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In this paper, an anti-unwinding adaptive robust backstepping control law is proposed to solve the attitude control problem of rigid spacecraft with external disturbances and inertia uncertainties. A new variable based on hyperbolic sine functions is designed to prevent the unwinding phenomenon, and then a novel robust backstepping controller is developed. Initially, a nonlinear tracking law is designed to obtain the desired angular velocity and to ensure that the attitude maneuvering system could have two equilibrium points. Subsequently, an adaptive robust control law is devised, which incorporates the adaptive estimation method to update the inertial parameters in real-time. Moreover, the bounded external disturbances are taken into account in the design of the control law, and thus the robustness of the attitude control system is improved. Furthermore, the global asymptotic stability of the attitude control system is verified by the Lyapunov-Krasovskii theorem. Finally, numerical simulations are carried out to demonstrate the effectiveness and robustness of the proposed control law, as well as the anti-unwinding characteristics are perfectly validated during the attitude maneuver of rigid spacecraft. [ABSTRACT FROM AUTHOR]

This study focuses on enhancing the control precision and efficiency of a two-degree-of-freedom (2-DOF) space manipulator used for active space debris removal. The unpredictable space environment introduces large uncertainties, which introduces unique challenges beyond the capabilities of a standalone computed torque controller and degrades control performance. To address this problem, a robust controller is developed, integrating traditional techniques such as sliding mode and computed torque control with a Neural Network framework. This synergy leverages both methods' strengths—conventional controls' accuracy and Neural Network's adaptability. The integration of Neural Network-based sliding mode control complements the robustness of computed torque control by actively mitigating uncertainties and disturbances inherent in the space environment. The 2-DOF manipulator's state variables model the system dynamics, necessitating accurate relative motion estimation between the manipulator and debris. The global asymptotic stability of the developed algorithm is demonstrated through the Lyapunov theorem, guaranteeing error convergence to zero. The convergence, stability, precision, tracking errors, and responsiveness of the controller have been analysed and validated by the MATLAB Simulink simulations. The novel approach's performance effectiveness is substantiated by numerical simulations and a comparative analysis with conventional computed torque control. Outcomes highlight the superior precision and efficiency in manipulator tracking the trajectory, validating the integrated controller's potential for successful active space debris removal. • Neural network-based sliding mode computed torque control (NSMCTC) approach ensures precise space manipulator position tracking. • NSMCTC reduces chattering and improves convergence for enhanced trajectory control in dynamic space environments. • Lyapunov stability theorem validates uniform and ultimate boundedness, ensuring global asymptotic stability. • Self-learning capability and low computational complexity distinguish NSMCTC from traditional computed torque control methods. • Potential applications extend to spacecraft manipulator attitude control, offering adaptability for safe debris capture in multidimensional dynamics. [ABSTRACT FROM AUTHOR]

A pressure control method that incorporates parameter adaptation has been devised for the pressure control system of pintle solid rocket motors, which exhibit notable nonlinearities and uncertainties in parameters. Firstly, the mathematical relationship between pressure and pintle displacement is derived using the zero-dimensional interior ballistic model., and the state space equation is deduced by incorporating the dynamics of motor inertia load. Secondly, by capitalizing on the robustness of robust control, the parameter identification capabilities of adaptive control, and the rigor of Lyapunov stability theory, the proposed approach enables precise control of the pintle position while effectively addressing uncertainties, thereby enhancing the overall performance and stability of the system. Finally, the aforementioned theoretical framework has been validated through rigorous numerical simulations. The findings demonstrate a notable enhancement in the pressure response speed of the adaptive robust controller, with improvements of 10.01 % and 13.16 %, 0.03 % and 1.54 %, 0.06 % and 22.39 % in different orders of magnitude of free volume as compared to the robust controller and PID controller, respectively. Moreover, it is evident that the adaptive robust controller relies on the accuracy of the model. The applied controller demonstrates notable advantages in terms of stability, robustness, and accuracy, thereby substantially enhancing the overall performance of the pressure control system. • The state space equation for the pressure of the pintle solid rocket motors is derived. • The pressure dynamics equation and the motor inertial load equation are combined. • An adaptive robust controller with respect to pressure is designed. • The controller response laws for various free volumes, pressure index and characteristic velocity are obtained. • The adaptive robust controller exhibits a higher dependence on model accuracy is observed. [ABSTRACT FROM AUTHOR]

In this paper, a delayed distributed proportional control is developed for a class of multiple-input-multiple-output systems in order to reduce the effects of external disturbances and uncertain dynamics. The system under consideration is a three degrees-of-freedom planar robot, consisting of an active joint driving the first link and passive flexible joints for the second and third links. The control challenge for this system is to drive the flexible arm around the desired setpoint or trajectory by controlling the first link. Actually, by using a specific Lyapunov-Krasovskii storage functional associated with a constrained optimization problem, the robustness of the proposed proportional retarded control action is achieved. This analysis, together with the attractive ellipsoid concept allows to conclude with Ultimately-Uniformly-Bounded stability. The closed-loop control algorithm is tested in numerical simulation and a comparative study with some existing recent control schemes based on the performance error is presented. • The proposed control scheme belongs to a class of multivariable proportional time delay distributed controller. • After finite time the trajectories of a flexible manipulator stay within reduced size attractive ellipsoid. • Ultimate Uniformly Bounded Stability of the controlled system is demonstrated by using a Lyapunov-Krasovskii functional. • The proposed algorithm can also be adapted for a class of nonlinear uncertain/disturbed multivariable output systems. • A sufficient condition to guarantee the feasibility of specific Bilinear Matrix Inequality is presented. [ABSTRACT FROM AUTHOR]

