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Based on a line-of-sight (LOS) guidance law for a curve parametrized path, a finite-time backstepping control is proposed for the kinematic path-following of an underactuated autonomous surface vehicle (ASV). Finite-time observer is utilized to estimate the unknown external disturbances accurately. The first-order Levant differentiator is introduced into the finite-time filter technique, such that the output of filter can not only approximate the derivative of the virtual control, but also avoid the singularity problem of real heading control. The integral terminal sliding mode is employed to improve the tracking performance and converging rate in the surging velocity control. By virtue of Lyapunov function, all the signals in the closed-loop system can be guaranteed uniformly ultimate boundedness, and accurate path-following task can be fulfilled in finite time. The simulation results and comparative analysis validate the effectiveness and robustness of the proposed control approach. [ABSTRACT FROM AUTHOR]

This paper aims to improve the tracking control performance of the three-stage valve (TSV) controlled electro-hydraulic servo system (EHSS) with parameter uncertainties and other lumped unknown nonlinearities, including unknown dynamics and disturbances. A more accurate nonlinear model of the TSV-controlled EHSS is established and a neural network-based finite-time command-filtered adaptive backstepping control (NNFCABC) method is proposed for the EHSS. Adaptive control is used to deal with the system parameter uncertainties, and the radial basis function neural network (RBFNN) algorithm is introduced to approximate the lumped unknown nonlinearities. The prediction errors of serial-parallel estimation models (SPEMs) and the tracking errors are utilized together to design adaptive laws to estimate the system parameters and the weights of the RBFNNs. The entire control framework utilizes command-filtered control and backstepping techniques. By applying Levant differentiators as command filters and introducing fractional power terms into the virtual control laws and the SPEMs, the proposed NNFCABC theoretically guarantees the tracking performance of the closed-loop control system with finite-time convergence. Comparative simulations and experiments verify the feasibility and superiority of the proposed control scheme. • A more accurate model of the three-stage valve is built. • Neural network and adaptive control are combined under command-filtered technology. • The finite-time convergence of all signals in the system is achieved. • Simulations and experiments have verified the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Background: Safety and significant improvement in overall glycated hemoglobin (A1C) and percentage of time spent in (TIR), below (TBR), and above (TAR) glucose range were demonstrated in the pivotal trial of adolescents and adults using the MiniMed™ advanced hybrid closed-loop (AHCL) system with the adjunctive, calibration-required Guardian™ Sensor 3. The present study evaluated early outcomes of continued access study (CAS) participants who transitioned from the pivotal trial investigational system to the approved MiniMed™ 780G system with the non-adjunctive, calibration-free Guardian™ 4 Sensor (MM780G+G4S). Study data were presented alongside those of real-world MM780G+G4S users from Europe, the Middle East, and Africa. Methods: The CAS participants (N = 109, aged 7–17 years and N = 67, aged >17 years) used the MM780G+G4S for 3 months and data of real-world MM780G+G4S system users (N = 10,204 aged ≤15 years and N = 26,099 aged >15 years) were uploaded from September 22, 2021 to December 02, 2022. At least 10 days of real-world continuous glucose monitoring (CGM) data were required for analyses. Glycemic metrics, delivered insulin and system use/interactions underwent descriptive analyses. Results: Time in AHCL and CGM use were >90% for all groups. AHCL exits averaged 0.1/day and there were few blood glucose measurements (BGMs) (0.8/day–1.0/day). Adults in both cohorts met most consensus recommendations for glycemic targets. Pediatric groups met recommendations for %TIR and %TBR, although not those for mean glucose variability and %TAR, possibly due to low use of recommended glucose target (100 mg/dL) and active insulin time (2 h) settings (28.4% in the CAS cohort and 9.4% in the real-world cohort). The CAS pediatric and adult A1C were 7.2% ± 0.7% and 6.8% ± 0.7%, respectively, and there were no serious adverse events. Conclusions: Early clinical use of the MM780G+G4S was safe and involved minimal BGMs and AHCL exits. Consistent with real-world pediatric and adult use, outcomes were associated with achievement of recommended glycemic targets. Clinical Trial Registration number: NCT03959423 [ABSTRACT FROM AUTHOR]

