Search

In the current power landscape, renewable energy sources (RESs) have assumed a crucial role in satisfying consumer demand. However, as the deployment of renewables increases, certain challenges arise, including issues with system frequency stability, inertia, and damping reduction. To address these concerns, an innovative approach is suggested in this study. The proposed strategy aims to maintain frequency stability in a diverse-source power system that encompasses two interconnected regions incorporating RESs. The proposed strategy comprises a new multi-degree of freedom FOTID controller known as the MDOF-TI λ D μ N controller in the secondary control loop (SCL) and optimally controlled fuel cells (OFCL) to enhance the system's stability under the effect of renewable energy (RESs) fluctuations. In this context, the gains of the considered strategy (optimal MDOF-TI λ D μ N in addition to OFCL) have been picked out by using an innovative optimization approach known as the Capuchin search algorithm (CapSA). The statistical tests are used to examine the efficacy of the considered CapSA compared to those of other optimization strategies utilized in previous studies. Furthermore, the performance of the proposed controller in the SCL is verified by contrasting its performance with that of another suggested controller known as MDOF-PIDN as well as other controllers such as PD-IT, PD μ N-I λ T, 2DOF-TI λ D μ N, 3DOF-PIDN, 3DOF-TIDN, and 3DOF-PI λ D μ N. Additionally, grid nonlinearities, including Boiler Dynamics, Generation Rate Constraint, Governor Dead Band, and random communication time delay (CTD), are considered. Moreover, the proposed strategy's performance is verified in the face of system constraints and nonlinearities. Different scenarios are implemented, and the simulation outcomes emphasize the superior performance of the suggested strategy. Therefore, the suggested strategy provides consistent power system adoption wherever it is implemented. • Proposing a coordination strategy based on applying LFC and controlled optimal fuel cell (OFCL) to strengthen the performance of a hybrid two-area system under the impact of the significant penetration of RESs. • Incorporating the proposed MDOF − T I λ D μ N controller into the secondary control loop of the LFC. • Validation of the efficacy of the suggested MDOF − T I λ D μ N controller in comparison with that of another suggested MDOF − PIDN controller in addition to that of other controllers in previous literature. • Evaluating the system performance considering conventional FCL (CFCL), OFCL, and OFCL controlled by the PI controller to indicate the best system performance. • The coefficients of the suggested MDOF − T I λ D μ N as well as OFCL and PI controller coefficients are established using a novel optimization technique called CapSA. [ABSTRACT FROM AUTHOR]

This article presents the structured H ∞ design and validation of two types of flight controller architectures: a passive fault-tolerant controller for the longitudinal motion and an active observer-based fault-tolerant controller for the lateral-directional motion. In the first, the controller follows the conventional Stability/Control Augmentation System (SCAS) structure, and its gains are obtained in continuous-time with the hinfstruct command by considering a set of elevator Loss-Of-Efficiency (LOE) faults. For the second, the conventional Luenberger observer-based controller structure is used, and the design aims to monitor the health of the aileron and rudder actuators in addition to provide active tolerance against LOE faults. Two different discrete-time designs are obtained for the latter, one focused on control performance optimization (using also the hinfstruct command), and the other on simultaneous control and observer performance optimization (using the systune command and under a slightly relaxed control performance constraint). For the two types of architectures, unmodeled dynamics are represented by uncertain bounded time delays modeled as pure delays or first-order Padé approximations. The resulting controllers are implemented on-board JAXA's research airplane MuPAL- α , and not only is their practicality demonstrated but also control performance is validated via Aircraft-In-the-Loop (AIL) testing under gust-free and realistic gusty conditions. This demonstration is at a Technological Readiness Level (TRL) of 7–8, resulting in a high-level of confidence in the validity of the proposed flight control structures. • Controller practical validation using JAXA's research airplane MuPAL- α. • Aircraft-in-the-loop test campaign with the full onboard FBW system and a pilot. • A passive fault-tolerant SCAS controller for the longitudinal motion. • An active fault-tolerant observer-based controller for the lat.-dir. motion. • Controllers designed using structured H ∞ optimization tools. • Demonstration of the validity of using Padé-approximated delays in design process. [ABSTRACT FROM AUTHOR]

This article studies the generalized efficient robust predictive control (GERPC) for the nonlinear systems denoted by interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy model over networks with consideration of packet dropout, bounded disturbance and adaptive event-triggered scheme (AETS). First, the packet dropout is described by a Bernoulli process, and an AETS with thresholds that can be self-regulated according to different situations is employed to decide whether to release data into the communication network, so as to reduce the data transmission frequency and the network burden; Second, taking into consideration of packet dropout and AETS, a unified IT2 T–S fuzzy control model based on GERPC is established, and the stability of closed-loop system with bounded disturbance is guaranteed by quadratic boundedness (QB) technique; Third, generalized efficient robust predictive controller is designed to obtain a larger initial feasible region, smaller online computation and acceptable control performance. The unconstrained control law is designed offline, and a new variable will be optimized online to achieve good control performance, which is introduced to adjust the control quantity. Then, the convex LMI reformulation method is utilized to handle non-convex terms in optimization problem, and the effectiveness of the GERPC algorithm is proved by the matrix partition technique. At last, the effectiveness of the designed controller based on the GERPC strategy is verified by a given simulation experiment. • A synthesis method of generalized efficient robust predictive control (GERPC) is proposed for IT-2 fuzzy NCSs with data dropout, adaptive event-triggered scheme (AETS) and bounded disturbance, which can obtain good control performance, larger initial feasible region and smaller online computation. • Design a fixed feedback gain offline, and the control perturbation introduced is calculated online. • Introduce an adaptive event-triggered scheme with dynamically adjustable threshold to avoid network communication congestion. • The stability of closed-loop system with bounded disturbance is guaranteed by quadratic boundedness (QB) technique;. [ABSTRACT FROM AUTHOR]