—Currently, most vehicles are generally equipped with roll stability control system or electronic stability control system to improve stability. However, when encountering emergency obstacle avoidance and similar conditions, roll stability control system may compete with path tracking control system for vehicle control, resulting in the inability to balance path tracking and roll stability. Therefore, a closed-loop feedback Nash game collaborative control method is proposed to solve the collaborative problem between the two control systems. Firstly, a fifth-order vehicle model with coupled yaw-roll dynamics is presented, which was extended to a vehicle-road closed-loop model based on lateral position, yaw angle and road preview information. Secondly, the path tracking control system and roll stability control system are regarded as two agents in the dynamic game control process. On this basis, a Nash game collaborative control strategy is designed by fully considering the control interaction between two game agents. Finally, considering the influence of uncertain disturbances, a finite-time minimax robust control strategy is proposed by using minimax optimization theory. Simulation and hardware in loop experiments show that the minimax robust regulator based on Nash game controller can effectively improve the path tracking and roll stability performance of vehicles. Moreover, the regulator improves the robustness of the system when it is subjected to bounded uncertain lateral disturbances. [ABSTRACT FROM AUTHOR]

• A novel active control method is proposed to suppress the milling chatter of thin-walled workpieces. • The non-smooth properties, time delays, multi-modal participation, and varying dynamic characteristics are synthesized together in controller design for the first time. • The combination of robust control and time delay controller enhances the control effect and reduces the control voltage to the single robust control. Chatter phenomena commonly plague the milling process of flexible workpieces, leading to undesirable fluctuations in accuracy and diminished machine efficiency. Limited by complex dynamics of this process, the non-smooth properties, time delays, multi-mode participation, and varying dynamic characteristics have never been considered together in active control. This paper proposes a novel active control method to suppress milling chatter of flexible workpieces by synthesizing these factors together in controller design for the first time. The dynamic model for milling processes of a cantilevered plate, which is regarded as an examined example of flexible structure, is transformed from a time delay differential equation with varying coefficients to a real-time differential equation with constant coefficients by uncertainty transformation. A robust combined time delay control method is tailored for the uncertainty system via combining a robust controller with synthesis method and an active time delay controller. Both simulations and experiments validate the effectiveness and performance advantages. Following the implementation of experiments, the maximum stable milling depth experiences a notable improvement from 0.1 mm to 0.8 mm after control, and is larger than that of 0.6 mm under single robust control. Additionally, under the given milling conditions, the control voltages of robust combined time delay control exhibit a 25 % and 11 % reduction compared to those of the robust control method. The novel combination of a robust controller and time delay controller enhances the control effect and reduces the control voltage. [Display omitted] [ABSTRACT FROM AUTHOR]

The new railway mobility paradigm Virtual Coupling, recognized as one of the most promising solutions to face the ever-increasing demand in railway transportation and the saturation of network capacity, enables two or more trains to virtually couple in a single convoy, hence reducing the headway among them. Within this context, the design of an effective and robust Virtual Coupling control strategy for uncertain nonlinear heterogeneous train convoys, able to simultaneously cope with uncertain nonlinearities and unexpected/unpredictable external factors, is an open challenge in the railway field. This is very crucial for High-Speed Trains, where, due to the high-speed operating ranges, uncertain factors have a stronger impact on Virtual Coupling performance. To deal with this issue, this work exploits the ability of Deep Reinforcement Learning based strategies in defining an optimal control policy via an iterative exploration of the surrounding unknown environment and without detailed knowledge of the plant dynamics. Specifically, we propose a novel Deep Deterministic Policy Gradient based control strategy to coordinate and manage the High-Speed Trains convoy such that this latter can autonomously adapt its behaviour to all the encountered driving scenarios. The effectiveness of the proposed approach is evaluated via simulation analyses, carried-out via an ad-hoc implemented Virtual Coupling Train System simulation platform. After verifying the efficiency of the training process in ensuring the fulfilment of the Virtual Coupling control objectives, extensive non-trivial simulations, also involving cooperative manoeuvres, are performed for the validation phase. Results confirm how the proposed model-free Deep Deterministic Policy Gradient approach guarantees the Virtual Coupling for nonlinear heterogeneous High-Speed Train convoys despite the co-presence of uncertainties and unknown external factors. Finally, the advantages and the benefits of the proposed data-driven control are disclosed via a comparison analysis against model-based control strategies. [ABSTRACT FROM AUTHOR]

—Currently, most vehicles are generally equipped with roll stability control system or electronic stability control system to improve stability. However, when encountering emergency obstacle avoidance and similar conditions, roll stability control system may compete with path tracking control system for vehicle control, resulting in the inability to balance path tracking and roll stability. Therefore, a closed-loop feedback Nash game collaborative control method is proposed to solve the collaborative problem between the two control systems. Firstly, a fifth-order vehicle model with coupled yaw-roll dynamics is presented, which was extended to a vehicle-road closed-loop model based on lateral position, yaw angle and road preview information. Secondly, the path tracking control system and roll stability control system are regarded as two agents in the dynamic game control process. On this basis, a Nash game collaborative control strategy is designed by fully considering the control interaction between two game agents. Finally, considering the influence of uncertain disturbances, a finite-time minimax robust control strategy is proposed by using minimax optimization theory. Simulation and hardware in loop experiments show that the minimax robust regulator based on Nash game controller can effectively improve the path tracking and roll stability performance of vehicles. Moreover, the regulator improves the robustness of the system when it is subjected to bounded uncertain lateral disturbances. [ABSTRACT FROM AUTHOR]

