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• Hybrid microgrid is an emerging and exciting research field in power engineering. • Presents systematic review on various control strategies for hybrid microgrid. • Comparison between control strategies satisfying various control objectives. • Discussion on research challenges in use of effective and robust control scheme. • Discussion on emerging trends in hybrid microgrid's operation and control. Presently, there is huge development in conventional power systems due to the evolution of modern smart grids, wherein interconnected microgrids with a high level of energy storage and renewable energy penetration are gaining popularity in the modern distribution network. Future electrical distribution systems, or even transmission structures that combine AC and DC sources and loads are thought to be most promising when it comes to hybrid AC-DC systems. Using a combined operation of both AC and DC microgrids through an interfacing converter, hybrid AC-DC microgrids are advanced and benefitted with the use of both AC and DC topologies. Power management techniques for these microgrids are among the most important operational aspects. This paper provides a systematic review on numerous schemes to control hybrid AC-DC microgrids. Basically, microgrid control strategies are categorized as local control and coordinated control. Coordinated control is further divided in three control strategies as, distributed, centralized, and decentralized control. All these control strategies are employed to satisfy many control objectives, like bus voltage and frequency regulation, power sharing, and management, power quality, stability and reliability improvement, energy management, and economic dispatch. In this paper, various aspects associated with these control objectives and limitations in their control strategies are discussed. Also, future prospects on control of hybrid AC-DC microgrids and an effective and robust control strategy for power management possibly to be employed, are presented in the concluding section of this paper. [ABSTRACT FROM AUTHOR]

One of the biggest challenges in optimizing the shape of complex solids is the requirement to maintain a reasonable mesh quality not only at the boundary but also for the bulk discretization of the interior. Thus, additional regularization and, in many cases, re-meshing of the structure during the iterative process is unavoidable with a Lagrangian description. By tracking the shape update using an Eulerian representation, embedded boundary methods are a promising technique for eliminating mesh distortion problems. This work consistently combines the unique features of implicit Vertex-Morphing and embedded boundary methods, facilitating the node-based shape optimization of solids with industrial complexity. One of the crucial elements for solving the primal problem on a fixed background grid is an efficient and robust quadrature scheme. To this end, we incorporate the open-source C++ framework QuESo (https://github.com/manuelmessmer/QuESo) developed for the numerical integration of arbitrarily complex embedded solids defined by oriented boundary meshes, e.g., in STereoLithography (STL) format. Meanwhile, applying the Helmholtz/Sobolev-based (implicit) filter to the vertices of the embedded boundary mesh not only exploits the extensive design space of node-based optimization but also ensures robust control over the feature size. To realize the above methodology, this work introduces a novel sensitivity analysis that yields mesh-independent shape gradients with respect to the immersed boundary. Through a specific sensitivity weighting, we recover a continuous gradient field from discrete values calculated at the nodes of the background grid. In addition, the presented workflow ensures robust enforcement of challenging geometrical constraints, including minimum wall thicknesses and design space limitations. We critically assess our approach with benchmarks and structures of industrial relevance. In all examples, trivariate B-Spline bases span the background grid, providing highly accurate finite element solutions and shape sensitivities at every iteration. Moreover, the elimination of mesh distortion problems enables a successful termination at the local optimum, even for large shape modifications. • Extension of Vertex-Morphing to embedded boundary methods. • Rigorous elimination of mesh distortion problems. • Discretization-independent sensitivity analysis. • Robust enforcement of design space constraints. • Application to solid structures with industrial complexity. [ABSTRACT FROM AUTHOR]

With the widespread application of unmanned aerial vehicles, harsh environments require higher control performance. Numerous existing works offer solutions for handling external disturbances during flight; however, the timeliness of the disturbance compensation is rarely discussed. This study focuses on trajectory tracking under external wind disturbances, specifically under two types of interference: average wind and wind shear. After compensating for the disturbance, a novel antidisturbance sliding mode control is designed based on reference model, to realize accurate, robust, and chatterless trajectory tracking under wind disturbance. Firstly, we addressed the limitations of conventional extended state observers, nonderived forms, and inadequate use of information by introducing state compensation function observers to enhance estimation accuracy for states and disturbances that are challenging to measure directly. Moreover, the disturbance variation is estimated using tracking differentiator and then compensated in the controller to enhance antidisturbance sensitivity. In addition, the effectiveness and stability of the proposed observer and controller are analyzed. Finally, a two-step verification is presented, including simulation with input disturbances and actual flight under two wind field types; multiple industrial fans are used to generate the average wind and wind shear during the experiment. Two robust control methods, state-of-the-art active disturbance rejection control and sliding mode control, are also implemented for comparison, and the results indicate that more than 61.2% accuracy improvement over comparison methods is achieved in simulation and actual experiments. [ABSTRACT FROM AUTHOR]

