. Another fuzzy controller is designed and has the role of imposing controller output to regulate the system response. Sliding surface and its changes are determined as controller signal inputs for obtaining better performance. An adaptive law is used to validate the stability and improve the performance of the system by modifying fuzzy controller coefficients, as well as reducing chattering phenomena and overcome the issues of dead-zone. Furthermore, Lyapunov stability proof is presented and the numerical simulations are the showcase of the high performance of the proposed controller.

Passenger comfort has always been of pivotal importance in the interior design of an automobile. A critical aspect in reaching this goal in the automotive industry is the design and implementation of an effective active sound management system with the ability to personalize the acoustic environment inside the car. This, in turn, requires designing an active noise control (ANC) system to mitigate the unwanted noise and an active sound profiling system to implement the desired sound. Due to the complexity of the sound field inside the car cabin, having a high-fidelity model that reflects all details is a challenging task. Therefore, in this paper, we develop a simulation platform to be able to evaluate the performance of the ANC system and the distribution of the sound field as a result of this mechanism. This helps to get a better insight into the behaviours of the sound field inside the cabin before its actual implementation. One important feature of this model, which may also have a significant effect on the performance of the ANC system, is the inclusion of a full-scale numerical model of the loudspeaker. The realistic model of the loudspeaker developed in this way allows to model the effect of loudspeaker coupling in an enclosed space and investigate its effect on the ANC system. The model is compared against the simplified mathematical model of the enclosure developed in the previous work by the authors to see how the approximate geometry and simplified model of the loudspeaker would degrade the performance of the ANC system and measure the changes in the acoustic radiation impedance of the loudspeaker.