The new railway mobility paradigm Virtual Coupling, recognized as one of the most promising solutions to face the ever-increasing demand in railway transportation and the saturation of network capacity, enables two or more trains to virtually couple in a single convoy, hence reducing the headway among them. Within this context, the design of an effective and robust Virtual Coupling control strategy for uncertain nonlinear heterogeneous train convoys, able to simultaneously cope with uncertain nonlinearities and unexpected/unpredictable external factors, is an open challenge in the railway field. This is very crucial for High-Speed Trains, where, due to the high-speed operating ranges, uncertain factors have a stronger impact on Virtual Coupling performance. To deal with this issue, this work exploits the ability of Deep Reinforcement Learning based strategies in defining an optimal control policy via an iterative exploration of the surrounding unknown environment and without detailed knowledge of the plant dynamics. Specifically, we propose a novel Deep Deterministic Policy Gradient based control strategy to coordinate and manage the High-Speed Trains convoy such that this latter can autonomously adapt its behaviour to all the encountered driving scenarios. The effectiveness of the proposed approach is evaluated via simulation analyses, carried-out via an ad-hoc implemented Virtual Coupling Train System simulation platform. After verifying the efficiency of the training process in ensuring the fulfilment of the Virtual Coupling control objectives, extensive non-trivial simulations, also involving cooperative manoeuvres, are performed for the validation phase. Results confirm how the proposed model-free Deep Deterministic Policy Gradient approach guarantees the Virtual Coupling for nonlinear heterogeneous High-Speed Train convoys despite the co-presence of uncertainties and unknown external factors. Finally, the advantages and the benefits of the proposed data-driven control are disclosed via a comparison analysis against model-based control strategies. [ABSTRACT FROM AUTHOR]

Utilizing parallel learning technology and an event-triggering mechanism, we introduce an adaptive security tracking control approach for nonlinear systems with both matched and mismatched uncertainties. The methodology commences with an overview of the constraints imposed by the control barrier function (CBF) on the system state, followed by the selection of a suitable CBF. An augmented system is constructed by amalgamating the system state and tracking error, with the mismatched uncertainty being decomposed to introduce an auxiliary system. Incorporating the CBF into the performance metrics ensures that the system state remains within a secure region. Subsequently, the paper employs an event-triggering mechanism to derive an event-based secure Hamiltonian–Jacobi–Bellman (HJB) equation. An adaptive single-critic network, designed using parallel learning technology, approximates the solution to the event-based secure HJB equation. Lastly, the Lyapunov method is employed to demonstrate the uniform ultimate boundedness (UUB) of both the tracking error and weight error. The efficacy of the proposed method is validated through two nonlinear examples. [ABSTRACT FROM AUTHOR]

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, the sensitivity of the control valve to flow coefficient and zero calibration under different operating ranges of the compression system is studied carefully and some recommendations for the users are provided. • Active surge control of a compressor in the presence of uncertainty as well as the external disturbances is investigated. • A disturbance observer-based sliding mode controller is proposed to stabilise the compressor system in the surge area. • The nonlinear feedforward disturbance observer is designed to estimate the abrupt changes taking place in the system. • The proposed disturbance observer-based technique is compared with other techniques under various operational conditions. • The robust performance and stability of the method especially close to the instability boundary is proven numerically. [ABSTRACT FROM AUTHOR]

This paper proposes a tube model predictive control method considering uncertainties of renewable energy generation in the low-voltage distribution network. For relatively low dependence on communication networks, a decentralized voltage control framework is urgently needed to be developed for voltage regulation in the low-voltage distribution network. Opposed to the traditional voltage control techniques, a novel decentralized tube model predictive voltage control (TMPC) strategy is proposed. First, this strategy entails the formulation of a discrete model encompassing distributed photovoltaic units and energy storage systems intertwined with the distribution grid model based on voltage sensitivity. Then, considering the worst case of voltage deviations caused by uncertain and stochastic power fluctuation, we specifically discuss a robust positively invariant to determine the control section of controllers. Further, the strategy employs tube model predictive controllers encompassing nominal and auxiliary controllers in distribution areas. The nominal controller generates a central path for different voltage operating points of each distribution area, and the auxiliary controller makes all trajectories lie in a bounded neighborhood called a tube. The effectiveness of the proposed control strategy is investigated on a test system containing renewable energy resources. • A novel decentralized control structure is proposed to serve voltage control for distribution networks, which can cooperate for accurate voltage control with random and fluctuant renewable energy. • In the nominal controller, operation points of DGs are generated considering the generation and load scheduling. In the auxiliary controller, detailed control objectives and constraints are proposed for suppressing disturbances of DGs. • To balance the conservation and performance, a worst-case-based robust positively invariant is developed to quantify the range of tubes to cope with the randomness. [ABSTRACT FROM AUTHOR]