• A novel I&I adaptive scheme is presented. • Immersion and invariance based adaptive terminal sliding mode control is proposed. • Immersion and invariance based adaptive terminal sliding mode control with saturation compensation is presented. This work investigates attitude control of spacecraft with uncertainties and input saturation. An immersion and invariance based adaptive robust control (IIARC) is proposed to improve tracking performance of spacecraft. The proposed approach consists of an immersion and invariance (I&I) estimator, a robust adaptive law, and a terminal sliding mode control (TSMC). The continuous fast TSMC is designed to stabilize the system and obtain fast convergence rate. To estimate the disturbances and uncertainties, I&I methodology is introduced into the proposed approach. The robust adaptive law is applied such that the prior information of disturbances is not required. IIARC with saturation compensation (IIARCSC) is presented to reduce the effect of input saturation on the attitude tracking of spacecraft. It is proved that the developed approach could guarantee the finite-time convergence (FTC). Extensive comparative simulations demonstrate the effectiveness of the presented scheme. [ABSTRACT FROM AUTHOR]

This note shows an elegant relationship between the quadratic optimal control and robust stabilization for linear time-invariant (LTI) systems, where the former control can robustly stabilize the latter system, provided that the matched uncertainty is bounded. Through reviewing the relevant literature, some common mistakes in regard to this relationship are found. The correct results are obtained and proved in both frequency and time domains. The results are applicable to both single- and multi-input cases. They are significant as the simple LQR design for the nominal system can be utilized to directly solve—with no further effort—the complex robust stabilization problem for a class of linear uncertain systems. [ABSTRACT FROM AUTHOR]

This paper proposes a reasonable, practical, and novel method for designing a robust controller for two degrees of freedom nano-electromechanical scanners based on the combination of quantitative feedback theory (QFT) and the Taguchi method. Although the main primary of this paper devotes to rotational control, an investigation of two-degree freedom (rotational/bending) is studied by employing the Taguchi method. A moveable main plate suspended by two nano-beams over a fixed substrate electrode is used to represent the scanner. The nanoscanner is thoroughly analyzed using the most comprehensive modeling design, considering the elastic, electrical, Casimir force and moment, and squeezes film damping. The nanoscanner's governing equations are initially derived by considering both rotation and deformation. In addition, because the size dependency of materials is significant in ultra-small structures, we developed the constitutive equations within the context of the modified couple stress theory to integrate the effect of scale dependency. Next, system uncertainties have been wholly addressed to achieve an accurate model. As a result, using robust control methods such as quantitative feedback theory to precisely control nano-scanners in the presence of uncertainties is inevitable. The quantitative feedback approach transforms the nonlinear plant into a family of linear uncertain plants in the first part. This is accomplished using a fixed-point theorem, after which appropriate disturbance rejection boundaries are discovered. In this problem, quantitative feedback controllers and checking the system's stability at any frequency and time are intended to solve the tracking problem and the disturbance rejection issue. Due to the uncertainty associated with the system model's complexity and accuracy, we employ a QFT controller to control the system. Moreover, because this system has only one input and two outputs, and changing the controller's gain is complicated, the Taguchi method has been employed to enhance better performance. Nonlinear simulations of the tracking issue are carried out, and the results demonstrate the effectiveness of the developed controllers and prefilters. The findings show that using the suggested approach effectively overcomes the challenges to robust control of a nonlinear rotational nanoscanner, and also the system achieves the best angle and deflection adjustment accuracy. [Display omitted] • Mathematical modeling of a Torsion/Bending of Rotational Nanoscanner and comparing control method with experimental data. • If a user inputs erroneous voltage to reach system's output, the control approach automatically corrects the system input. • Designing SIMO (Single Input Multi Output) robust controller using Taguchi Method in the presence of uncertainties. • Modifying controllers in a system using the Taguchi method ensures stability and output without adverse effects. • Nonlinear simulations of the tracking issue are carried out, and the results shoe the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper aims to tackle the three-dimensional trajectory tracking problems of small fixed-wing unmanned aerial vehicles subject to nonlinearities, uncertainties and wind disturbances via adaptive techniques. The control objective is to efficiently control the thrust and the deflections of control surfaces to ensure the unmanned aerial vehicle arrives at a specified location within a given time frame. However, achieving this goal for small fixed-wing unmanned aerial vehicles can be challenging because the precise dynamic model and several parameters are not accessible, making most existing control strategies unworkable. Motivated by these facts, based on feedback linearization techniques, we derive linear models with equivalent disturbances to describe the translational dynamics without requiring precise aerodynamic force model information. To deal with the dilemmas where the norm bounds of equivalent disturbances depend on control inputs, system states, and unknown disturbances, a novel robust adaptive control strategy is designed for position control. Based on the assumption of two-time separation, the control scheme incorporates two parts, namely, a position controller containing the horizontal-plane and altitude parts and a robust filter-based attitude regulator. Also, to prevent chattering issues, we design a practical and robust adaptive position controller under which the tracking error is ultimately bounded The overall closed-loop stability is theoretically investigated based on the Lyapunov arguments. Hardware-in-loop simulation experiments are performed to testify our developed control scheme. • A novel robust adaptive control strategy is proposed to deal with the uncertainties related to not only system states but also to control inputs. • A cascade control strategy is employed to solve the trajectory tracking problems of fixed-wing UAVs. • A robust adaptive three-dimensional trajectory tracking controller is designed. • Hardware-in-Loop simulations are conducted. [ABSTRACT FROM AUTHOR]

This paper addresses the regulation of water quality influencers such as Organic Nitrogen, Ammonia Nitrogen, Nitrate Nitrogen, Dissolved Organic Carbon and Dissolved Oxygen for a sub-surface flow horizontal wetland in a controlled environment. The plant uncertainty is quantified by using measurement data. The robust control of the process is achieved using a decentralized quantitative feedback theory based control strategy. The control structure also employs a feed-forward mechanism to minimize loop interactions and feedback control to ensure stability, tracking and disturbance rejection. The numerical simulations and hardware-in-loop implementation show that the performance specifications are robustly met. • The control of water quality deciding variables by using a constructed wetland • The parametric uncertainty is quantified using measurement data collected • A decentralized quantitative feedback theory based robust controller is adopted • Each loop is equipped with feed-forward controllers to minimize coupling effects • Performance is verified by numerical simulation and hardware-in-Loop validation [ABSTRACT FROM AUTHOR]