This study presents a novel approach for controlling an industrial process that exhibits uncertainty and significant nonlinear features. The proposed method utilizes a virtual unmodeled dynamic and data-driven nonlinear robust predictive control strategy. The representation of a controlled object involves a composite state space model that combines both linear and high-order nonlinear elements. Moreover, a robust model predictive controller is developed using the linear component. In addition, the notion of one-step optimal feedforward is used in combination with a compensating controller to handle the high-order nonlinear factor specifically. Subsequently, a compensation controller with incremental characteristics is developed for a modified version of the high-order nonlinear term. Furthermore, the stability conditions of the closed-loop system are derived, and an analysis is conducted on the stability and convergence of the proposed approach. The TTS20 three-capacity water tank was utilized in both simulations and practical scenarios. The study demonstrated that the suggested approach successfully reduces system output variations and enhances overall performance in response to unpredictable changes in the process's dynamic features. • Adding a feed-forward branch to the control system raises the stability margin, which is better than the RMPC. • This study explores the integration of unmodeled dynamics using a real-time predictive compensation technique. • The effective use of implicit data has the potential to enhance the control system's ability to adapt and adjust itself. [ABSTRACT FROM AUTHOR]

This paper presents the effect of a Battery Energy Storage System (BESS) on the power system inter-area oscillations under changing load conditions. The dynamic interaction between the BESS control modes and synchronous generators, along with BESS control parameters, influence the damping of prominent inter-area oscillations under different loading conditions. Furthermore, H ∞ mixed sensitivity scheme-based Wide-Area Damping Controller (WADC) is proposed for BESS. This controller is designed using the Linear Matrix Inequality (LMI) framework to mitigate the prominent inter-area oscillation of the power system. Sensitivity to control parameter variation and the geometric measure of the observability are utilized to select the location and feedback signal for the WADC. The performance of the proposed WADC is compared with an Energy Storage System-based stabilizer and WADC proposed in an existing work on a modified New England ten-machine benchmark system. The Real-Time Digital Simulator (RTDS) platform is used to assess the real-time viability of the proposed WADC. The simulation results demonstrate that the proposed WADC can effectively diminish several inter-area modes. Additionally, it offers robust performance under varying load conditions, contingencies and with communication delays in feedback signals. The proposed controller is also effective under the system integrated with Doubly Fed Induction Generator-based Wind Turbine System. • The impact of BESS on the power system inter-area oscillation damping is investigated. • The impact of dynamic control mode interaction between SGs and BESS on the system oscillation modes under various operating conditions is investigated. • A H ∞ mixed sensitivity scheme-based WADC is designed for BESS to alleviate the power system inter-area oscillations. • The H ∞ mixed sensitivity scheme-based WADC is implemented in the RTDS to assess its feasibility in a real-time environment. [ABSTRACT FROM AUTHOR]

The last few years have seen the massive deployment of electric buses in many existing transit networks. However, the planning and operation of an electric bus system differ from that of a bus system with conventional vehicles, and some key problems have not yet been studied in the literature. In this work, we address the integrated operational control and charging scheduling problem for a network of electric buses with a limited opportunity charging capacity. Operational control is carried out through speed control of the vehicles and bus holding at the terminal. We propose a hierarchical control framework to solve this integrated problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer. Since this optimization problem might become very large as more bus lines are considered, we propose to apply Lagrangian relaxation in such a way as to exploit the structure of the problem and enable a decomposition into independent subproblems. A local search heuristic is then deployed in order to generate good feasible solutions to the original problem. This entire Lagrangian heuristic procedure is shown to scale much better on transit networks with an increasing number of bus lines than trying to solve the original problem with an off-the-shelf solver. The proposed procedure is then tested in the high-fidelity microscopic traffic environment Vissim on a bus network constructed from an openly available dataset of the city of Chicago. The results show the benefits of combining the charging scheduling decisions with the real-time operational control of the vehicles as the proposed control framework manages to achieve both a better level of service and lower charging costs over control baselines with predetermined charging schedules. • Charging scheduling for electric buses traditionally excludes operational control. • A robust control strategy can be obtained by combining these aspects. • Formulation of the bus network control problem as an MILP. • A scalable solution procedure is obtained with Lagrangian relaxation. • Better service regularity and lower charging costs are obtained. [ABSTRACT FROM AUTHOR]