Distributed energy resources (DER) are integrated into a microgrid through dc-dc power electronic converters. The bidirectional dc-dc converter regulates charging and discharging operations of ESS. Model predictive control (MPC), is a high-performance control technique for these converters, but it is limited in robustness to parameter mismatch, model uncertainties and sensor measurement noise. Therefore, in this paper, an improved hybrid cascade-parallel extended state observer (CP-ESO) is proposed, for model-free predictive control, to guarantee both robustness to parameter/model uncertainties and measurement noise suppression. A novel structurally-adaptive ESO scheme is also proposed to improve the disturbance rejection of CP-ESO during transient response. These results are supported by analysis and design guidelines for the selection of optimal sub-frequencies. Experimental results, for a bidirectional dc-dc boost converter, validate the effectiveness of the proposed methods against model uncertainties, external disturbances from variable input voltage and load, as well as measurement noise. • Analysis and design guidelines for multi-frequency extended state observers (ESO). • Improved cascade-parallel extended state observer with better noise suppression. • Novel structurally-adaptive ESO that gives better transient disturbance rejection. • Experimental validation with a bidirectional dc-dc boost power converter. [ABSTRACT FROM AUTHOR]

Linear control-oriented models are important to represent canal dynamics for designing controllers. This study focuses on hydraulic control structure (gate) modelling to address the complex interdependent behavior inherent in irrigation canals. A comprehensive mathematical model that incorporates the water level with gate-opening to model discharge is introduced for single and multiple canal pool scenarios. The proposed model captures the hydraulic coupling within and among canal pools, a key finding. The model is evaluated extensively under uniform and non-uniform flows across three distinct canals, highlighting the model's applicability to various systems. The uncertainty inherent within the nominal model is also assessed for varying operating conditions and hydraulic parameters. The proposed model is compared with the existing and the Saint-Venant (SV) model, showing improved accuracy in water-level predictions. This advancement in hydraulic modelling contributes to adaptable canal models essential in developing robust controllers to enhance water management in irrigation canals. • A mathematical canal model is developed incorporating water level in hydraulic control structure. • A generalised model structure applicable to an arbitrary number of pools is developed. • The proposed model outperforms existing models in estimating water levels by capturing hydraulic coupling. • The model is evaluated across diverse canal configurations under uniform and non-uniform (backwater) flow conditions. • The model's uncertainty is assessed for varying operational conditions and the roughness coefficient. [ABSTRACT FROM AUTHOR]

We present a novel artificial intelligence approach that encompasses both predictive and prescriptive aspects for the challenging task of model-based control of industrial wood planers. These sophisticated lumber finishing machines are known for the complexity of their operation, and the available data pertaining to the planing process exhibits complex, non-linear patterns. First, we leverage an ensemble of Gaussian Processes with a specialized weighting scheme named Automatic State Matching , achieving a 39% reduction in prediction error for the thickness of the outgoing board compared to conventional industry methods, as corroborated by real-world data. Subsequently, the predictive strategy is utilized in a novel robust control strategy which exploits the properties of Gaussian Processes to prescribe settings for wood planers. An empirical evaluation on simulated data demonstrated the viability of our prescriptive method, resulting in an 83% reduction in deviation from a predetermined target dimension. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for the synthesis of multiple resonant controllers in the voltage regulation loop of uninterruptible power supplies (UPSs) operating in parallel with droop control. The resonant controller gains are calculated through a convex optimization problem aiming to attenuate the rms value of circulating current between the UPSs. Linear matrix inequalities (LMIs) constraints ensure the robust closed-loop robust stability and performance under the insertion or removal of UPSs and load variations. Experimental results from two 3.5 kVA UPSs illustrate the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper introduces theoretical conditions for the computation of a novel type of state-feedback controller that makes use of Linear Parameter Varying approaches for synthesis. The novelty of this new State-Feedback controller lies on the fact that the controller has a fixed structure with constants matrix gains. However, the controller gains are affine on a parameter dependent basis function, which allows the controller to self-schedule according to real-time changes of the varying parameters. This type of controller is conceived with a focus on implementation, as in contrast with most other LPV approaches. The implementation of this Parameter-Dependent State-Feedback controller does not require any online interpolation or matrix inverse operations, independently of the number of varying parameters. The performance of the proposed approach is validated in a Scaled Autonomous Vehicle for steering control in a path tracking application. [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 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]

[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]

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]

• 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]

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]

In this paper, an adversarial deep reinforcement learning-based control method is proposed to address the issue of robust depth tracking of an underactuated autonomous underwater vehicle in the presence of intrinsic coupled dynamics and external disturbances. First, long-short-term-memory neural network is presented to memorize and predict the changes in the state of vehicle, and a cascaded multilayer perception projects the output into action space of vehicle. Subsequently, adversarial deep reinforcement learning scheme is applied to the training of control agent by introducing an adversary which counteracts the control behavior, whereby the agent is enabled to learn the control strategy in different distributions of state transition. For evaluation of the performance, a control agent is pre-trained in simulation environment based on the reliable digital model of a real vehicle, and the simulation environment is paced at one iteration per second to align with real-time operations to ensure the portability of training result. Furthermore, the training cost is also extremely reduced. Finally, experiments are conducted with time-varying disturbances to further prove the feasibility of the proposed learning-based control scheme on a prototype of underwater vehicle in towing tank. Moreover, comparative experiment results show the better robustness performance of the learning-based control agent than that of classic line-of-sight based proportional–integral–derivative and adaptive line-of-sight based proportional–integral–derivative controllers in different scenarios. [ABSTRACT FROM AUTHOR]