Microbial electrolysis cell (MEC) is a fundamental type of bio-electrochemical system. MEC is a novel and emerging renewable energy technology that is based on biomass. The behavior of the MEC system is highly nonlinear due to the complexity of its dynamics. For the desired optimal production of hydrogen, feedback control of MEC processes is necessary. Due to the novelty of MEC, limited research is available on its control. Studies on linear model-based robust control of MEC processes are missing. In this article, we develop a nonlinear dynamic model for MEC, linearize this model, and calculate a linear time-invariant transfer function. Based on this linearization, a fixed-structure, optimal and robust controller is proposed to achieve a fast-settling time exhibiting no overshoot and having zero steady-state error. The robustness of the developed controller is evaluated for parameter uncertainty, measurement noise, and disturbance rejection. Batch biomass processes are fed only at the start of each process cycle. The output does not follow the desired response when the substrate or biomass is consumed. Then, the error accumulates, and it causes the control effort to increase unboundedly. The existing literature on control of fed-batch MEC processes does not consider this integral windup phenomenon. In this article, we also develop an anti-windup control strategy to eliminate the integral windup error and to avoid any possible instability or destruction. The overall conclusion of our study is that the developed robust controller achieves a faster and more robust response than the existing controllers. We provide an anti-integral windup solution to eliminate windup errors in feedback control of fed-batch MEC processes. [Display omitted] • We design an optimal robust controller for a dynamic model of microbial electrolysis cells. • We linearize the non-linear model and calculate a time-invariant transfer function. • We evaluate uncertainty, noise, and disturbance of the controller. • We develop a strategy to eliminate windup error and avoid instability. • Our controller achieves a more fast and robust response than the existing controllers. [ABSTRACT FROM AUTHOR]

This paper addresses the control problems of distributed attitude coordination for multiple flexible spacecraft based on the feedback of inertial vector measurements. It is assumed that the angular velocity information is not available, and the modal variable cannot be measured. The orientation error model with vector measurements is introduced. And then the distributed attitude synchronization control scheme is presented without actuator faults or disturbances. It is guaranteed that the desired equilibrium is locally asymptotically stable and the undesired equilibrium is unstable. Meanwhile, the domain of attraction is given by inequality constraints. In the case that there exist actuator faults and external disturbances, a robust fault-tolerant control strategy is developed to realize attitude synchronization. The hyperbolic tangent function in place of sign function is adopted to eliminate external disturbances. Meanwhile, the adaptive laws are proposed to estimate actuator faults. The salient feature of these control approaches is that no modal variable observers are utilized and the control schemes are robust to the inertia uncertainty of spacecraft. Finally, numerical simulations are implemented to manifest the validity of the presented control schemes. [ABSTRACT FROM AUTHOR]

The fuel cell airpath multivariable control problem of optimally coordinating the electric compressor motor and the back-pressure valve to achieve efficient and safe conditions, for both steady state and transient operation, has not been completely addressed in the literature. This paper proposes a nonlinear model predictive control strategy, implemented via the Garrett Motion proprietary NMPC toolbox, to regulate the oxygen stoichiometry and the cathode pressure of an automotive fuel cell airpath system, while avoiding compressor surge and air starvation. The controller set-points are optimized, using the nonlinear model, to achieve the maximum system power as a function of the operating stack condition. The effectiveness and robustness of the proposed control strategy have been validated by means of a simulated World harmonized Light-duty vehicles Test Cycle (WLTC), under both state feedback and model parameters uncertainties. [Display omitted] • MPC ensures compressor surge and oxygen starvation avoidance under severe dynamic scenarios. • Controller robustness assessed via a WLTC regulatory cycle under noisy and uncertain conditions. • Fuel efficiency is not significantly sensitive with respect to different controller calibrations. [ABSTRACT FROM AUTHOR]

It is well known that induction motors consume active and reactive energy from the utility grid to operate; additionally, when a power converter drives the motor, a high content of current harmonics is produced, and both circumstances decrease the utility grid power factor, which later requires to be improved. To this end, this paper presents a novel complete solution through a robust control system employed in a back-to-back topology power converter to deliver, instead of consuming, regulated reactive power toward the main grid, which comes from a capacitor bank in a DC-bus. This salient feature of delivering reactive power, and simultaneously, regulating the speed for an induction motor, becomes one of the contributions of this work to enhance the power factor. The robust converter controller is synthesized in a cascade form, by applying the linearization block control and state-feedback techniques. These techniques are combined with the super-twisting strategy for canceling the nonlinearities and the effect of the external disturbances. The complete system consists of a back-to-back converter, an LCL filter coupled to the main grid for mitigating the current harmonic content, and an induction motor under variable load conditions. Experimental tests expose the performance and robustness of the proposed controller, where a robust control for the reactive power acts under sudden changes in the active power produced through abrupt variations in the motor load. [Display omitted] • To deliver regulated reactive power for power factor enhancement in the utility grid. • Simultaneously, regulating the induction motor speed through a robust control system. • To present experimental results for validating the proposed cascade control system. [ABSTRACT FROM AUTHOR]

In this paper, an iterative neural network adaptive robust control (INNARC) strategy is proposed for the maglev planar motor (MLPM) to achieve good tracking performance and uncertainty compensation. The INNARC scheme consists of adaptive robust control (ARC) term and iterative neural network (INN) compensator in a parallel structure. The ARC term founded on the system model realizes the parametric adaptation and promises the closed-loop stability. The INN compensator based on the radial basis function (RBF) neural network is employed to handle the uncertainties resulted from the unmodeled non-linear dynamics in the MLPM. Additionally, the iterative learning update laws are introduced to tune the network parameters and weights of the INN compensator simultaneously, so the approximation accuracy is improved along the system repetition. The stability of the INNARC method is proved via the Lyapunov theory, and the experiments are conducted on an home-made MLPM. The results consistently demonstrate that the INNARC strategy possesses the satisfactory tracking performance and uncertainty compensation, and the proposed INNARC is an effective and systematic intelligent control method for MLPM. • A iterative neural network scheme with network parameters and weights tuned via iterative learning is proposed to improve the uncertainty approximation capability. • The proposed scheme integrating of model-based and data-based method improves the tracking performance of the maglev planar motor. • The stability of maglev planar motor regulated by the proposed iterative neural network adaptive robust control method is proved via Lyapunov theory. [ABSTRACT FROM AUTHOR]

