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Today, hydrogen fuel cells (HFCs) have become very popular in various applications because of their ability to be a clean energy source. One of the difficulties associated with HFCs is their sluggish response to variations in the load. This paper presents a nested control strategy based on a type-2 neuro-fuzzy controller (T2NFC) to improve the HFC's dynamic response in a hybrid stand-alone power network using HFC and battery. A system model is constructed using Matlab-Simulink. An interleaved converter is used to draw the maximal power from the HFC and reduce the ripple in the HFC's current. Two T2NFCs control DC voltage and battery charge/discharge current. The robustness of the T2NFC is evaluated for input disturbance, output disturbance, and both disturbances. The results show that the proposed control strategy is robust against input and output disturbances. Also, it provides improved dynamic response of the HFC, lower ripple in HFC current, and less overshoot or undershoot in DC voltage both in transient and steady-state. For step reference input, the proposed controller improves settling time of 24.06 % and overshoot of 59.55 % compared to the conventional PI controller. The results verify the effectiveness of the proposed controller under different operating conditions of hybrid stand-alone power network with fuel cell and battery. • The response of HFC is enhanced, leading to faster and more accurate power generation. • The hydrogen fuel cell's current is experienced a significant reduction in ripples. • The hydrogen fuel cell is now being operated at its maximum power point. • A Type-2 Neuro-Fuzzy Controller is developed to provide robust control of the hydrogen fuel cell's output voltage. [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]

• 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]

• 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]

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]

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]

This paper proposed a novel underactuated control scheme to deal with the rotational deployment of a dual-body tethered satellite system without using tangential force acting on the tether. To describe the spin motion of the system during tether deployment, the parent satellite is modeled as a rigid body. An underactuated robust control framework is developed to govern the rotational deployment of the system in the presence of disturbances and model uncertainty. The adaptation update laws are designed to adjust the gain of the robust term of the controller, thereby improving the anti-interference ability of the system to cope with the perturbations stimulated by the spinning of the parent satellite. The underactuated rotational deployment controller theoretically guarantees the stability of motion of the entire system, that is, the uniformly ultimate boundness of the closed-loop control system can be strictly proven using Lyapunov theory. Finally, simulations are performed to validate the effectiveness of the proposed control scheme. • Rotational deployment strategy for a tethered satellite system is investigated. • The underactuated methodology governs the deployment process without resorting to tangential forces. • An adaptive-gain based robust controller improves the anti-interference ability of the system. [ABSTRACT FROM AUTHOR]

This paper proposes a novel feedforward/feedback combined control applied to a piezoelectric （PZT） actuated micro positioning stage. Based on the rate-dependent P–I inverse hysteresis model, a feedforward controller is built to compensate the hysteresis characteristics of the piezoelectric actuators, and the relevant parameters of the model are obtained through system identification. An adaptive control algorithm is adopted in feedback controller to reduce the influence of external disturbance. By introducing error-related forgetting factors to adjust the weight of old data and new data in real time, the robustness and accuracy of the control system are improved. The simulations are carried out to estimate the performance of the time-varying forgetting factors based adaptive algorithm. Finally, experimental tests are conducted to investigate the performance of the proposed feedforward/feedback combined control. The experimental results indicates that the settling time is 53 ms corresponding to the square wave signal and the maximum tracking error of a sine trajectory with the frequency of 20 Hz is 1.63 μm. [Display omitted] • A novel feedforward/feedback hybrid control is proposed. • A rate-dependent P–I inverse hysteresis model is built to compensate the hysteresis. • An adaptive control is adopted in feedback controller. • Error-related forgetting factor is adopted to adjust the weight of data in real time. [ABSTRACT FROM AUTHOR]

The ship-borne Stewart platform compensates for the six-degree-of-freedom motion induced by the ship, thereby enhancing operational reliability, safety, and expanding the operational window. Firstly, this paper investigates the dynamical characteristics during the motion compensation process. It reveals that the platform's motion generates an inertia force that counteracts the inertia force generated by the ship's motion, then only the gravity is left to compensate for each leg. Subsequently, the nonsingular terminal sliding mode control law of the platform is derived based on the dynamical characteristics to achieve robust control performance, followed by a comprehensive analysis of Lyapunov stability. To improve the accuracy of the robust controller, an adaptive estimation method utilizing time-delay estimation and Kalman filtering is employed to estimate the time-varying gravity. This approach significantly enhances the precision of the controller. Finally, to validate the compensation effect of the proposed method under conditions of parameter uncertainty, measurement uncertainty, and disturbances, a simulation model of the Stewart platform is established. Comparative analysis with the PID(Proportional-Integral-Derivative) control method and RBSMC(Recursive Backstepping Sliding Mode Control) demonstrates that the proposed method effectively improves the compensation rate. [ABSTRACT FROM AUTHOR]

