This paper proposes an approach for fixed-time (FxT) adaptive optimized formation control of nonlinear multi-agent systems (MASs) with unknown nonlinear dynamics and full-state constraints. To address system uncertainty and state constraints while achieving optimality in FxT settings, the paper presents a novel adaptive estimation and analysis. The proposed approach first introduces a tan-type nonlinear mapping to handle state constraints, eliminating the feasibility condition of the conventional barrier Lyapunov function method. Next, the actual optimal controller is iteratively designed using the identifier-actor-critic structure and optimized backstepping method, with neural approximators used to learn system uncertainty. Finally, a monotonically decreasing function is constructed to prove that the designed actor-critic update laws have an upper bound, which is essential for stability analysis. The proposed scheme can ensure that the formation is realized at a fixed time while optimizing a given performance index and meeting the constraint requirements. The simulation results verify the effectiveness of the proposed approach.

This paper presents a novel approach for addressing the finite-time distributed formation maneuvering (FTDFM) of multiple unmanned surface vehicles (USVs), which takes into account the challenges posed by velocity and error constraints. Each USV is subject to parameter uncertainty, ocean disturbance, actuator fault, and input saturation, making the task of achieving reliable and accurate formation particularly challenging. To overcome these challenges and meet practical requirements, a finite-time (FT) performance function is selected as the constraint function, which ensures that the velocity and error of each USV stay within a given bounded set within a known time. Using FT stability theory, a new framework is proposed that integrates a tangent-type barrier function and an improved backstepping approach to handle uncertainties and constraints. In this approach, a tracking differentiator (TD) is introduced to replace the virtual controller's derivative, and a smooth function is used to address the input saturation, effectively reducing the complexity and dynamic order of the algorithm. The proposed controller is capable of ensuring the realization of the desired formation within a finite time while maintaining the constraints without deviation. Additionally, by using the auxiliary variable technique, the proposed control method can also be applied to USVs with underactuated models. Simulation examples are provided to demonstrate the efficacy of the proposed control algorithm in achieving accurate and reliable formation maneuvering of multiple USVs under various constraints.

This article explores the application of reinforcement learning (RL) strategy to achieve an adaptive fixed-time (FxT) optimized formation control of uncertain nonlinear multiagent systems. The primary obstacle in this process is the difficulty in attaining FxT stability under the actor-critic setting due to intermediate estimation errors and generic system uncertainties. To overcome these challenges, the RL control algorithm is implemented using an identifier-actor-critic structure, where the identifier is utilized to address the system uncertainty involving unknown nonlinear dynamics and external disturbances. Furthermore, a novel quadratic function is introduced to establish the boundedness of the estimation error of the actor-critic learning law, which plays a pivotal role in the FxT stability analysis. Finally, a unified FxT optimized formation control strategy is developed, which guarantees the realization of the predetermined formation at a fixed time while optimizing the given performance measure. The effectiveness of the proposed control algorithm is verified through simulation of a team of marine surface vessels.

The adaptive practical prescribed-time (PPT) neural control is studied for multi-input multi-output (MIMO) nonlinear systems with unknown nonlinear functions and unknown input gain matrices. Unlike existing PPT design schemes based on backstepping, this study proposes a novel PPT control framework using the dynamic surface control (DSC) approach. Firstly, a novel nonlinear filter (NLF) with an adaptive parameter estimator and a piece-wise function is constructed to effectively compensate for filter errors and facilitate prescribed-time convergence. Based on this, a unified DSC-based adaptive PPT control algorithm, augmented with a neural networks (NNs) approximator, is developed, where NNs are used to approximate unknown nonlinear system functions. This algorithm not only addresses the inherent computational complexity explosion associated with traditional backstepping methods but also reduces the constraints on filter design parameters compared to the DSC algorithm that relies on linear filters. The simulation showcases the effectiveness and superiority of the devised scheme by employing a two-degree-of-freedom robot manipulator.

This article studies the finite-time output-feedback cooperative formation problem of multiple marine surface vessels (MSVs) without using velocity information, where each MSV contains unknown time-varying actuator faults, ocean disturbances, and model uncertainties. Considering the performance specifications and requirements in practice, the formation errors and velocities are also required to change in a predefined compact set. Aiming at the challenge of unknown input gain caused by unknown time-varying multiplicative faults, a novel nonlinear extended state observer with an adaptive law is first constructed, which cannot only recover unmeasurable velocities from position-heading information but also simultaneously compensate for ocean disturbances, model uncertainties, and additive faults. Subsequently, combining the barrier Lyapunov function and the nonlinear tracking differentiator technique, a unified finite-time output-feedback cooperative control framework is developed, including both kinematics and kinetics, to prevent constraint deviation and avoid the computational complexity of the traditional backstepping method. Using finite-time stability theory, the designed output feedback cooperative controller can ensure that the desired cooperative performance is achieved within a finite time and tracking errors converge to a small neighborhood of the origin while ensuring that the closed-loop system is semiglobally uniformly ultimately bounded. Simulation examples are shown to demonstrate the effectiveness of the proposed control scheme.