• We elicit college students' willingness to pay (WTP) for an early versus late job shift in an unsafe neighborhood. • Subjects with higher personal safety concerns and women forego more earnings for the earlier shift. • Results are robust to control for a wide range of confounders. • Higher safety standards around jobs may reduce distortions in the labor market. We elicit college students' willingness to pay (WTP) for an early versus late job shift in an unsafe neighborhood. We find that concerns about late shift safety and gender are determinants of differences in WTP: Subjects with higher personal safety concerns (i.e., feeling unsafe on the way to or around work) and women forego more earnings to secure the early shift. Yet, we find no differences in WTP when the job is remote. Controlling for a wide range of confounders, such as risk preferences, morning preferences, time use, demographic characteristics, victimization, and information about crime, does not meaningfully affect the effect of safety concerns. Victimization and time use mediate the gender gap. Exploiting past administrative data, we find that subjects with higher WTP for the safer on-site shift are less likely to enroll in evening classes and leave campus earlier during the term, providing evidence for the external validity of our study. [ABSTRACT FROM AUTHOR]

This study aims to develop a robust control for the quadrotor slung-load system that efficiently follows a reference trajectory. A fractional-order robust sliding mode control has been chosen to control the quadrotor's altitude, position, and attitude. An anti-swing controller was also installed to limit the swing angle of the suspended load. It was created via delayed feedback, in which the quadrotor's position reference trajectory is changed by the difference of the load angles within a specific delayed value. Designing an adaptive FOSMC would control the system when the system uncertainties do not know the bounds. Moreover, the control parameters and the anti-swing controller for the FOSMC can be obtained using some optimization techniques to increase the controllers' accuracy. [ABSTRACT FROM AUTHOR]

Direct Torque Control (DTC) of Induction Motors (IMs) is popular in motor drive applications because of its robust and simple control structure. The IM winding can be controlled on both sides using dual inverter technique which more effective for Electric Vehicle (EV) with a greater number of voltage vectors. However, the battery performance of the dual inverter will deteriorate unevenly on both sides, resulting in fluctuating voltages for the EV system. This will lead to the generation of distorted stator currents and a significant droop in the stator flux, which in turn can increase the total harmonic distortion (THD) in the system. Additionally, the performance of torque may not be able to regulate effectively. This paper examines the effect of unstable voltage on voltage vector mapping performance with tilted angles and proposes new sector definitions based on voltage ratio conditions. Moreover, the proposed sector for each predefined voltage ratio is tested under three-speed conditions. The proposed technique effectiveness is validated through hardware experiments using a dSPACE 1104 controller and retuning the stator current for proper waveform. This approach improves the stator current waveform, improves stator flux droop, enhances torque regulation and minimizes the THD in the DTC system. [ABSTRACT FROM AUTHOR]

• The TM capture is first reformulated to nonlinear quadratic optimal control. • The high-efficiency NMPC with robustness and optimality is proposed for TM capture. • The mixed state and control constraints are integrated into the robust optimal control. • The proposed method guarantees the computational efficiency. This paper presents a nonlinear high-efficiency model predictive control (NHMPC) with constraints designed for the test mass (TM) capture phase of the drag-free satellite about gravitational wave observatory. To avoid collisions between test mass and satellite cavity, TM capture is the essential technology for drag-free satellite. Test mass is located inside an electrostatic suspension and locked by a clamp mechanism initially. The test masses are released with high initial offsets and velocities when the mechanism is retracted. The purpose of this phase is to guarantee the TM to be positioned at the cage center and attitude aligned with the local cage frame. Due to the low actuation authority of electrostatic suspension along with critical initial offsets and velocities, it is a challenging task to design the attitude and translation control schemes with simultaneous consideration of system performance and energy consumption. For the problem given above, the TM capture can be reformulated into a nonlinear quadratic optimal control problem with the state and input constraints. A nonlinear model predictive control (NMPC) structure is also designed to handle the noises by forming a closed loop. This control framework can realize optimality and robustness in a compromise. To improve the speed of solving high-dimensional nonlinear optimal control online for MPC, the indirect Chebyshev pseudospectral method with constraints is employed. The convergence and stability of the control loop are analyzed. Simulation examples show that the feasibility and performance of the designed control loop are verified compared with the existing methods and computation times in the millisecond range can be achieved. [ABSTRACT FROM AUTHOR]

Synchronization of memristive neural networks (MNNs) by using network control scheme has been widely and deeply studied. However, these researches are usually restricted to traditional continuous-time control methods for synchronization of the first-order MNNs. In this paper, we study the robust exponential synchronization of inertial memristive neural networks (IMNNs) with time-varying delays and parameter disturbance via event-triggered control (ETC) scheme. First, the delayed IMNNs with parameter disturbance are changed into first-order MNNs with parameter disturbance by constructing proper variable substitutions. Next, a kind of state feedback controller is designed to the response IMNN with parameter disturbance. Based on feedback controller, some ETC methods are provided to largely decrease the update times of controller. Then, some sufficient conditions are provided to realize robust exponential synchronization of delayed IMNNs with parameter disturbance via ETC scheme. Moreover, the Zeno behavior will not happen in all ETC conditions shown in this paper. Finally, numerical simulations are given to verify the advantages of the obtained results such as anti-interference performance and good reliability. [ABSTRACT FROM AUTHOR]

In this paper, the composite robust control problem of uncertain nonlinear systems with unmatched disturbances is investigated. In order to improve the robust control performance, the integral sliding mode control method is considered together with H ∞ control for nonlinear systems. By designing a disturbance observer with a new structure, the estimations of disturbances can be obtained with small errors, which are used to construct sliding mode control policy and avoid high gains. On the basis of ensuring the accessibility of specified sliding surface, the guaranteed cost control problem of nonlinear sliding mode dynamics is considered. To overcome the difficulty of robust control design caused by nonlinear characteristics, a modified policy iteration method based on sum of squares is proposed to solve the H ∞ control policy of the nonlinear sliding mode dynamics. Finally, the effectiveness of the proposed robust control method is verified by simulation tests. • A new disturbance observer is proposed to achieve faster and more accurate estimation. • The use of disturbance estimation in sliding mode control can reduce the impact of chattering. • A novel robust control design method is proposed based on policy iteration and SOS method. [ABSTRACT FROM AUTHOR]