The development of a sampled-data robust controller, applied to a 2-DoF helicopter system, is proposed in this paper. The helicopter presents a nonlinear dynamics, which is modeled in this paper using a quasi-LPV approach, thus allowing the application of convex LMI conditions for the determination of the desired controllers. The robustness against effects such as sampling rate variation, noise and modeling approximations is assured mainly through a guaranteed bound for the L 2 norm. Also, a performance index based on the H 2 concept is used for the synthesis procedure, which is one of the main contributions of this paper. An algorithm is proposed to optimize the performance while guaranteeing the desired robustness. The analysis and validation of the proposed synthesis procedure are performed both through computational simulations and by applying the controllers on a physical 2-DoF helicopter, which could be satisfactorily stabilized. [ABSTRACT FROM AUTHOR]

In this paper, the speed and position control of high-speed trains (HSTs) under non-strictly repeated conditions is investigated. Considering the modeling accuracy and control cost, a multi-particle dynamics model is established in a single coordinate system. Based on this model, a novel robust adaptive iterative learning control (RAILC) method is proposed. The Lyapunov function is established to prove that the system state converges in a finite time, and the composite energy function (CEF) is constructed to prove that the control error approaches zero along the iteration axis. The application value in engineering practice of the proposed RAILC is verified by two simulation tests. Existing adaptive iterative learning control (AILC) and adaptive terminal sliding mode control (ATSMC) are introduced as control groups to show the system convergence of the RAILC on the iteration axis and time axis. • Effectively utilizing but not overly relying on historical operation data under non-strictly repeated conditions. • The state of the closed-loop system converges in finite time and approaches zero along the iteration axis. • It is the first use of robust control to solve the limitations of iterative learning on high-speed train. • Numerical simulations with control groups validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a new method that uses the equivalent-input-disturbance (EID) approach to estimate the wind load of a seismically isolated building. To verify the control performance of wind-resistant design methods and devices, wind-load data are important for structural design of skyscrapers. However, wind loads cannot be measured directly and is usually estimated based on structural responses. Previous studies focused on a building model in which the damping coefficient is proportional to a linear combination of mass and stiffness (referred to as Rayleigh damping). These existing methods cannot be directly applied to a seismically isolated building because its damping is not Rayleigh damping. Moreover, a structural model usually contains uncertainties caused by modeling errors that cause the degradation of estimation performance. To solve these problems, this paper combines the EID approach with a robust control strategy to design a wind-load estimator for a seismically isolated building with uncertainties. First, the configuration of an EID-based estimation system is shown. Next, a robust wind-load estimator is presented. Then, simulations are carried out to demonstrate the validity of the presented method using wind-load data obtained from a wind-tunnel test. An 11-degree-of-freedom model of a seismically isolated building is used for numerical verification. The results show that, even if a model contains 20% uncertainties, the presented method accurately estimates the wind load of a seismically isolated building. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of formation control of multiple quadrotor vehicles, focusing on ensuring inter-vehicle collision avoidance in the presence of time-varying external disturbances. The problem is divided into two sub-problems: (i) the generation of collision-free trajectories; and (ii) trajectory tracking. Initially, by employing the Lyapunov-based backstepping technique, a set of collision-free trajectories is generated based on a potential function. Additionally, a trajectory tracking controller is developed for each individual quadrotor, ensuring accurate tracking of their respective trajectories. Furthermore, to attain robust tracking performance, nonlinear disturbance observers are developed and incorporated into the control inputs to compensate for time-varying external disturbances. Comprehensive simulation and experimental results are provided and analyzed to demonstrate the effectiveness and performance of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, the application of a robust nonlinear model predictive control (NMPC) framework to mammalian cell cultures is proposed, dealing with possible large kinetic parameter uncertainties. Industrial constraints formulated in view of good manufacturing practice and quality-by-design approach are also considered, namely the assurance that all state trajectories are contained within a corridor defined by lower and upper safety bounds. The latter are assimilated to the well-known tube-based paradigm which is used to formulate the corresponding robust NMPC problem. Both classical and tube-based NMPC performances are assessed in numerical simulations where specific key-species are regulated while dealing with an uncertain plant model. The capability of the tube-based method to reduce the impact of the parameter variations on the state trajectories and the violation of the constraints is highlighted, suggesting the transfer of the method on a real pharmaceutical process. • NMPC design for a 25-state data-driven mammalian cell culture model. • Qualitative model sensitivity analysis using confidence corridors. • Robust tube-based NMPC framework application to mammalian cell culture model. • Practical guidelines for experimental validation in industrial environment. [ABSTRACT FROM AUTHOR]

We propose a conservative relaxed control method to stabilize Takagi–Sugeno (TS) fuzzy systems. First, a conventional piecewise linear function approximation method is adopted to consider the membership functions' information in the closed-loop system's stability analysis. Subsequently, a model reconstruction method is proposed to reformulate linear approximation error terms in the TS fuzzy form, which can effectively introduce more information into stability conditions and achieve better robust control effects. Then, based on a fuzzy Lyapunov function approach, control design conditions are derived in terms of linear matrix inequalities, with less conservatism for related results in the literature. Finally, via a benchmark experiment, it is verified that the proposed method can show better robustness with fewer decision variables in robust convex conditions. [Display omitted] • A new model reconstruction method is proposed. • Introduce the linear approximation errors information into the stability analysis. • Relaxed linear matrix inequality robust/stability conditions are derived. [ABSTRACT FROM AUTHOR]