This article presents a novel adaptive contour control scheme for dual-linear-motors-driven gantry stages (DLMDGSs) with time-varying model parameters and disturbances, aiming to realize high-precision contour control. Specifically, a novel adaptive contour controller based on a coupled model is proposed, incorporating powerful projection adaption laws, to achieve the synchronization of multi-axis motors, regression of adaptive parameters, and high-precision contour performance. Additionally, a contour error estimation (CEE) method with four-order convergence rate is designed to ensure estimation accuracy, enhance estimation robustness, and reduce estimation time. Moreover, considering the periodicity of common control contour tasks, an iterative learning control (ILC) reference trajectory compensation structure is adopted to further improve the contour control effect. Finally, the stability and convergence of the closed-loop system are rigorously proven, and comparative experiments are conducted on a DLMDGS platform to demonstrate the superiority of the proposed control strategy. [ABSTRACT FROM AUTHOR]

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

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

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]

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

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

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

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 introduces an advanced H∞ frequency control method for MGs, addressing uncertainties in secondary frequency control resources. By integrating parametric cyber uncertainties, it effectively counteracts unknown cyber-attack impacts, ensuring stable dynamic performance during cyber-attacks without requiring additional isolation or detection methods, simplifying MG control. • This study assesses cyber threats, incorporating physical uncertainties and RES/load fluctuations. It models key cyber threats (FDI, DoS, controller hijacking) and designs robust controls treating cyber-attacks and RES/load fluctuations as bounded uncertainties and disturbances. • The proposed H∞ control surpasses classical H∞ , optimal PI, and traditional frequency control in enhancing MG security, control, and functionality. It enables proactive control across diverse scenarios, ensuring cyber robustness, resilience, and uninterrupted performance against potential disruptions. • The application of the proposed robust control can be extended to the interconnected MG operations, significantly improving overall robust performance, even when the robust control is implemented in just one area during multiple cyber-attack events. A microgrid (MG) is a smart grid cyber-physical system, with component coordination relying on cyber resilience. Weak communications, protocols, and tools make the MG's secondary frequency control vulnerable to various cyber-attacks, posing new challenges and stability risks. In response to this challenge, this paper introduces the enhanced robust H ∞ technique considering the dynamic impacts of cyber-attacks on secondary frequency control to develop a secondary frequency control loop, improving the regulation performance and cyber resiliency of the MG frequency. The secondary control cyber-attack strategies mainly rely on false data injection (FDI), denial of service (DoS), and controller hijacking. These attack techniques are simultaneously considered in formulating the H∞ problem and control synthesis as unstructured parametric uncertainty, attenuating the concurrent cyber impacts. The study extends a load frequency control model to illustrate how cyber-attacks can be represented mathematically and physically in the MG. The results reveal that cyber-attacks affect secondary frequency control elements differently depending on the type of cyber threats used. By implementing an enhanced H∞ controller, the MG can effectively maintain stable frequency levels even when faced with malicious attacks and disruptions caused by renewable energy sources and loads. [ABSTRACT FROM AUTHOR]