This work addresses the stabilization of variable fractional-order (VFO) neutral-type systems with structure perturbations and unknown disturbance signals using the feedback control approach. The goal is to design disturbance-observer-based delayed state- and output-feedback controllers to achieve robust stability of such VFO systems. The proposed controller consists of two parts, namely a primary controller based on the linear feedback technique, and an auxiliary controller based on the disturbance observer. A disturbance observer is developed to estimate the disturbance signal, which is generated by an exogenous system. Based on matrix inequalities, order-dependent and delay-dependent conditions are formulated via FO Lyapunov theory that guarantee the robust stability of the closed-loop system. Simulations verify the main results. • Addresses stabilization of variable fractional-order neutral-type systems. • Considers structure perturbations and unknown disturbance signals. • Adopts disturbance-observer-based delayed state- and output-feedback controllers. • Derives stability conditions using matrix inequalities and FO Lyapunov theory. [ABSTRACT FROM AUTHOR]

In this paper, sensorless robust speed control with nonlinear lumped mismatched disturbance observers for a permanent magnet type axial gap bearingless motor (AGBM) is designed. Multistage anti-windup-based dynamic surface control combined with integral backstepping control is proposed to control the motor's axial displacement and rotor speed. The approach is against parameter uncertainties and external disturbances, improving steady-state accuracy, eliminating the derivative explosion phenomenon, no chattering problem, and reducing the magnitude of the control system when current saturation occurs. In addition, a novel nonlinear lumped mismatched disturbance observer is proposed to improve the approach under unmodeled dynamics and external disturbances. To obtain high-accuracy tracking control, the control system includes the robust controller combined with the disturbance observers and anticipatory activation of anti-windup (AW) compensation, which means the controller is more complex. Then, to design a sensorless robust speed control for the motor, the rotor position and speed observer require higher accuracy. High-gain back-EMF observer combined with an improved phase-locked loop is proposed to estimate rotor angular position and speed even when the motor speed is reversed. Overall stability of closed-loop system control, including a sensorless speed control approach for motors using back-EMF estimation combined with saturation of the currents and lumped disturbance observers, is mathematically proven. Finally, the simulation results under measurement noise show that the proposed control system are obtained the effectiveness, feasibility, and robustness. [Display omitted] [ABSTRACT FROM AUTHOR]

• The preview tracking controller for interconnected continuous-time systems with parameter uncertainties is designed. • The previewable information is integrated to the states of error system, which is a new framework of preview control. • The performance index function and performance signal are constructed to research the influence of disturbance. • The proposed preview controller can make the tracking quality improves as the preview length increases. This paper designs a decentralized robust preview tracking controller for interconnected continuous-time systems with parametric uncertainties. The uncertain error system, which is composed of the derivative equation of the states, the tracking error dynamics, and the available future reference dynamics, is constructed. Then, a cost function is introduced to evaluate the tracking quality. Meanwhile, a performance signal is defined to discuss the influence of disturbances on the tracking performance. Based on the above operations, the considered tracking control is converted to the H-infinity control of the uncertain error system. The sufficient conditions which guarantee that the closed-loop system is asymptotically stable with H-infinity disturbance attenuation are derived. Further, the decentralized preview controller is obtained with the help of the linear matrix inequality (LMI). The result is applied to double-parallel inverted pendulum system and 3-interconnected machine power system. Numerical simulation demonstrate that the suggested preview controller can make the output track the reference signal, and the tracking quality be improved as the preview length increases. [ABSTRACT FROM AUTHOR]

In this article, the problem of high precision motion control for motor servo systems with modeling uncertainties and unknown actuator backlash is addressed. The combination of synthesized adaptive laws and continuous nonlinear robust term handles parameter uncertainties and system disturbances. The adaptive technique updates the unknown parameters of actuator backlash in real time and the backlash inverse function eliminates the backlash effect. Meanwhile, the designed controller without knowing the range of the disturbance upper bound but automatically estimates through the adaptive law, which improves the engineering practicability. Finally, the theoretical analysis proves the perfect asymptotic stability of the presented controller even with unmodeled disturbances and unknown actuator backlash. Extensive comparative experiments reveal the superiority of the presented method. • A nonlinear robust adaptive controller is proposed for motor servo system with unknown actuator backlash. • The unknown upper bound of unmodeled disturbances can be updated by adaptive laws online. • Excellent asymptotic output tracking performance is achieved with the proposed controller. • The effectiveness of the proposed controller is verified by comparative experimental results. [ABSTRACT FROM AUTHOR]

Saturation is mainly characterized by its passivity and magnitude bound. But most of the saturation control methods only make use of either of these features. To enhance the performance of saturated systems, this paper develops a novel method capable of fully using both of these two features. This method is a two-stage design scheme which integrates the phase-shaping technique with the gain-scheduled control. The phase-shaping fully uses the passivity of saturation while the gain-scheduling actively utilizes the magnitude bound of saturation. In this way, the design conservatism associated with existing methods is reduced substantially. Specifically, a matrix-type phase-shaping method is developed through the placement of systems' frequency loci, and a meta-heuristic method is devised for the design of the phase-shaping function. Furthermore, the gain-scheduled control is transformed into the robust performance problem of a passive uncertain system, and designed by the passivity-based robust control method of the authors. Application to two practical control systems validates the effectiveness of the proposed method. The superiority is demonstrated via comparisons with typical saturation control methods. • Propose gain-scheduling with phase-shaping method for input-saturated linear systems. • Fully utilize the passivity in phase-shaping to maximize the system's phase margin. • Actively use the magnitude bound in gain-scheduling to reduce conservative design. • Extend the matrix phase-shaping by placing the nominal system's frequency locus. [ABSTRACT FROM AUTHOR]

