Adaptive Finite-Time Command Filtered Controller Design for Nonlinear Systems With Output Constraints and Input Nonlinearities

Adaptive Unified Output Constraints Control for Uncertain Interconnected Nonlinear Systems With Unknown Measurement Drifts

This article investigates the problem of unified output constraints for a class of uncertain interconnected nonlinear systems, where the measurement of system states is affected by unknown drifts in the powers of the measurement functions. Compared to previous works on output constraints, the main challenge addressed in this article is the unavailability of the true system states during the controller design process and the nondifferentiability of the sensor's output functions. To achieve the control objectives, the following control scheme is proposed in this study. First, a novel barrier Lyapunov function is introduced, which is specifically designed to handle systems with unknown measurement drifts. This function can be uniformly applied to satisfy both scenarios of systems with or without output constraints. Second, the adding a power integrator (AAPI) technique and dynamic surface control (DSC) techniques are enhanced to effectively handle the unknown measurement drifts and avoid singularity problems in the controller design. The decentralized controller proposed in this article can realize that the outputs are strictly constrained within predefined boundaries and guarantees convergence of all system states to an arbitrarily small neighborhood. Finally, we provide two simulation examples to validate the effectiveness of our proposed control strategy.

Composite Learning Adaptive Tracking Control for Full-State Constrained Multiagent Systems Without Using the Feasibility Condition

This article proposes a distributed consensus tracking controller for a class of nonlinear multiagent systems under a directed graph, in which all agents are subject to time-varying asymmetric full-state constraints, internal uncertainties, and external disturbances. The feasibility condition generally required in the existing constrained control is removed by using the proposed nonlinear mapping function (NMF)-based state reconstruction technology, and the Lipschitz condition usually needed in the consensus tracking is also canceled based on the adaptive command-filtered backstepping framework. The composite learning of the neural network-based function approximator (NN-FAP) and the finite-time smooth disturbance observer (DOB) provides a novel scheme for handling internal and external uncertainties simultaneously. One advantage of this scheme is that the use of online historical data of the closed-loop system strengthens the excitation of NN's learning. Another advantage is that the DOB with NN-FAP embedding realizes that the finite-time observation for external disturbance in the case of the system dynamics is unknown. A complete controller design, sufficient stability analysis, and numerical simulation are provided.

Integration of path planning and following control for the stratospheric airship with forecasted wind field data

The absence of real-time airspeed sensors, which was more often ignored in previous studies, and low dynamic characteristics render stratospheric airship control challenging. This study creatively overcomes the aforementioned problems in an integrated path planning and following control scheme using forecasted wind field data. Herein, an efficient and practicable path planning algorithm is designed. Further, a smooth vector field guidance law is proposed for solving the problem of complex path following. Subsequently, an event-triggered neural network-based adaptive tracking controller is designed, considering the wind forecast error influence. Finally, these three parts are organically integrated to achieve autonomous flight. The stability of the closed-loop system and the exclusion of Zeno behavior are rigorously proved. The simulation results reveal that the convergence rate is 63.8% improved, essentially exhibiting better optimization, the tracking errors are eliminated within 80 s, and 99.4% control input updating times are saved.