To improve the flowrate control accuracy of fuel control system at high supply pressure and large flowrate working condition, this paper proposes a novel dual closed-loop control for fuel metering unit (FMU) to address issues related to unknown velocity, matched/unmatched disturbances, and parameter uncertainties. Firstly, for a comprehensive assessment in flowrate feedback method, a global sensitive analysis is utilized to research the effect ratio of factors affecting the discharge flowrate. Then, a novel dual closed-loop control is proposed, in which the outer loop is flowrate loop, and a novel extended-state-observer (ESO)-based robust adaptive control without velocity sensor is used as the internal displacement loop control. Specifically, ESO can effectively estimate the unknown velocity and unmatched disturbance, and the parameter estimations can be updated by the adaptive law determined only by spool displacement and reference signal. The novel robust control can address matched/unmatched disturbances and model uncertainties effectively. Through Lyapunov method, it follows that the novel ESO-based robust adaptive control can achieve asymptotic tracking performance when occurring time-invariant disturbances and bounded tracking performance when occurring time-varying disturbances. Comparative experiments verify the superiority of the proposed control, which provides a new method and glimpse for the advanced control strategy of aeroengine. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper presents a novel dual defense security methodology for fault detectable reusable hardware intellectual property (IP) core against reverse engineering and piracy using multi-cut based architectural obfuscation and handprint biometric signature, useful for consumer electronics (CE) systems. The proposed approach, firstly generates a fault detectable design corresponding to target application framework. Subsequently, multiple cut insertion is executed resulting in multi-checkpointing which in turn camouflages the hardware design architecture during high level synthesis (HLS), thereby making it uninterpretable for an adversary. Next, handprint biometric signature of IP vendor is covertly embedded into the fault detectable IP design, which provides robust and seamless detective countermeasure against piracy. The proposed methodology achieves the following: a) strength of obfuscation ∼14 % b) robust detective control against piracy using handprint biometric signature at 0.0 % design cost overhead c) robust security in terms of digital proof against piracy as evident from lesser probability of coincidence and higher tamper tolerance. • A novel dual defense security methodology for fault detectable reusable hardware IP core. • Multi-cut based architectural obfuscation and handprint biometric signature-based approach. • Ensures protection of fault secured IPs against the threats of piracy and reverse engineering. • Presents almost negligible (zero) design cost solution. • Ensures robust security by yielding desirable probability of coincidence and tamper tolerance. [ABSTRACT FROM AUTHOR]

At present, positioning and tracking the joint angle of the PAM-actuated system is still a challenging issue, due to the inherent hysteresis, high nonlinearities, and time-varying characteristics. Considering the problem of uncertain parameters, external disturbance, and saturated input, a model-free optimal robust tracking control scheme is proposed for the PAMs system in this paper. Also, a nonquadratic cost function is introduced to address the saturation air pressure input. Moreover, an integral reinforcement learning(IRL) algorithm is developed to solve the H ∞ tracking Hamilton–Jacobi–Isaacs(HJI) equation. And the IRL algorithm is implemented on a critic–actor-disturbance neural network (NN) structure. The presented control scheme avoids the complete system dynamic and simultaneously compensates for the uncertain parameters, and external disturbance. Then, the effectiveness of the optimal robust tracking controller is verified via computational simulation and keeps the system stable. In addition, the tracking experiment is implemented on a hardware platform, indicating that the presented approach achieves satisfactory tracking performance. [ABSTRACT FROM AUTHOR]

Quadrotor vehicles are captivating devices that serve as valuable platforms for testing and developing nonlinear control algorithms due to the pronounced nonlinearities in their dynamics models. Additionally, these platforms are employed for a range of functions, spanning from entertainment to industrial applications. In engineering practice, it is imperative to develop a controller for a quadrotor to fulfill a desired task. Nevertheless, we must perform real-time experiments to assess the controller's performance effectively. Regardless, this task is challenging due to hardware and software limitations hindering the setup of the experimental platform and deployment of the controller. Therefore, it is essential to have a functional experimental platform for validating the performance of different control methodologies. With this objective in mind, this manuscript develops a step-by-step procedure for setting up a low-cost quadrotor testbed. To better understand the process, tutorial videos were generated to walk the reader through the steps required to implement a given controller successfully. Furthermore, an academic example employing a new Observer-based Proportional Integral Derivative controller is applied to demonstrate the effectiveness of the proposed methodology. The experimental results verify the novel method's efficiency despite the inherent hardware and software limitations or the influence effect of the inevitable endogenous and exogenous perturbations. • A novel methodology for implementing a robust controller in a low-cost quadrotor platform is presented. • A set of tutorial videos is included to walk the reader through the steps required to reproduce the proposed methodology. • A new robust PID controller is designed to control a quadrotor vehicle. • Experimental results demonstrate that the proposed methodology is effective regardless of endogenous and exogenous disturbances. [Display omitted] [ABSTRACT FROM AUTHOR]

• Development of an uncertainty and disturbance estimator-based speed controller for open-orbit pumping kite generator systems. • An adequate reference model to deal with the problem of periodical variation of rotational speed reference. • Increase the pumping kite system generated power by improving the speed control. Pumping kite generator systems (KGS) represent a novel and exciting concept for future wind energy harvesting at higher altitudes where the wind is more reliable, stable, and highly predictable. The configuration of such systems generally employs a flying tethered kite attached to a generator/motor and connected to the grid using a power electronic interface. This work aims to enhance control system efficiency and increase the generated power by improving the speed control of the generator. To this end, an uncertainty and disturbance estimator (UDE) based speed controller is developed to deal with the challenges presented by this new technology, particularly periodical variation of the rotational speed reference in maximum power point tracking. The practicality and effectiveness of the proposed UDE-based control method are demonstrated through simulation and experimental results. It is shown that the proposed control strategy offers improved tracking performance and robustness against model uncertainties and external disturbances. [ABSTRACT FROM AUTHOR]