In this study, an approach with the position control scheme is proposed for the mobile manipulator robot, by applying the adaptive strategies with the backstepping control technique. In the proposed backstepping controller, the fixed problem of main control parameters has been improved by the adaptive self-updating process. In addition, this control method requires no prior knowledge of the control system, such as the unknown dynamics and disturbances. And, to relax uncertainties, an adaptive robust controller is also designed as a compensator for the main backstepping controller. Moreover, all adaptive updating algorithms are designed based on the Lyapunov theorem so that the proposed control system performances are guaranteed. The advantage of the improved control system will be evaluated via simulation results for the 3 - DOF MMR system. • The proposed BC control inputs have been improved by online self-updating algorithms for fixed control gains. • With the simple adaptive estimator, the uncertain dynamic effects to MMR control system have been maximally excluded. • The robustness has also been reinforced by eliminating inevitable estimating/updating errors and other uncertainties. • All the adaptive algorithms are designed by the Lyapunov theorem so that the stability of control system is guaranteed. [ABSTRACT FROM AUTHOR]

The integration of BeiDou System (BDS) and the fifth generation (5G) system is expected to provide positioning services with high accuracy and reliability. In particular, as an important technique in the 5G system, device-to-device (D2D) communication enables the cooperative positioning for the mobile users (MUs) to offer a large number of redundant measurements, where the received signal strength (RSS) measurement can be exploited due to its easy access and cost-efficiency. However, the RSS measurements cannot be accurately modelled to reflect the propagation distances. In this paper, a robust BDS/5G integrated positioning scheme based on the resilient observation model is proposed. Specifically, the extended Kalman filter (EKF) is exploited to recursively integrate the BDS pseudorange and RSS measurements to estimate the unknown position parameters. We establish a resilient observation model based on the signal propagation model in the 5G network, through which the errors in the RSS measurements are compensated for by the BDS pseudorange measurements. Moreover, the effects of the abnormal errors in both kinds of the measurements are controlled by the robust estimation through automatically adjusting the weights of the measurements. Numerical results show that the proposed resilient observation model can correct part of the errors in the RSS measurements and results in higher positioning accuracy compared to the positioning based on the RSS measurements or the direct integration of both the BDS pseudorange and RSS measurements in different scenarios. The proposed algorithm also outperforms the positioning based on the BDS pseudorange measurements when the visible satellites are not sufficient. [ABSTRACT FROM AUTHOR]

• A new mathematical model for rubber–tyred gantry cranes is developed considering the elastic structure. • Two advanced sliding mode controls are proposed to validate dynamic model. • Experiment shows the sufficient accuracy and effective application of modeling and control to cranes. A gantry crane holds strict underactuation and elastic multibody structure, and thus faces challenges in control system design. For model–based control, we develop a three–dimensional dynamic model of rubber–tyred gantry cranes considering the distributed mass and bending deformation of gantry. Such modelling hybridises the lumped model of trolley–bridge–payload motions and continuous model of gantry vibration. We consider a complex operation in which three motors composed of travelling trolley, pushing bridge and hoisting container run simultaneously. We propose two versions of the robust controller, namely, dynamic sliding mode and fast terminal sliding mode for tracking the three motions, eliminating swings of container, and reducing the bending vibration of elastic gantry. Simulation and experiment reveal precision and effective application of our crane modelling to control approaches. The control system effectively tracks and stabilises all outputs and achieves robustness when a crane suffers from the structural and parametric uncertainties. [ABSTRACT FROM AUTHOR]

We analyse around four decades of annual time-series data revisiting the long-run relationship between globalization and income inequality for 24 OECD member countries across different geographical regions, applying the Yamamoto-Kurozumi multivariate vector autoregression (VAR) framework. We observe that rapid globalization is not the key cause of rising long-run intra country inequality. This result is obtained by controlling for growth, terms of trade, minimum wage legislation, and unionization and found robust by further controlling education. Most of the countries in our study with a long-run relationship reveal the robust reverse causal impact of rising globalization on reducing inequality. Our impulse response breakdown across various sub-components of globalization suggests that economic globalization is not a primary contributor to long-run inequality for developed industrialized countries. Our framework guides future research to concentrate more on country-specific relationships, with policy guidance tailored for each country based on their level of economic development and institutional quality. [ABSTRACT FROM AUTHOR]

This paper deals with the nonfragile fuzzy tracking control technique of a spacecraft subject to external disturbances and control gain uncertainties. First, the Takagi–Sugeno (T–S) fuzzy model of the tracking error dynamics is newly derived. Unlike the existing studies, we assume that the controller output is norm bounded and its gain matrix is perturbed because of the aging of the actuator. Next, a fuzzy disturbance observer (DOB) estimating the external disturbance is constructed to enhance the robustness of the control system. Then, a fuzzy controller feeding back the estimated disturbance is designed to attenuate external disturbances. In addition, we establish the linear matrix inequality (LMI) conditions, by which both the H ∞ disturbance attenuating performance and the asymptotic stabilization are guaranteed. From the solution to the LMI conditions, we obtain the controller and DOB gain matrices. Finally, a simulation example validates the disturbance estimation and tracking performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a fractional neural integral sliding-mode controller based on the Caputo-Fabrizio derivative and Riemann–Liouville integral for a robot manipulator mounted on a free-floating satellite is proposed and developed. The mathematical model is obtained by using the Euler–Lagrange formalism and generalized through the Caputo-Fabrizio derivative. The conventional neural integral sliding-mode controller reported in the literature were also developed to make a comparative analysis between both controllers. The controllers are tuned using a swarm intelligence algorithm. External disturbances are also considered. Simulation results show the superiority and robustness of the proposed control system for trajectory tracking tasks through the root-mean-square error. Additionally, two additional reference trajectories were considered. In this sense, the numerical simulations were done by using the same orders and gains, showing the robustness of our fractional-order control strategy under different operating conditions. Further, the proposed controller uses the fractional derivative in the sense of Caputo-Fabrizio which has not been used enough in the area of robotics, so this is a start to analyse the contributions that arise when using said derivative in this robotic. [ABSTRACT FROM AUTHOR]