• Developed a robust cooperative control strategy (RCC) strategy to improve the performance of CAV platoon against uncertain vehicle dynamics induced by vehicle sensor error and control errors. • Designed an efficient and globally convergent solution algorithm for the minimax problem. • Develop an effective method to estimate the ranges of the sensor errors and control errors. In a real-world driving environment, a platoon of connected and autonomous vehicles (CAVs) is subject to many internal and external disturbances, resulting in uncertain vehicle dynamics. In general, the disturbances can be categorized into two types: disturbances due to vehicle sensor errors (e.g., GPS error) and disturbances due to vehicle control errors (e.g., actuator delay). In the literature, many control strategies have been proposed to improve the robustness of the CAV platoon against uncertain vehicle dynamics induced by these disturbances. However, most of these strategies only consider one type of disturbance and cannot tackle both types of disturbances simultaneously. Furthermore, they are designed to maximize the benefits of each vehicle in the platoon independently, which can deteriorate the performance of the platoon. To address these problems, this study proposes a robust cooperative control (RCC) strategy to maneuver the vehicles in the platoon cooperatively to counteract the impacts of both types of disturbances. The RCC strategy is developed based on a minimax problem, where the maximization subproblem seeks to find the worst inputs for the uncertainty terms in the vehicle dynamics equation to minimize the platoon performance, while the minimization subproblem seeks to find the optimal control decisions for all subsequent vehicles to maximize the platoon performance in the worst case. To solve the minimax problem, this study proposes a globally convergent solution algorithm. It can solve the minimax problem very efficiently to enable real time deployment of the RCC strategy. Numerical application indicates that compared to the existing methods, the RCC strategy can dramatically improve the robustness of the CAV platoon against the uncertain vehicle dynamics induced by both vehicle state detection errors and vehicle control errors. Therefore, it can maneuver the CAV platoon safely and efficiently in a real-world driving environment. [ABSTRACT FROM AUTHOR]

The H ∞ control method is one of the most widely used methods of robust control theory, in which system stability can be guaranteed by using linear matrix inequalities (LMIs) and Lyapunov theory. The presence of various uncertainties such as parametric uncertainty, time delay, sensors/actuators faults and failure is inevitable in the active control of the building structures. This paper focuses on the uncertain structural control system, in which the controller synthesis process is complicated by a set of uncertainties and the generation of large and sparse matrices. Synthesizing a sub-optimal controller for this uncertain structural control system is done according to the new approach and to validate it, a comparison with the controller of critically damped conditions(CD) as an ideal state is performed. In order to compare the two mentioned controllers, two 3- and 8-story shear structures excited by synthetic earthquake are considered to assess the responses. The simultaneous presence of various types of uncertainties is examined and their effects on the results are investigated. The results indicate that the sub-optimal designed H ∞ robust controller displays good robustness against time delay; however, parametric uncertainty was evaluated as an unfavorable effect for the allowable time delay. Despite the negative impact of the parametric uncertainties, it was illustrated that the nominal sensors/actuators faults do not affect the allowable time delay. • Active control algorithm for uncertain systems. • Critically damped state in active control. • Sensitivity analysis for mass, stiffness, and damping parameters. • Different fault ranges of active control system sensors are compared with each other. [ABSTRACT FROM AUTHOR]

This work is concerned with solving neural network-based feedback controllers efficiently for optimal control problems. We first conduct a comparative study of two prevalent approaches: offline supervised learning and online direct policy optimization. Albeit the training part of the supervised learning approach is relatively easy, the success of the method heavily depends on the optimal control dataset generated by open-loop optimal control solvers. In contrast, direct policy optimization turns the optimal control problem into an optimization problem directly without any requirement of pre-computing, but the dynamics-related objective can be hard to optimize when the problem is complicated. Our results underscore the superiority of offline supervised learning in terms of both optimality and training time. To overcome the main challenges, dataset and optimization , in the two approaches respectively, we complement them and propose the Pre-train and Fine-tune strategy as a unified training paradigm for optimal feedback control, which further improves the performance and robustness significantly. Our code is accessible at https://github.com/yzhao98/DeepOptimalControl. • Benchmarking offline vs. online learning in neural network control. • Main challenges: dataset for offline, optimization for online. • Offline learning outperforms online in optimality and speed. • Proposing "Pre-Train and Fine-Tune" paradigm for better, robust control. [ABSTRACT FROM AUTHOR]

In this study, we developed a saturated super-twisting algorithm for autonomous underwater vehicles to address the trajectory tracking problem. On the basis of Lyapunov arguments, we performed a stability analysis of the resulting closed-loop system under the proposed controller, considering the MIMO system dynamics, parametric uncertainties, and external disturbances. Finally, we conducted several real-time experiments to demonstrate the effectiveness and robustness of the proposed control solution as compared to the traditional super-twisting algorithm. • A saturated super-twisting algorithm is proposed for autonomous underwater vehicles. • Lyapunov-based stability analysis of the resulting closed-loop system is performed. • Real-time experiments demonstrate the effectiveness and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

This article considers the problem of asynchronous attack tolerant control for a type of Markov jump cyber-physical systems (MJCPSs) suffering hybrid cyber-attacks. In the measurement channel, the Denial-of-Service (DoS) attack and deception attack are cogitated simultaneously. Meanwhile, the impact generated by the deficiency of plant mode information due to cyber-attacks is described via the partial unknown mode transition probability and the asynchronization between the plant and the controller. On these bases, a detected-mode-dependent asynchronous dynamic controller with a certain capacity to resist malevolent cyber-attacks is proposed. Then, to guarantee the stochastic stability and reach the designated H ∞ performance for MJCPSs under hybrid cyber-attacks, sufficient conditions are given by means of some decoupling technologies. Finally, the effectiveness of the presented control tactic is demonstrated, which is supported by an application on a linearized F-404 engine system. • A dynamic control strategy depending on compromised output is proposed. • Imperfect mode information is considered in the design process. • Conditions with less conservativeness are deduced. [ABSTRACT FROM AUTHOR]