We consider control design for a nonlinear ordinary differential equation (ODE) and transport partial differential equation (PDE) cascaded system whose propagation speed is spatially-varying. The ODE state is driven by the uncontrolled boundary of the transport PDE. Our nonlinear delay-compensated control law enables global asymptotic stability of the closed-loop system. The controller design is deduced using a backstepping transformation and constructing a Lyapunov functional. We offer global stability guaranteed by the assumption of the propagation speed is continuously differentiable and positive. The validity of the proposed controller is illustrated by a simulation result.

We consider predictor control of a nonlinear ODE/wave PDE cascaded system with state-dependent propagation speed. First, the original cascaded system is transferred into a coupled hyperbolic PDE cascading with a nonlinear ODE by the first-step backstepping transformation. Further, this cascaded system is transferred into a pair of transport PDEs with state-dependent coefficients cascading into a nonlinear ODE by the second-step backstepping transformation. The key challenge is to prove the well-posedness and uniqueness of kernel equations, and design a compensator for the resulting cascaded system. Our design provides asymptotic stability and attraction region estimation for the closed-loop system since the propagation speed of wave depends on the ODE's state. An example is given to show the validity of the proposed design.

As patient breathing irregularities can introduce a large uncertainty in targeting the internal tumor volume (ITV) of lung cancer patients, and thereby affect treatment quality, this study evaluates dose tolerance of tumor motion amplitude variations in ITV-based volumetric modulated arc therapy (VMAT). A motion-incorporated planning technique was employed to simulate treatment delivery of 10 lung cancer patients’ clinical VMAT plans using original and three scaling-up (by 0.5, 1.0, and 2.0 cm) motion waveforms from single-breath four-dimensional computed tomography (4DCT) and multi-breath time-resolved 4D magnetic resonance imaging (TR-4DMRI). The planning tumor volume (PTV = ITV + 5 mm margin) dose coverage (PTV D95%) was evaluated. The repeated waveforms were used to move the isocenter in sync with the clinical leaf motion and gantry rotation. The continuous VMAT arcs were broken down into many static beam fields at the control points (2°-interval) and the composite plan represented the motion-incorporated VMAT plan. Eight motion-incorporated plans per patient were simulated and the plan with the native 4DCT waveform was used as a control. The first (D95% ≤ 95%) and second (D95% ≤ 90%) plan breaching points due to motion amplitude increase were identified and analyzed. The PTV D95% in the motion-incorporated plans was 99.4 ± 1.0% using 4DCT, closely agreeing with the corresponding ITV-based VMAT plan (PTV D95% = 100%). Tumor motion irregularities were observed in TR-4DMRI and triggered D95% ≤ 95% in one case. For small tumors, 4 mm extra motion triggered D95% ≤ 95%, and 6–8 mm triggered D95% ≤ 90%. For large tumors, 14 mm and 21 mm extra motions triggered the first and second breaching points, respectively. This study has demonstrated that PTV D95% breaching points may occur for small tumors during treatment delivery. Clinically, it is important to monitor and avoid systematic motion increase, including baseline drift, and large random motion spikes through threshold-based beam gating.

This paper investigates the finite-time distributed formation maneuvering control for multiple autonomous surface vessels (ASVs) systems, in which not only velocity and formation error constraints are simultaneously considered, but also the existence of actuator faults and multiple uncertainties are tolerated. To deal with the serious uncertainty and two classes of constrained signals, an improved adaptive backstepping and barrier function are combined in a unified distributed framework. Afterwards, by introducing a nonlinear tracking differentiator to estimate the derivative of the virtual controller, a finite-time distributed maneuvering controller is constructed for each ASV to reduce the computational complexity caused by the traditional backstepping method. Based on the finite-time stability theory, the constructed distributed controller for each ASV can ensure that the closed-loop system realizes the semiglobally finite-time stability, the velocity and formation error of each ASV remain within the defined compact set and the desired formation is achieved within a finite time. Finally, the effectiveness of the proposed control scheme is proved by simulation example.