For control-affine nonlinear systems with a disturbance and unknown parameters satisfying a matching condition but not having a known bound, we introduce a direct adaptive control scheme with three design elements: (a) nonlinear damping, (b) single-gain adjustment, and (c) deadzone in the update law. Combining these three ingredients for the first time, we guarantee the following previously unattained property: an attenuation of the plant state to an assignable small level, despite unknown bounds on the parameters and disturbance, without a drift of the gain, state, and input. Given the tools and results, we refer to the proposed scheme as a Deadzone-Adapted Disturbance Suppression (DADS) Controller. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) are increasingly exploited for various precise missions including inspection of subsea systems, hydro-graphic surveys, environmental monitoring, maintenance operations, harbor security operations, and so on. This article presents an improved tracking control approach for such systems subjected to ocean turbulences and uncertainties. The proposed method utilizes self-tuning control parameters and adaptive robust laws to improve the tracking performance and overcome challenges associated with nonlinear dynamics, uncertainties, and external disturbances. The proposed methodology includes the kinematics controller as the outer controller and the kinetics controller as the inner servo loop designed based on an adaptive integral-exponential fast terminal sliding surface. The results indicate a drastic improvement in the integral of error and integral of time multiplied by error compared to relevant control methods algorithms such as sliding mode control (SMC), Adaptive nonlinear control, and terminal SMC in similar working conditions, though the performance of all these algorithms is reasonably acceptable. • Developing a kinematics controller based on the proposed optimal PID tuning method to determine virtual expected velocities. • Developing a kinetics controller as the inner servo loop based on the system dynamics model and an adaptive integral-exponential fast terminal sliding surface to calculate optimal forces and moments. • Improving the integral of error (IE) and integral of time multiplied by error (ITE) under similar working conditions compared to other control algorithms (SMC, Adaptive nonlinear control, and terminal SMC) by adjusting control coefficients using the Sequential Quadratic Programming (SQP) algorithm and adaptive laws. [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]

This paper deals with the formalization, design, theorization, and implementation of a robust adaptive nonlinear control scheme to solve the bottom sitting-oriented soft landing control problem for an underwater glider under model uncertainties, actuator dynamics, and external disturbances. First, uniformized motion equations are derived based on Lie derivatives for describing pitch angle and vertical velocity tracking control. Then, the robust adaptive control framework is enhanced by containing an anti-windup auxiliary system and a disturbance observer. The stability of the closed-loop control system based on Lyapunov theory is proven to guarantee the boundedness of the entire internal signals. Finally, multifarious experimental results are also conducted by a self-developed underwater glider to demonstrate the effectiveness of the proposed controller. • Formularize the bottom sitting-oriented soft landing problem for UG. • Propose uniformized motion equations for pitch/vertical velocity tracking control. • Operate without detailed a priori knowledge of parametric uncertainties and perturbations. • Provide economical thoughts for easy-to-operate experiments to verify control performance. [ABSTRACT FROM AUTHOR]

The paper presents a nonlinear dynamic model for DGMSCMGs, sometimes used as actuators to control the attitude of large satellites. The developed models describe the translation dynamics, the rotation dynamics of the Active Magnetic Bearing Rotor, as well as the rotation dynamics of the two mobile gimbals. Two control architectures are initially designed by using the dynamic inversion concept, proportional-integrator-derivative/proportional-derivative dynamic compensators, linear observers, and a neural network to compensate the effect of the dynamic inversion error. One also develops a similar adaptive control architecture consisting of a proportional-integrator dynamic compensator, a feed-forward neural network, and a linear observer. The latter system models two interconnected nonlinear servo-systems and controls the angular rates of the two mobile gimbals actuated by the attitude controller of the satellite. The validation of the novel control architectures is achieved in Matlab/Simulink, the obtained results proving a very good angular rate precision and the robustness of the control systems in relation to the external disturbances. [ABSTRACT FROM AUTHOR]

For a class of nonlinear systems with mismatched time delays and disturbances, robust adaptive stabilization control is studied. The control law is split into two terms. One is used to cope with the delayed states, and the other is to handle the disturbance. The improved robust adaptive laws with control gain function and time-varying σ -modification are constructed to estimate the unknown integrated parameters. It is proved that the designed adaptive controller can ensure the asymptotic convergence of the closed-loop system states. Another adaptive algorithm is also proposed to ensure the asymptotic stability of the closed delayed nonlinear system without external disturbance. • We introduce control gain function ρ (⋅) and embed it into the Lyapunov–'Krasovskii functional. • Continuous memoryless control functions are designed. • The theoretical result is applied to chemical reactor. [ABSTRACT FROM AUTHOR]

In this paper, a robust non-singular fast terminal sliding mode control scheme with adaptive neural networks is presented for a class of nonlinear systems with unknown bounds of uncertainties. To reduce transmission and computation burden in resource-constrained networked systems, two kinds of event-triggering mechanisms are taken into consideration in the proposed adaptive sliding mode control scheme. The one, from the sensor to the controller, can guarantee finite-time convergence of system states into a predesigned band; and ensure that Zeno behavior does not occur. The other, from the controller to the actuator, can guarantee the asymptotically stability and Zeno behavior exclusion. Simulation results are given to verify the effectiveness and feasibility of the proposed schemes. [ABSTRACT FROM AUTHOR]