In this paper, we propose a saturated fuzzy logic system (FLS) based robust tracking control method for a marine manipulator. To handle time-varying output constraints, we introduce a tangent barrier Lyapunov function (TBLF). Additionally, we employ a FLS to approximate the model uncertainties and unknown disturbances, ensuring no estimation drift through the use of a smooth projection function. To accurately simulate real operation scenarios, we consider state delays and input saturation. The state delay is addressed using a Lyapunov–Krasovskii functional technique. Through the developed control strategy, it is guaranteed that the tracking errors converge to the neighborhood of zero as the time approaches infinity, while respecting output constraints and input saturation. The effectiveness of the proposed controller is validated through extensive simulations. • A fuzzy logic system based controller is proposed for a marine manipulator • Model uncertainties, disturbances, constraints, and input saturation are considered • Projections are designed to ensure estimated weights remain bounded • Barrier Lyapunov functions are used to guarantee adherence to output constraints • Lyapunov-Krasovskii functions are employed to handle state delays [ABSTRACT FROM AUTHOR]

This paper investigates the bipartite output consensus tracking problem for disturbed agent networks where the agents have different state dimensions. A prescribed time robust coordination control scheme is proposed, which consists of a leader-state observer and an output-regulation-based robust controller that both converge within a preset time. Time-scaling functions with preset parameters T 1 and T 2 respectively take effect in the observer and the controller, and by computing the output regulation matrix which turns the output errors of followers tracking the heterogeneous leader into their state errors, the model-mismatched agents could reach bipartite output tracking consensus within a total duration of T 1 + T 2. Moreover, neural networks are deployed to estimate and eliminate the unknown disturbances. Thorough Lyapunov-based derivations approve the preset-time convergence and robustness for the controller. Numerical simulations are performed to verify the efficacy of the proposed control scheme. • The bipartite output consensus tracking control problem of heterogeneous multi- agent systems subject to unknown disturbances is addressed. • The integration of the distributed prescribed time observer and the prescribed time controller can ensure that the observation errors and tracking errors converge within the preset time. • Neural networks are deployed to estimate and eliminate unknown disturbances, and the communication topology is extended to a structural balance signed graph. • By applying the output regulation method in the controller, all the follower agents are able to track a model-mismatched leader. [ABSTRACT FROM AUTHOR]

[Display omitted] • Exothermic CSTR shows the unit intensification process by cascade feedback. • Cascade feedback provides robustness for unit intensification of chemical reactors. • Together robust temperature control and Lagrange-based feedback for intensification. • Controversy on unit intensification can be solved by robust and optimal feedback. • Minimum time stabilization with robust temperature control for unit intensification. Process intensification has recently opened challenges to enhance production, performance and yielding by distinct variables related to stable operation, process configuration, and economical and environmental concerns. Indeed, the process intensification offers scientific opportunities and challenges onto processes operation to achieve the indicators of the sustainable development. Here, the intensification problem is formulated as a cascade feedback interconnection, where the temperature is robustly stabilized and the minim-time stabilization of the product concentration is simultaneously tackled. The contribution deals with a CSTR whose convergence time for stable steady-state operation can be minimized, which promotes: (i) minimizing the transitive operation to reduce the yielding deviation and (ii) enhancing the production performance along larger time with a given reaction volume. In this sense, the contribution shows that unit intensification of chemical reactors is feasible when reconfiguration, optimal design or other intensification alternatives are unavailable. [ABSTRACT FROM AUTHOR]

To enhance the precision of on-orbit assembly for large space optical telescopes, the control of attitude motion in the Autonomous Space-based Simulator (ASS) is imperative. This paper presents a robust finite-time trajectory tracking control method aimed at improving the position and attitude tracking performance of ASS. Considering the dynamic characteristics inherent in ASS, we introduce an improved non-singular fast terminal sliding mode controller (INFTSMC) incorporating an adaptive fuzzy mechanism to attain finite-time error convergence and robust control. The adaptive fuzzy logic control (AFLC) method yields an equivalent gain, eliminating discontinuous switching in terminal sliding mode control (TSMC). This reduction minimizes chattering in control inputs and compensates for uncertainties and time-varying disturbances within the dynamic model. The proposed method's effectiveness is validated through comprehensive simulation and experimental studies. • The Adaptive Fuzzy Improved Non-singular Fast Terminal Sliding Mode Control strategy is designed. • Precise tracking of Autonomous Space-based Simulators with uncertainties and external disturbances is achieved. • The proposed control scheme ensures finite-time convergence of trajectory tracking errors. [ABSTRACT FROM AUTHOR]