A novel approach is proposed to solve the problem of the starch adhesive's low strength in humid environment. Herein, silicone and itaconic acid were grafted with native starch, and then the starch-based adhesive (IA-M/B-OSt) was obtained with a high wet-bond strength and environmentally friendly. Subsequently, the IA-M/B-OSt adhesive was applied in plywood processing, and the plywood turned out to have strong bonding strength even after soaking in hot water at 63 degrees C for 3 h. The results of SEM, FT-IR, Confocal Raman and H-1 NMR confirmed that grafting reactions occurred between silicone and starch. Hot-pressing was necessary for the application of IA-M/B-OSt adhesive, which facilitated the transformation of Si-OH into Si-O-Si and achieved high wet-bond strength. This study provides a simple and efficient approach for preparing starch based adhesive, and opens up a new way for applying biomass materials in the green and sustainable adhesive industry.

This paper aims to address a finite-horizon model predictive control (MPC) for non-linear drum-type boiler-turbine system using a system-identification method. Considering that the strong state coupling of a non-linear mechanism model, the subspace identification method is first utilized to obtain a linear state-space model, and transformed into an input–output model. By taking the inputs and outputs of the input–output model as system states, an augmented non-minimal state-space (NMSS) model of state measurable is constructed. In order to reduce the computation burden, the augmented NMSS model is further transformed into a canonical formulation by adopting a Kalman decomposition. Based on the minimal realization state-space model, the MPC controller is parameterized as a finite-horizon optimization problem. Finally, simulations are performed and evaluated the performance of the proposed method, and the simulation results show that: the linear model approximate the non-linear system accurately; the proposed MPC method can achieve a satisfactory stable control performance; and the computation time 18.388 s for the overall optimization problem also illustrates the real-time performance effectively.

This paper investigates the distributed linear quadratic tracking control problem for multi-agent leader–follower systems. Considering that only few followers can access the state information of the leader owing to the limited communication range, a novel distributed control law is designed to make followers convergent to the leader by introducing appropriate interconnections among the followers. Besides the tracking consensus of the leader–follower systems, the designed control law also enables the associated cost to be less than a given tolerance for any initial states of the leader and the followers. The implementation of the designed distributed control law can be executed by solving a single Riccati equation, requiring no global information of the communication topology. Two examples are finally provided to demonstrate the effectiveness of the proposed method.

This article investigates decentralized adaptive tracking problems for nonlinear large‐scale systems with strong interconnections, unknown control directions and actuator faults. In the presence of measurable system states, a decentralized state feedback control algorithm with a certain inherent fault tolerance capabilities is developed using the dynamic surface control and adaptive techniques. Particularly, a special Nussbaum gain is introduced in the control laws to compensate unknown control directions and unknown bounds of actuator efficiency factor. Using the graph theory and Lyapunov analysis method, it is proved that the designed fault‐tolerant state feedback controller can drive the tracking error for each subsystem to a small neighborhood of the origin while keeping the semiglobal uniform ultimate boundedness for all other closed‐loop signals. When the system states are unmeasurable, an output feedback fault‐tolerant control framework is also formed by introducing a new K‐filters with adjustable parameters and constructing appropriate coordinate transformations. Finally, the effectiveness of the proposed fault‐tolerant control algorithms is verified by simulating two system models.

Ligand-activated signaling through the type 1 insulin-like growth factor receptor (IGF1R) is implicated in many physiological processes ranging from normal human growth to cancer proliferation and metastasis. IGF1R has also emerged as a target for receptor-mediated transcytosis, a transport phenomenon that can be exploited to shuttle biotherapeutics across the blood–brain barrier (BBB). We employed differential hydrogen–deuterium exchange mass spectrometry (HDX-MS) and nuclear magnetic resonance (NMR) to characterize the interactions of the IGF1R ectodomain with a recently discovered BBB-crossing single-domain antibody (sdAb), VHH-IR5, in comparison with IGF-1 binding. HDX-MS confirmed that IGF-1 induced global conformational shifts in the L1/FnIII-1/-2 domains and α-CT helix of IGF1R. In contrast, the VHH-IR5 sdAb-mediated changes in conformational dynamics were limited to the α-CT helix and its immediate vicinity (L1 domain). High-resolution NMR spectroscopy titration data and linear peptide scanning demonstrated that VHH-IR5 has high-affinity binding interactions with a peptide sequence around the C-terminal region of the α-CT helix. Taken together, these results define a core linear epitope for VHH-IR5 within the α-CT helix, overlapping the IGF-1 binding site, and suggest a potential role for the α-CT helix in sdAb-mediated transcytosis.