Many current controllers are designed based on linear models, which may lead to poor performance when limiting its variables to a safe operation region, or even lead to instability when subject to abnormal operation conditions. Taking that as motivation, the contribution of this work is a new automated test-driven design procedure for robust and optimized current controllers applied to LCL-filtered grid-tied inverters. The design of the control gains towards an optimal solution is oriented by high-fidelity simulations of the converter, covering tests under normal and abnormal operating conditions, such as: reference tracking, grid impedance variations, voltage sags, harmonic compliance, and current and voltage limitations. A particle swarm optimization algorithm is used to evolve the control gains in a computationally efficient way, and linear matrix inequalities are employed to accelerate the optimization process and to provide a theoretical certificate of robust stability. Results are obtained in both controller hardware-in-the-loop testbed and an experimental 5.4 kW prototype, illustrating cases where the proposed design ensures superior performance under normal and abnormal grid conditions when compared with three other current control designs from the literature. • An automated way to provide robust and optimized controllers using PSO algorithm. • Less conservative LMIs for checking the closed-loop robust stability. • High-fidelity simulations of the GTI using Controller Hardware-in-the-Loop. • Experimental results with superior performance and complying with IEEE 1547 Std. [ABSTRACT FROM AUTHOR]

The uncertainty and disturbance estimator (UDE) technique has been proven to be simple but effective in reconstructing unknown disturbances. However, classic UDE with a constant gain cannot deal with more complicated issues, such as the trade-off between steady-state and transient performance of the estimator, simultaneous attenuation of input disturbances and measurement noises, etc. Motivated by this observation, the design of UDE technique is extended to the time-varying situation and an implementable form of time-varying UDE (TV-UDE) is proposed. In particular, velocity measurements are assumed to be unavailable in the design. A time-varying ordinary differential equation is constructed to build the estimation relationship. A formula for the integration by parts is thereafter introduced to derive an implementable form of the TV-UDE. The classic UDE is shown to be a special case of the TV-UDE. The UDE is combined with a nominal dynamic output-feedback controller to yield a robust control solution to a class of uncertain second-order attitude control systems without velocity measurements. The design of a TV-UDE is discussed with a low initial gain and a higher final gain to avoid transient performance issues. Finally, numerical simulations and physical experiments are carried out on a 2-DOF AERO attitude helicopter platform to demonstrate the control performance of the solution. It is illustrated that the proposed design can effectively reduce the steady-state errors in both the trajectory tracking and target pointing tasks. Meanwhile, the input saturation and peaking phenomena associated with a high-gain estimation are avoided by applying the TV-UDE. • A time-varying UDE (TV-UDE) is developed by building a linear parameter-varying (LPV) estimation relationship. • TV-UDE is more general and provide more flexible performance than the time-invariant UDE. • Design specifications and examples are provided to generate a smooth time-varying parameter. • The transient performance issues associated with a high-gain UDE are effectively avoided by applying a TV-UDE. • Simulation and experimental verifications for the trajectory tracking and pointing control tasks are carried out. [ABSTRACT FROM AUTHOR]

Model invalidation is the process of testing assumptions of a dynamical model by comparing simulated responses with experimental data, considering any discrepancies as evidence that the model may be invalid. In this study, a model invalidation methodology is presented, to obtain robust control oriented models for wave energy converters (WECs). In particular, this methodology can deal separately with dynamical uncertainty and external noise in experimental data sets. To this end, considering linear system theory, this study proposes a methodology for building input–output data sets for WEC systems, via a two-stage approach. Model invalidation results are analysed statistically, and the practical implications of considering dynamical uncertainty in WEC system models are discussed in terms of control performance, specifically absorbed energy. As indicated by the analysis and results presented in this study, failure to include dynamic uncertainty in the analysis can lead to performance overestimation. The importance of a good dynamical description for accurate estimation of experimental control performance is highlighted. Finally, this study emphasises the need for closed-loop controllers for WEC systems that can simultaneously maximise energy and guarantee robust stability, an area currently lacking within the WEC literature. [ABSTRACT FROM AUTHOR]

• Robust output-feedback absolute stabilizing control approach to fluid resuscitation. • Observer-based LQR design for set point tracking and disturbance rejection. • LMI-based observer design for absolute stability against physiological uncertainties. This paper presents a robust closed-loop control approach to fluid resuscitation in patients with hemorrhagic blood loss. A unique strength of the proposed approach is its robustness against uncertain and time-varying patient physiology and therapeutic effectiveness. First, we adopted an observer-based control architecture that can fulfill set point tracking and disturbance rejection objectives. Second, we determined the control gains to achieve adequate transient response performance using the linear quadratic regulator design. Third, we determined the observer gains as a solution to a set of linear matrix inequalities so that the overall closed-loop fluid resuscitation control system is (i) robust against the variability in patient physiology and (ii) absolutely stable against unknown therapeutic effectiveness. We demonstrated the initial proof-of-concept of the proposed approach by conducting rigorous in silico testing using a large number of physiologically plausible virtual patients, while ascertaining the absolute stability via the circle criterion analysis. The results suggested that the proposed approach to closed-loop control of fluid resuscitation is a promising option to advance automation of fluid resuscitation armed with stability against a large variability in patient physiology and therapeutic effectiveness as well as adequate performance in set point tracking and disturbance rejection. [ABSTRACT FROM AUTHOR]