The launch and recovery (L&R) of a small vessel from a large mothership occurs in various settings, such as the rescue of stranded personnel, extraction of unmanned autonomous sea monitoring sensors and the deployment of rescue submersibles. L&R of a small vessel, from a large mothership, at high sea states is difficult and is often avoided for safety reasons. This paper proposes sliding mode controllers to automate the process involving a modified davit crane in which the suspension point is movable. This is the first time a robust control strategy has been used to tackle this specific maritime problem. In this work, a second-order sliding mode controller is used to change the length of the cable during the recovery process and ensures that a pre-defined profile for the cable's length is followed. This controller alone proves to be sufficient for a safe recovery at low sea states. However, for higher sea states, the small vessel and mothership collide under certain circumstances. An integral sliding mode controller is introduced to adjust the crane's boom, which modifies the position of the suspension point, such that the oscillations of the small vessel are minimized. This controller aims to keep the distance between the mothership's hull and the centre of the small vessel at a constant value by adjusting the position of the crane's boom and reducing the swing angle. The overall control algorithm is found to be very effective, compared to the fixed boom case, ensuring that collisions do not occur under any of the tested configurations, and a safe distance is maintained between the mothership and the small vessel. The performance evaluation of the controller predicts that it will safely operate in rough sea states for any initial combination of swing angle and velocity, if a safe recovery can be carried out at a lower sea state for the same initial conditions. [ABSTRACT FROM AUTHOR]

• RST controller with droop method is proposed to resolve instability of DC microgrid. • Enabling percentage load current sharing between DC-DC converters is considered. • Proposed control tested with many disturbances for performance evaluation. • HIL testing is implemented to verify the efficiency of the proposed strategy. • Good performance under CPL, CVL, and uncertainty system parameters is verified. DC microgrids are appealing in distribution networks due to their high efficacy, stability, and ease of incorporation with renewable energy resources. In order to maintain system reliability, load sharing is crucial, because disturbances such as the constant power load (CPL), constant voltage load (CVL), uncertainty parameters, and variations in input voltage may result in instability. The conventional droop control method has been frequently employed to regulate the DC microgrid. However, under significant disturbances, conventional droop control is not suitable for achieving both accurate current sharing and acceptable voltage regulation. To tackle these limitations, this paper introduces a robust control for sharing load current and regulating DC bus voltage in a DC microgrid feeding a CPL and CVL. The proposed approach is split into two loops. The main loop, which is based on an RST controller, is used to regulate the current load sharing between two parallel boost DC/DC converters, handle the instability issue caused by disturbances, and improve system reliability. The DC bus voltage is adjusted using the secondary loop built using the proportional-integral (PI) controller. The key benefits are precise voltage regulation, load power sharing, and good reliability. Aside from that, the RST controller has great resilience, quick dynamic response, and strong stability for significant load fluctuations. The feasibility and efficacy of the proposed approach are confirmed by time-domain simulation results, and hardware-in-the-loop (HIL) testing under various scenarios. The obtained findings show that the suggested control strategy is capable of ensuring proportionate power-sharing while dealing with DC voltage instability under CPL, CVL, uncertainty parameters, and input voltage fluctuations. [ABSTRACT FROM AUTHOR]

• The dynamic controllability and strategies of the DCRED-IC process is investigated. • Composition controller is applied to precisely control the feed ratio of EO and H2O. • The anti-disturbance performance of two structures is compared by ISE. • The improved structure (CS2) is more robust and shows gratifying dynamic responses. The double-column reactive-extractive distillation (DCRED) process is a promisingly intensified compactness configuration for separating ternary water-containing azeotrope(s) by introducing the external reactions, such as the typical common adopted epoxy ethane (EO) hydration reaction. However, limited investigation has focused on the dynamic control analysis for the highly strong coupled DCRED process. In this paper, the efficient and robust control structure is proposed to achieve the dynamic operation of the DCRED process for the system of ethyl acetate/isopropanol/water. The composition controller is applied to precisely control the feed ratio of EO and water, thereby ensuring the qualified products when facing ± 10 % feed flowrate disturbances and ± 5 % feed composition disturbances. This work provides an alternative control strategy for the DCRED process, which is beneficial for its practical industrial applications. [Display omitted] [ABSTRACT FROM AUTHOR]