Quadrotors are extremely agile UAVs with many unknowns regarding their moment of inertia, drag coefficients, and mass. Aside from aerodynamic turbulence, the quadrotor has a high level of nonlinearity due to its underactuated mechanical system. These last two factors cause issues that necessitate adaptive and robust control. A hybrid robust nonlinear control approach for a quadrotor is described in this research paper. To address the quadrotor trajectory tracking problem, a combination of backstepping and sliding-mode approaches based on proportional integral derivative (PID) surfaces is developed, with the Lyapunov method used to evaluate the control system's stability. The adaptive technique, in conjunction with sliding-mode and super-twisting algorithm, is then investigated in order to reduce the chattering impact of sliding mode control and estimate the various controller parameters. Super-twisting adaptive backstepping PID sliding mode approach for the quadrotor rotational and translational subsystems is presented to reduce error convergence time and improve speed tracking performance. The simulation results under various scenarios (parameters uncertainties under Gaussian random disturbances, constant and time-varying external disturbance) are given to demonstrate the efficacy and practicability of the proposed control. A comparative analysis is performed with different developed controllers using the performance of the integral of absolute error and the integral of the absolute value of the derivative of the control inputs to demonstrate the superiority and effectiveness of the designed control technique. [ABSTRACT FROM AUTHOR]

Tension control is critical for maintaining good product quality in most roll-to-roll (R2R) production systems. Previous work has primarily focused on improving the disturbance rejection performance of tension controllers. Here, a robust linear parameter-varying model predictive control (LPV-MPC) scheme is designed to enhance the tension tracking performance of a pilot R2R system for deposition of materials used in flexible thin film applications. The performance of a tension controller may degrade due to disturbances associated with model uncertainties and the slowly-changing dynamics in R2R systems. We introduce a method that separately treats these two sources of disturbance. The controller utilizes an incremental model to eliminate the errors caused by the mismatch between the nominal model and the actual system. A tube-based MPC formulation combined with scheduled parameters adequately updates models and corrects for the time-varying dynamics. Constraints on the rated motor torque are incorporated in the MPC to maintain the controller reliability and avoid machine failures. We illustrate the operation of our control algorithm through simulation of an actual R2R system. The controller outperforms the benchmarks in terms of fast transient response and offset-free tension tracking. It also demonstrates immunity from variations due to parametric uncertainties. • Accurate tension tracking is essential for high-precision roll-to-roll processes. • Incremental model compensates for model-plant mismatches. • Tube-based MPC ensures the robustness to time-varying dynamics. • Enforcing constraints on motors improves reliability and avoids machine failures. [ABSTRACT FROM AUTHOR]

Fractured formations lead to insufficient contact or loss of contact between the drill bit and rocks, which subsequently causes notable fluctuation in weight-on-bit due to flexible drill strings. This paper presents a robust integrated control design to suppress weight-on-bit fluctuation. First, a finite element drill-string longitudinal model is employed as the basis of control design, which has been verified using actual run data. The integrated controller contains a proportional–integral (PI) controller and a dynamic output feedback controller, dealing with the system's first-order and high-order dynamics. The dynamic output feedback controller only needs to focus on the system's high-order dynamics since the controller synthesis is based on the pre-designed PI controller. The controller decreases the flexible mode amplitude from disturbance to the output channel through H-infinity loop shaping, making the closed-loop system less flexible. The numerical results demonstrate that the developed approach can effectively suppress weight-on-bit fluctuation due to drill-string flexibility when drilling in fractured formations. • Suppress weight-on-bit (WOB) fluctuation caused by flexible drill strings and fractured formations in the drilling process. • A finite element drill-string model and a modified bit–rock interaction model in fractured formations are presented. • A robust integrated controller based on a two-step design procedure is presented. • Separate control design for the system's first-order and flexible modes. [ABSTRACT FROM AUTHOR]

The paper proposes a formation tracking control method for the uncertain artificial swarm systems under the inequality constraints. Not only can the agents perform swarm behaviors (e.g., convergence, formation and avoidance of collision), but they can also track the fixed targets in a constrained area (which is formulated as the inequality constraints, such as unilateral constraint and bilateral constraint.). The swarm behaviors are creatively considered as the servo constraints or the control objectives for the swarm agents. Based on the Udwadia–Kalaba (U–K) equation, those prescribed behaviors are realized by a model-based control design (that is the servo constraints force model-based feedforward control). To deal with the inequality constraints in the formation tracking process, a differential homeomorphism transformation is used to relieve the environmental constraints for the swarm agents. Moreover, the uncertainty of the swarm agents (i.e., the parameter uncertainty in modeling and the external disturbances) is considered, which is time-varying and unknown (but bounded). An uncertainty estimation method with dead-zone and leakage term is designed to calculate the possible upper bound of the uncertainty. In virtue of the estimated upper bound of the uncertainty, a robust control is designed for the uncertain swarm agents to obey the prescribed swarm behaviors in the formation tracking task. The system performances of the artificial swarm systems under the proposed control are theoretically guaranteed by a range of rigorous theorems and numerically verified by the simulations of three agents. • Converts an inequality constraint problem into an equation constraint problem. • A robust adaptive swarm tracking control design method with dead zones and leakage is proposed. • The designed artificial potential field function has the property of "repulsion at near and attraction at far". [ABSTRACT FROM AUTHOR]

Iterative learning model predictive control (ILMPC) has been considered as potential control strategy for batch processes. ILMPC can converge to the desired reference trajectory with high precision along batches and ensure system stability within batches. However, as a model-based control method, the control performance of the ILMPC algorithm deteriorates when exists model parameter uncertainty. Therefore, guaranteeing system tracking performance in the case of model parameter uncertainty is a challenging task in the framework designing of ILMPC method. To this end, we develop a two-stage robust ILMPC strategy for batch processes, which integrates the robust iterative learning control (ILC) in the domain of batch-axis and robust model predictive control (MPC) in the domain of time-axis into one comprehensive control scheme. The integrated control law of the developed two-stage robust ILMPC algorithm is obtained by solving two convex optimization problems. As a result, the developed control method obtains faster convergence speed and better tracking performance in the case of model parameter uncertainty. Moreover, the convergence analysis of the system is presented. Finally, comparative simulations are provided to verify the superiority of the developed control algorithm. • A two-stage robust iterative learning model predictive control for batch processes is proposed, the tracking performance with model parameter uncertainty can be improved. • The convergence analysis of the proposed control algorithm is provided. • Three comparative simulations are provided to verify the superiority of the developed control algorithm. [ABSTRACT FROM AUTHOR]