The distributed fault detection and isolation of linear discrete time-varying multi-agent systems subject to heterogeneous dynamics and norm bounded model uncertainties is investigated. By combining the model uncertainties and external disturbances into a new generalized disturbance, a distributed closed-loop residual generator is constructed based on the estimate of the generalized disturbance. Then, the concerned fault detection is transformed into an indefinite quadratic minimum problem by using the finite-horizon robust H∞ filtering method, for which necessary and sufficient minimum conditions are provided by the Krein-space theory. Afterwards, a computationally efficient recursive algorithm is further developed to determine the residual for individual agent. Based on the resulting residuals, the fault occurrence can be alerted by devising a novel distributed fault detection scheme, which is then applied for the fault isolation by some appropriate transformation of the output measurements. Finally, both numerical and practical simulations are proposed to verify the effectiveness of the proposed algorithm.

This paper investigates the distributed fault detection problem for linear discrete time-varying heterogeneous multi-agent systems under relative output information. Due to the lack of absolute outputs, an augmented model is built by stacking all local relative output information. Then, the fault detection problem consisting of residual-generation and residual-evaluation is handled using the H ∞ filtering framework. The residual-generation problem is actually a minimization problem of an indefinite quadratic form, and the Krein space-Kalman filtering theory is applied, which results in a low computational burden despite the time-varying characteristic. Using the Krein space theory, a necessary and sufficient condition for the minimum is derived, and a residual-generation algorithm is developed. Further, a residual-evaluation mechanism is designed by constructing an evaluation function and detecting faults by comparing it with a threshold. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed fault detection approach.

The paper investigates the output feedback control for nonlinear systems with non-smooth unknown output functions. The output function only needs to have a generalized derivative (which may not be derivable), and the upper and lower bounds for which may not to be known. The considered system includes more other uncertainties, for example, the integral input-to-state stable (iISS) cascade subsystem, the unknown control direction, the unmeasured states and the external disturbance. To overcome the effects caused by the external disturbance, we first treat the disturbance as an extended state, and a state observer is designed to estimate both the unavailable system states and the external disturbance. In addition, to deal with the challenge raised by the unknown control direction and the non-smooth output function, we choose a special Nussbaum function with a fast growth rate to ensure the integrability for the derivative of the selected Lyapunov function. After that, a dynamic output feedback controller is designed to drive the system states to the origin while keeping the boundedness for all other closed-loop signals. Finally, a simulation example is given to show the effectiveness of the control scheme.

SummaryThis paper investigates the output feedback control for the uncertain nonlinear system with the integral input‐to‐state stable (iISS) cascade subsystem, which allow not only the unknown control direction but also the unknown output function. The unknown output function only needs to have a generalized derivative (which may not be derivable), and the upper and lower bounds of the generalized derivative need not to be known. To deal with the challenge raised by the unknown output function and the unknown control direction, we choose a special Nussbaum function with a faster growth rate to ensure the integrability for the derivative of the selected Lyapunov function. Then, a dynamic output feedback controller is designed to drive the system states to the origin while keeping the boundedness for all other closed‐loop signals. Moreover, via some appropriate transformations, the proposed control scheme is extended to deal with more general uncertain nonlinear cascade systems with quantized input signals. Finally, two simulation examples are given to show the effectiveness of the control scheme.

This paper investigates the output feedback control for nonlinear systems with unknown output functions and unknown growth rates, where the unknown output functions are Lipschitz continuous and the unknown growth rates are time-varying. To deal with this challenging control problem, a time-varying observer is designed and a time-varying scaling transformation is introduced, which can avoid using the derivative information of the output function and can effectively compensate the unknown time-varying growth rate. Then, by combining the time-varying scheme, the backstepping method and the certainty equivalence principle, an output feedback controller is designed to guarantee the boundedness of the closed-loop system states and the global convergence of the original system states. Finally, two simulation examples are given to show the effectiveness of the control scheme.

Isocyanate blocking can improve the long-term stability of isocyanate and reduce its toxicity for use in aqueous systems. The current isocyanate blocking technique has a low blocking degree, and this study designed a reasonable process route using tetrabutylammonium bromide (TBAB) as the phase transfer catalyst in isopropyl alcohol (IPA) solvent to prepare water-soluble isophorone diisocyanate (IPDI) blocked by NaHSO3 with a blocking degree of ≥ 98%. This work used 1% m (TBAB) / m (NaHSO3) with a m (IPA): m (H2O) ratio of 1 : 1; the NaHSO3 was prepared in a 15% aqueous solution. The n (HSO3−) : n (-NCO) ratio was greater than 1 : 1. The stirring speed was 200 r/min, the reaction temperature was 30 °C, and the reaction time was 2.5 h. FT-IR was used to analyze the changes in the main functional groups during the blocking reaction. TG and DCS were used to analyze the deblocking characteristics of water-soluble blocked IPDI. This paper also studied the effect of the blocked IPDI on paper properties. Experimental results showed that the strength and water resistance of paper made from the treated fiber can be remarkably improved by using water-soluble blocked IPDI.