• Compact UAV leveraging Coandă effect for exceptional maneuverability. • Exploring theoretical study, mechatronic design, and manufacturing complexities of Coandă UAV. • First application of Fuzzy Logic Controller to Coandă-effect UAV, surpassing PID control. • Advantageous Sugeno model provides faster, more robust control in uncertain environments. • Systematic comparison shows FLC strategy outperforms PID approach by 35.23% in settling time. • Comprehensive experimentation in Matlab® and Simulink for microcontroller-based UAV. • Groundbreaking fusion of novel design and advanced control techniques for Coandă UAVs. • Addressing unique aerodynamic challenges, contributing significantly to UAV research. Recent years have experienced a notable surge in unmanned aerial vehicles (UAV) research, prompting exploration into innovative concepts. This paper introduces a compact UAV harnessing the Coandă effect, an underexplored phenomenon in fluid mechanics. Featuring a single lift motor and two types of flaps, this UAV offers exceptional maneuverability, presenting significant challenges compared to conventional multi-rotor UAVs. To address these challenges, we explore the theoretical study, mechatronic design, and manufacturing complexities of the Coandă UAV. Emphasizing the distinctiveness of our work, we assess a Fuzzy Logic Controller (FLC) for UAV stabilization, marking the first application of such techniques to a Coandă-effect UAV, in contrast to the Proportional-Integral-Derivative (PID) control employed by other researchers. This innovative application of Fuzzy logic, particularly the Sugeno model, proves advantageous, offering faster and more robust control in uncertain or noisy environments. The proposed FLC strategy is systematically compared against a classical PID control approach, formulated based on the Mamdani and Sugeno models, optimized and manually tuned using a genetic algorithm. Our results showcase a significantly improved settling time of 0.417 s with the FLC strategy, surpassing the PID control approach by 35.23%. To substantiate our findings, we present comprehensive experimentation conducted at both software and hardware levels using Matlab® and Simulink for a microcontroller-based UAV. This groundbreaking fusion of novel design and advanced control techniques not only addresses the unique challenges posed by the Coandă UAV's aerodynamics but also contributes significantly to the field of UAV research. [ABSTRACT FROM AUTHOR]

This paper investigates the attitude control system of a flexible satellite consisting of a rigid main-satellite, a partially expanded coilable mast and a rigid sub-satellite. Multiple critical information such as the length of deployed coilable mast, system damping and stiffness matrices are uncertain. The satellite also suffers from parameter perturbation, external disturbance, flexible vibration and failed gyroscope. It is challenging but significant to achieve high-precision attitude control under these uncertainties. To address this issue, the attitude dynamic model which describes both bending and torsion movement of coilable mast is built based on the Lagrange theory firstly. Then an extended state observer is designed to estimate both angular velocity and system lumped disturbance. The nonsingular terminal sliding mode control law is proposed based on extended state observer to drive the main satellite tracking desired attitude. Stability analysis indicates that the estimation errors of extended state observer and attitude tracking errors can converge into a small neighborhood of the origin. The convergence domain of system states can be presented by inequalities which guides the selection of controller gains. Finally, numerical simulations are carried out to verify the effectiveness of proposed control law with different parameters. The steady-state accuracy can be improved by selecting appropriate parameters according to the provided guideline. Moreover, compared with relevant researches, simulation results demonstrate that the proposed method can achieve higher control accuracy and require lower energy consumption. The vibration of coilable mast can be suppressed passively and the stability of closed-loop system can be guaranteed. [ABSTRACT FROM AUTHOR]

The paper develops a methodology for the design of coherent equalizing filters for quantum communication channels. Given a linear quantum system model of a quantum communication channel, the aim is to obtain another quantum system which, when coupled with the original system, mitigates degrading effects of the environment. The main result of the paper is a systematic equalizer synthesis algorithm which relies on methods of state–space robust control design via semidefinite programming. [ABSTRACT FROM AUTHOR]

This paper is about robust Model Predictive Control (MPC) for linear systems with additive and multiplicative uncertainty. A novel class of configuration-constrained polytopic robust forward invariant tubes is introduced, which admit a joint parameterization of their facets and vertices. They are the foundation for the development of novel Configuration-Constrained Tube MPC (CCTMPC) controllers that freely optimize the shape of their polytopic tube, subject to conic vertex configuration constraints, as well as associated vertex control laws by solving convex optimization problems online. It is shown that CCTMPC is—under appropriate assumptions—systematically less conservative than Rigid- and Homothetic- Tube MPC. Additionally, it is proven that there exist control systems for which CCTMPC is less conservative than Elastic Tube MPC, Disturbance Affine Feedback MPC, and Fully Parameterized Tube MPC. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the cooperative robust consensus tracking problem of uncertain networked Euler-Lagrange systems (NELSs) with time-varying delays and output constraints. By systematically integrating the neural network (NN) adaptive technique and the logarithmic type Barrier Lyapunov Function (BLF) in combination with the additional robust control law, two distributed robust consensus schemes for uncertain NELSs are proposed for two cases of time-varying communication and input delays respectively, which can fully guarantee to constrain the output consensus error within a safety region simultaneously. Furthermore, numerical simulation examples are provided to demonstrate the comparable potential advantages of the proposed robust control law over some existing algorithms, including adaptability, stability, and robustness, as well as delay effects. • An NN-based robust controller is designed to effectively handle the uncertainty of NELSs. • Combining NN with BLF ensures the adaptability, stability, and robustness of the consensus tracking scheme for NELSs. • A unified analytical framework is developed by deriving upper bounds on the allowed values of communication and input delays. [ABSTRACT FROM AUTHOR]