The strong nonlinear dynamics of tail-sitter vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) have consistently constrained their applications. Particularly, the widely-varying flight speed and attitude during the transition flights pose substantial challenges for the controller design. In order to gain a clear understanding of the nonlinear transition flights, the long- and short- period dynamic characteristics of tail-sitter UAVs are investigated for the first time. The results reveal that the short-period mode exhibits either sluggish response or excessive overshoot in different transition stages, primarily due to the widely-varying damping ratio and natural angular frequency. Even worse, the long-period mode can diverge due to positive poles. To address these issues, a systematic controller is proposed. First, the transition trajectory is optimized and the optimal control inputs are employed in a feedforward manner to trim the nominal forces and moments. On this basis, a discrete-time linear quadratic regulation (LQR) controller with a predefined decay rate is developed to position the desired poles, and a novel angular acceleration estimation method is introduced to compensate for unmodeled dynamics. Simulations under different uncertainties indicate that the proposed control method performs better in both transition trajectory tracking and uncertainty suppression, compared to the PID and incremental nonlinear dynamic inversion (INDI) controllers. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive control problem for the strict-feedback nonlinear time-varying system (NTVS) with unknown control coefficients and model uncertainties. To address the problem that the neural network (NN) can only achieve local approximation of unknown nonlinear functions, a novel NN control scheme is proposed. The scheme consists of a NN controller that operates inside the approximation domain and a robust controller that operates outside the approximation domain, and a novel switching function is designed to enable the two controllers to switch smoothly in the vicinity of the approximation domain to ensure that all signals of the closed-loop system are globally uniformly ultimately bounded. To address the problem that unknown time-varying control coefficients are difficult to handle, an adaptive fault-tolerant control scheme is proposed in combination with the congelation of variables method. The scheme employs classical adaptive control to adapt to the variation of unknown control coefficients and robust control to eliminate the time-varying disturbance terms, and introduces a positive integrable time-varying function to achieve asymptotic tracking of an arbitrary reference signal. The combination of these two schemes constitutes a global adaptive neural network control (GANNC) method for the NTVS with unknown control coefficients and model uncertainties. Finally, an adaptive attitude fault-tolerant controller for launch vehicles is designed by using the GANNC method, which can realize the accurate tracking of the attitude control system to the reference command under normal status, strong disturbances and actuator failures, and comparative experiments prove the effectiveness and superiority of the controller. [ABSTRACT FROM AUTHOR]

Traditionally, the problem of congestion arising in power system networks due to various contingencies and varying renewable generation is addressed by optimal power flow-based methods, once in every 5–30 min. As these techniques are performed for a block period of time using forecasted data, they are not effective in real-time congestion mitigation through corrective actions. This paper proposes a controller-based approach for real-time congestion management through line power flow regulation. We consider a robust H ∞ power flow tracking control strategy to regulate the line power flows to the specified limit, while attenuating the effect of disturbances on the system. Using synchrophasor measurements from the lines, the proposed controller can detect line overloading and automatically re-dispatch the selected generators and battery energy storage systems to maintain power flow within the desired limit. The robust line power flow tracking problem with disturbance rejection is formulated as a dynamic game between the controller and disturbance entities, and robust static output feedback H ∞ controller gains are obtained via an iterative algorithm. The efficacy of the proposed controller in regulating line power flow and mitigating congestion is verified by extensive simulations on a 16-machine 68-bus test system and Kundur's 4-machine 2-area test system. It is shown that the designed controller can regulate line power flows within 40–60 s, which is fast enough to avoid overheating of transmission lines. Furthermore, the proposed strategy is simple from the perspective of design and implementation, and outperforms the existing congestion management techniques. • Line power flows can be regulated to specified operating limits with the controller. • Congestion in the network can be cleared automatically in short period of time. • Effect of disturbances are attenuated to a prescribed level to provide robustness. • Requires only power flow measurements from lines to implement robust control action. [ABSTRACT FROM AUTHOR]

• The UIPC is used as the main component for interconnecting of AC and DC sub-grids. • A robust nonlinear model reference adaptive control strategy is offered for controlling the UIPC. • A novel configuration for the DC link power converter is suggested. • A harmonic-based modeling method is used to model the UIPC's power converter. This paper focuses on the control of unified interphase power controller (UIPC) in isolated hybrid microgrids (HmGs), where the UIPC is used as the main component for interconnecting of AC and DC sub-grids. The investigated HmG encompasses two AC sub-grids and one purely storage DC sub-grid (PSDC) and multiple adjustable loads. A robust nonlinear model reference adaptive control (NMRC) strategy is offered for controlling the UIPC, keeping an acceptable two-way power exchange control with uncomplicated topology. Further, a novel configuration for the DC link power converter is suggested using the dual-active-bridge topology. Also, a harmonic-based modeling method is used to model the UIPC's power converter. The proposed configuration provides bidirectional power flow, increased power density, straightforward adaptation of zero-voltage switching, and direct access to cascading and parallelism to increase the ratings and reliability of UIPC. The simulation results illustrate the feasibility of the suggested topology for isolated HmGs. [ABSTRACT FROM AUTHOR]