This paper presents a command‐filter‐based formation control scheme for the leader‐follower omnidirectional mobile robots (OMRs) subject to the absence of the velocity measurements, input saturation and unknown bounded disturbances. First, an adaptive velocity observer is designed based on a novel state transformation of the OMR dynamics. Then, a finite‐time command filtering strategy is proposed, where the Levant filter is used to estimate the time derivative of the virtual control law, and an error compensator with finite time convergence is constructed to eliminate the filtering error correspondingly. Moreover, the anti‐saturation module and adaptive law are designed to attenuate the influence caused by input saturation and external disturbances. The proposed control scheme can ensure that all signals of the closed‐loop system including the formation error are uniformly bounded, and the effectiveness is verified by the simulations results. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the command filter‐based adaptive neural network tracking control problem for uncertain nonsmooth nonlinear systems. First, an integral barrier Lyapunov function is introduced to deal with the symmetric output constraint and make the output comply with prescribed restrictions. Second, by the Filippov's differential inclusion theory and approximation theorem, the considered nonsmooth nonlinear system is converted to an equivalent smooth nonlinear system. Third, the Levant's differentiator is used to deal with the "explosion of complexity" problem. An error compensation mechanism is established to attenuate the effect of the filtering error on control performance. Then, an adaptive neural network controller is set up by resorting to the backstepping technique. It is strictly mathematically proved that the tracking error can converge to an arbitrarily small neighborhood of the origin and all the signals in the closed‐loop system are semi‐globally uniformly ultimately bounded. Finally, a numerical example and an application example of the robotic manipulator system are provided to demonstrate the availability of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This article considers the problem of finite-time command-filtered control for switched nonlinear systems with input quantization and output constraints. The unmeasurable state is estimated by designing a switched state observer. During the design process, to overcome the chattering problem effectively, the hysteresis quantization is designed as two bounded nonlinear functions. Furthermore, in order to restrict the output to an expected range, the barrier Lyapunov function approach is introduced. The "explosion of complexity" and the error compensation problems in the backstepping design are solved by using a finite-time command-filtered approach. A first-order Levant differentiator is used instead of the general command filter in this paper, which can not only filter the intermediate signals accurately to get the differential signals, but also ensure the finite-time stability of the filter. Stability of the closed-loop system in the sense of semi-global practical finite-time stability (SGPFS) is proved by exploring a multiple Lyapunov functions approach. Finally, a simulation example is provided to verify the validity of the presented control method. [ABSTRACT FROM AUTHOR]

This paper investigates a class of output-mask-based adaptive neural network (NN) tracking control for nonlinear stochastic time-delayed multi-agent systems (STMASs) based on a unified event-triggered approach. The output signal relies on an output mapping acted as a mask, defined as a privacy-protection-like method, so that the internal state of one agent cannot be identified by other distrustful eavesdroppers or attackers. Moreover, the construction of a unified event-triggered control scheme retains the advantages of the saturation threshold triggering strategy, incorporates distributed errors, and increases the flexibility of thresholds. Furthermore, for stochastic time-delay multi-agent systems, the initial value limitation of the conventional first-order filter is removed by a first-order Levant differentiator, and a new estimation term in the fuzzy observer is established to solve the nonlinear fault. The unknown function in pure-feedback form is addressed via combining Butterworth low-pass filter and radial basis function neural networks (RBF NNs). Finally, the boundedness of all signals in the closed-loop systems is demonstrated, and the effectiveness of the proposed algorithm is verified by some simulation results. • This paper proposes a privacy-protection-like scheme for continuous systems to make those signals transmitted in ciphertext. • A unified approach for event-triggered control, namely dynamic saturation threshold event trigger, was established. • The proposed method relaxes two conditions and reduces the conservatism. [ABSTRACT FROM AUTHOR]

Countries in the Middle East are requiring larger and more complex shipping operations for the growing downstream sectors of the hydrocarbon industry, such as refining and petrochemistry. These shipping operations generally entail a closed-loop system, in which the vessels travel back and forth from a manufacturing plant to various international ports for exporting the hydrocarbon products. The current shipping systems only focus on feasible, smooth, and repeatable operations with a first-available-first-use (FAFU) policy, owing to the lack of a rigorous and systematic optimal scheduling method for maritime transportation systems. This study presents an integrated short-term scheduling model formulated as a mixed integer linear programming (MILP) model, for dispatching vessels from a petrochemical plant to international ports and economically operating a plant-side port in the closed-loop shipping system. Further, the MILP model considers varying vessel capacities and other related complex restrictions. A constructive heuristic algorithm combined with an optimization solver is adopted to solve the MILP model with sufficient speed for practical use. Numerical studies using a real data set demonstrate the effectiveness of the developed model compared to the current scheduling policy, FAFU. Moreover, we analyze the economic and computational performances of the developed solution method. Our method solves the test scenarios effectively, presenting a cost saving opportunity of 11.3% on average, which is equivalent to 439,829 USD per year for the studied operation. [ABSTRACT FROM AUTHOR]

This paper studies a time-varying formation control problem of a swarm of autonomous surface vehicles (ASVs) with prescribed constraints. Every ASV suffers from unavailable velocities, saturated inputs, internal uncertainties, and external disturbances. A distributed finite-time performance-prescribed time-varying formation control method is proposed for ASVs. Specifically, an adaptive fuzzy state observer (AFSO) is firstly proposed to recover the unavailable velocity by using saturated inputs and estimated disturbances from the fuzzy logic system. Secondly, a tunnel prescribed performance (TPP) function is designed to limit tracking errors with a concise form and smaller overshoot. At the kinematic level, with TPP-based error transformation, a C 1 finite-time time-varying guidance law is proposed by using the smooth switch functions and recovered velocities. Next, a finite-time command filter based on Levant differentiator is presented to smooth the guidance signals. At the kinetic level, a C 1 finite-time saturated control law is developed with the estimated velocities and disturbances. Then, it proves that tracking errors of the proposed closed-loop system converge into a small neighborhood around the equilibrium within finite time while evolving under TPP constraints regardless of saturated inputs. Finally, comparison results are provided to demonstrate the effectiveness of the proposed distributed finite-time performance-prescribed time-varying formation control method. • Distributed finite-time performance-prescribed formation control method is proposed for ASVs. • The TPP with a concise form is developed to achieve smaller overshoot in a positive manner. • An adaptive fuzzy state observer is designed to recover unavailable velocities. [ABSTRACT FROM AUTHOR]