In this paper, to resist stochastic cyber attacks and suppress the influence of the exogenous disturbance in synchronization of multiplex neural networks, an adaptive quantization control strategy is proposed. To overcome uncertainties and represent constraints in communication, an adaptive quantitative controller is designed, which is used to arrive outer synchronized behavior with an H ∞ performance between the multiplex neural networks under stochastic cyber attacks and exogenous disturbance. The node of the multiplex networks is neural networks which is named neural sub-networks. It is assumed that the neural sub-networks in the multiplex networks with delay topology are coupled by nonlinear functions. Based on Lyapunov stability method, the conditions for the multiplex networks to achieve synchronization with an H ∞ performance are presented. Finally, numerical example illustrates the effective of the theoretical framework. [ABSTRACT FROM AUTHOR]

In this article, the event-triggered near-optimal control issue is studied for the input-constrained uncertain system with the input-to-state stability (ISS) attribute. In the proposed approach, we design a near-optimal controller and an adaptive event-triggered mechanism (ETM) to optimize the cost function and reduce the redundant computational consumption. Toward this end, the robust control problem of the uncertain system is first converted into the near-optimal control issue for the nominal system via the designed cost function. Then, for reducing the redundant computational consumption for uncertain systems, the adaptive ETM is distinguishingly devised for the matched uncertainty. Subsequently, a sufficient condition is offered to ensure the asymptotical stability for the discrete-time uncertain system with the ISS attribute under the event-triggered controller. To further improve the control performance for the closed-loop systems, the goal representation adaptive critic design approach is proposed in the event-triggered context with non-periodic weight updating rules for neural networks are designed for the controlled system. Moreover, the corresponding convergence of the proposed control approach is considered. Finally, the effectiveness of the suggested approach is demonstrated by two simulation cases. [ABSTRACT FROM AUTHOR]

This study provides some empirical evidence on the dynamic linkage between modern renewable (clean) energy consumption (SDG-7.2) and economic growth using the Dynamic Common-Correlated Effects (DCCE) estimation Approach in 20 Sub-Saharan African (SSA) countries for the period from 2000 to 2017. The study also aims to examine the role of traditional energy dependence, income, and education in the linkage using the dynamic panel data threshold effects model with endogenous regressors estimations. The findings indicate that an increase in modern renewable energy in the total final energy mix is not conducive to economic growth (induces a certain economic cost) in the SSA region. This could be explained by the fact that modern renewables are not being utilized efficiently and fully in the region. However, the modern renewables-growth linkage depends on traditional energy dependence, the level of income, and education. Specifically, the negative effect of modern renewables tends to weaken in the regimes with lower traditional energy intensity, higher levels of income and education. Our main conclusion remains robust to additional control variables considered in the analysis. Hence, while expanding the use of clean energy technologies, basic enabling environments need to be created to make a smooth clean energy transition and reduce its economic costs. [ABSTRACT FROM AUTHOR]

This study processes the adaptive robust control problem in a task space for a mobile manipulator with uncertain dynamics and external disturbances based on radial basis function neural networks(RBFNN) and a nonlinear state observer. Owing to the high nonlinearity, strong coupling, and unknown uncertainty characteristics of the mobile manipulators, their control poses a considerable challenge. An adaptive robust control strategy for nonlinear mobile robot systems with unknown uncertainties and disturbances based on the RBFNN and state observer is proposed in the operating space. First, a feedforward-feedback virtual speed generator is developed based on the feedforward control and backstepping method to implement virtual speed tracking control in the operation space to make the positional error asymptotically stable. Then, a model-independent RBFNN based adaptive robust controller (ARBFNNC) with the state observer is proposed, which converts the virtual speed input into the control torque of the actual mobile manipulator to achieve precise position tracking in the task space, and theoretical analysis performed through Lyapunov stability theory shows the global asymptotic stability of a system under the control of the proposed method. Finally, simulation results confirm the effectiveness of the proposed control method in position regulation and tracking control of the nonlinear mobile manipulator. [ABSTRACT FROM AUTHOR]

This work considers robust moisture content and activity control in a continuous convective drying for particulate bio-materials. The major process uncertainty is in the kinetics of the second (falling-rate) drying period. Robust feedback controllers in the multi-variable and de-central case are designed to guarantee desired product specifications. The advantages of the robust controllers are discussed in comparison with a conventional PI controller. The case study presented is the drying of baker's yeast in a fluidized bed dryer. The results show constant performances concerning the uncertainty of the drying kinetics and disturbance rejection at different operating points. • Robust multivariable controller design for convective drying. • Constant controller performance concerning various process uncertainties and operating points. • Baker's yeast in fluidized bed dryer as case study. [ABSTRACT FROM AUTHOR]

• Parabolic trough control with irradiation class dependent parameter. • All sky imager for the generation of spatial irradiation maps. • Robustness analyses for real applications. Parabolic trough (PT) power plants focus the sun's irradiation onto an absorber tube using collector mirrors to increase the temperature of the heat transfer medium (HTF). In the power plant, the HTF is used to generate electricity. To heat up the HTF, PT solar fields are set up in large areas and pipes are used to distribute the HTF in the solar field. The control system adjusts the temperature by changing the mass flow into the solar field and the focusing of the individual collectors. Transient conditions such as cloud passage are the main challenge for the system. State of the art control schemes use information from a few irradiation measuring points in the solar field. These measurement points are precise for the individual location but cannot accurately represent the irradiation situation in the whole solar field. In this paper, a control system that uses spatially resolved irradiation information from all sky imager systems is investigated. First, the control system was developed and tested in various simulation runs under ideal conditions. The focus of this work is to investigate the robustness of the control concept under non-ideal conditions. For this purpose, the simulation conditions are adapted to real situations and the behavior of the control system is analyzed. A robust behavior of the control system is shown in the simulations under different non-ideal scenarios. Overall, an improvement in electrical yield of 1.4% can be achieved compared to a reference without access to spatially resolved irradiance data. [ABSTRACT FROM AUTHOR]