The stability of formation flight is not only sensitive to external disturbances but also to data observed and transferred between sensors and unmanned aerial vehicles (UAVs). A multi-constrained model predictive control (MPC) strategy, combined with Kalman-consensus filter (KCF) and fixed-time disturbance observer (FTDOB) is developed for the formation control of multiple quadrotors here Firstly, KCF is used to effectively fuse the data shared in the formation with noise and uncertainty, which improves the applicability and robustness of the formation in complex environments. Secondly, FTDOB is able to estimate the external disturbances suffered by the quadrotor in a fixed time and provides real-time compensation for the controller. On this basis, an improved MPC (IMPC) is designed for each UAV of the formation, which improves the computational efficiency while ensuring the asymptotic stability of the system. Eventually, the capability and effectiveness of the proposed strategy are verified by simulation in terms of disturbance rejection and noise suppression, as well as good trajectory tracking of the formation. • A modified MPC strategy is proposed for the formation control. • The use of an FTDOB ensures that the estimation error converges to zero within a fixed time. • KCF is used to fuse the data shared in the formation with noise and uncertainty. [ABSTRACT FROM AUTHOR]

This paper presents a gain-scheduling (GS) robust control strategy using the interpolation approach for ball screw drives while guaranteeing the system stability in the operating range. A set of linear-time-invariant (LTI) robust controllers is designed at selected table positions for the robustness against the uncertain load mass and the structural vibration suppression at those positions. By interpolating the LTI robust controllers, a GS robust controller is obtained to deal with the dynamic variations caused by the table motion. The closed-loop stability under the proposed GS robust controller is verified with the parameter-dependent Lyapunov function, which avoids tedious simulations to check the reliability of interpolating GS controllers before practical operations. The method presented in this paper enables the GS controller to consider the unmeasurable load mass in ball screw drives and simplifies the application complexity. The extensive experimental results demonstrate that the proposed GS robust controller significantly improves the tracking performance and the vibration suppression, in comparison to LTI controllers and GS controllers. • A gain-scheduling robust controller is developed for ball screw drives. • The table-position-dependent mode and the uncertain inertia are considered. • Parameter-dependent Lyapunov function is used to verify the closed-loop stability. • The proposed method improves the vibration suppression and trajectory tracking. [ABSTRACT FROM AUTHOR]

This article presents a robust coalitional model predictive control (MPC) approach where neighboring agents negotiate the bounds of their coupling variables. Also, the control variables of each agent are divided into a private part that is locally optimized, and a public part that is controlled by the corresponding neighbors. Under certain conditions, the agents communicate to update the bounds that determine the constraints on these private and public variables. Moreover, the mutual disturbances induced by coupling are considered using a tube-based approach, guaranteeing recursive feasibility and stability of the closed-loop system. The proposed method is tested on a simulated eight-input coupled tank benchmark to illustrate its benefits. • Splitting the global optimization problem by clustering model predictive controllers. • Coupling variables decomposition into multiple versions distributed among agents. • Event-based communication to negotiate the size of the input constraint set. • Mutual disturbances are considered using a tube-based approach. • Feasibility and stability of the closed-loop system are guaranteed. [ABSTRACT FROM AUTHOR]

This article presents the control of Soil moisture, Nitrogen, and Salt content in soil using a robust control technique to maintain a desired quality of soil in a drip irrigated field. The control of this multi-variable system is accomplished by adopting two possible control structures namely (i) a decentralized controller backed by a feed-forward controller (ii) a diagonal centralized de-coupler along with a diagonal controller. The controllers are designed using quantitative feedback theory. The efficacy of the proposed techniques is validated through numerical simulations. A comparative study among the two methods reveals that the former performs better than the later. Another comparative study shows that the proposed controller outperforms some of the reported works. • Control of Soil moisture, Nitrogen and Salt content in a drip irrigated field. • A 3 × 3 uncertain model is derived from physical laws and practical data. • De-centralized QFT along with feed-forward controllers is proposed. • Centralized decoupler with diagonal QFT based controllers is proposed. [ABSTRACT FROM AUTHOR]

This paper studies the adaptive neural networks (NNs) tracking control problem for a class of mobile robot systems with full-state constraints. First, to compensate for the adverse effects of the unknown dead-zone input, which is ubiquitous in mobile robot motors, a new robust control algorithm is put forward by the use of adaptive control technique. Then, a new unified barrier function (UBF) is constructed to deal with the problem of state constraints. Different from traditional barrier Lyapunov function (BLF) methods, which can only constrain the error of the system state and virtual controller, our proposed control method can constrain the system state directly by introducing a novel nonlinear transformation function and a new coordinate transformation. It's worth noting that the UBF can be utilized to deal with both constrained and unconstrained cases by resizing parameters without changing the control structure. Furthermore, adaptive NNs are introduced to approximate the uncertainty of the system. Finally, based on the Lyapunov stability theory, it is proved that all signals in the closed-loop system are ultimately bounded and the full-state constraints are never violated. The effectiveness of the control method is verified on simulation examples and a mobile robot experimental platform. [ABSTRACT FROM AUTHOR]

There exist the uncertainties and the inequality constraints in permanent magnet synchronous motor (PMSM) system. In order to meet the safety control requirements in industrial applications, the state transformation is used to meet the inequality constraints for limiting the outputs within desired bounds. Then, fuzzy set theory, which is different from fuzzy logic, is used to describe uncertainty, and the fuzzy PMSM dynamical model is established. Based on that, a robust control with high-order term is proposed to compensate for the time-varying uncertainty. Furthermore, for improving the system performance and decreasing the control cost, the Stackelberg game is introduced into the optimization scheme design, in which the leader plays a more important role than follower. These characteristic corresponds to the influence of the two tunable control parameters on the system. Thus, the optimal parameters are obtained by the rules of Stackelberg game. Finally, experimental results show the effectiveness of the above theories. • A robust control guarantees uniform boundedness and uniform ultimate boundedness. • The uncertainty in PMSM system is described by fuzzy set theory. • The inequality constraints are satisfied by the diffeomorphism principle. • The tunable control parameters are optimized based on Stackelberg game theory. • The intelligent cybernetics method and optimization scheme are established. [ABSTRACT FROM AUTHOR]