In this paper, distributed state and output feedback control law based on the dynamic event-triggered mechanism are proposed to solve the robust cooperative output regulation problem for a class of general linear uncertain multi-agent systems (MASs) subject to external disturbances. The discrete-time control law proposed for each agent is equivalent to a discrete one and thus directly implementable in the digital platform. Our design can handle parametric uncertainties, reject external disturbances and track a dynamic leader while excluding the Zeno phenomenon by providing the existence of a lower bound for inter-event times of each agent, the efficiency of which is illustrated by a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, an observer-based type-2 fuzzy method is proposed for control and energy management strategy (EMS) of the hybrid energy storage system (HESS) which can be composed of the fuel cell (FC), battery (BA), and supercapacitor (SC). The objective and main contribution of the suggested strategy is to provide: 1) Appropriate tracking performance of power sources by an observer-based control method in the presence of noise and signal ripples. 2) An observer-based composite adaptive type-2 fuzzy (OCAT2F) to approximate the voltage of power sources. 3) A dynamical model of DC-bus to guarantee the stability of closed-loop system. 4) An intelligent EMS. To have a high-power supply, the proposed EMS includes two parts; a type-2 fuzzy logic control rule table (T2FLCRT), and an observer-based robust adaptive fuzzy type-2 fuzzy (ORAT2F). Furthermore, stability analyses of the closed-loop system are provided by the input-output linearization (I-OL) approach and based on the Lyapunov theorem. The simulation results of the proposed control scheme under MATLAB/Simulink indicate that the suggested strategy can provide a suitable control performance, and stability of the whole system is achieved. • An observer-based control method in the presence of noise and signal ripples. • Type-2 adaptive fuzzy estimation of power source voltages to reduce costs and sensors. • Improvement of the energy management strategy by type-2 fuzzy system. • Dynamical modeling of hybrid energy systems including fuel cell, battery, supercapacitor, and DC-bus system. [ABSTRACT FROM AUTHOR]

A new stochastic control model for the long-run environmental management of rivers is mathematically and numerically analyzed, focusing on a modern sediment replenishment problem with unique nonsmooth and nonlinear properties. Rational inattention as a novel adaptive strategy to collect information and intervene against the target system is modeled using Erlangization. The system dynamics containing the river discharge following a continuous-state branching with an immigration-type process and the controlled sediment storage dynamics lead to a nonsmooth and nonlocal infinitesimal generator. Modeling uncertainty, which is ubiquitous in certain applications, is considered in a robust control framework in which deviations between the benchmark and distorted models are penalized through relative entropy. The partial integro-differential Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation as an optimality equation is derived, and its uniqueness, existence, and optimality are discussed. A monotone finite difference scheme guaranteeing the boundedness and uniqueness of numerical solutions is proposed to discretize the HJBI equation and is verified based on manufactured solutions. Model applications are also conducted with the parameter values identified from the available data and physical formulae. The computational results suggest that environmental management should be rationally inattentive in a state-dependent and adaptive manner. • Sediment replenishment under discrete observation and model uncertainty is studied. • Novel streamflow-sediment dynamics driven by a branching-type process is proposed. • An adaptive Erlangization is proposed as a feedback replenishment strategy. • The associated HJB equation is analyzed both mathematically and numerically. • A realistic problem is analyzed as well using identified parameter values. [ABSTRACT FROM AUTHOR]

The problem of robustly stabilizing and estimating the state of an open-loop unstable continuous exothermic tubular reactor with constrained control is solved with a comprehensive methodology that includes: (i) a control–estimation (CE) system made by a saturated proportional–integral temperature control with anti-windup protector, and a low-order noninterfering (computationally efficient) nonlinear (NL) geometric state estimator, and (ii) closed-loop robust functioning conditions coupled with sensor location, control limits and gain selection criteria. The CE system is obtained as a simplified-robustified realization of the implementation with a robust NL estimator of a constrained NL state-feedback passive or model-predictive robust stabilizing control. The design methodology is illustrated and tested with a representative example through numerical simulation. • Control–estimation (CE) system design for exothermic tubular reactors. • Robust stability conditions with sensor location, control limit and gain selection criteria. • Solvability in terms of passivity, detectability, and bifurcation. • CE system: PI control with antiwindup protection plus noninterfering state estimator. • Estimator built with efficient PDE-to-ODE discretization. [ABSTRACT FROM AUTHOR]