Neural networks-based robust adaptive flight path tracking control of large transport

For the ultralow altitude airdrop decline stage, many factors such as  actuator nonlinearity, the uncertain atmospheric disturbances, and model  unknown nonlinearity affect the precision of trajectory tracking. A robust  adaptive neural network dynamic surface control method is proposed. The  neural network is used to approximate unknown nonlinear continuous  functions of the model, and a nonlinear robust term is introduced to  eliminate the actuator’s nonlinear modeling error and external disturbances. From Lyapunov stability theorem, it is rigorously proved that all the signals in the closed-loop system are bounded. Simulation results confirm the perfect tracking performance and strong robustness of the proposed method

On a weaker notion of ring stability for mixed traffic with human-driven and autonomous vehicles

International audienc

Adaptive distributed control of uncertain multi-agent systems in the power-chained form

Power-chained form systems are a generalization of strict-feedback and pure-feedback systems since integrators with positive odd-powers can appear in the dynamics (chain of positive-odd power integrators) and they are extremely challenging to deal with, as their linearized dynamics might possess uncontrollable modes whose eigenvalues are in the right-hand-side plane, making standard feedback linearization or standard backstepping methodologies fail. The adding-one-power-integrator technique was proposed to handle power-chained formsystems. Progress made for power-chained formsystems includes employing universal approximators to handle completely unknown nonlinearities. However, state-of-the-art results on power-chained form systems are mainly focused on the single-agent case since a direct extension of the existing design to a distributed setting is not very meaningful on account of the facts that: i) the control gain of each virtual control is incorporated into the next virtual control law iteratively, possibly leading to high-gain issues; ii) state-of-the-art results rely on the assumption that the agents’ control directions are known a priori and are available for control design; iii) universal approximators often used in the adding-one-power-integrator procedure inevitably increase the complexity in the sense that extra adaptive parameters have to be updated (i.e. extra nonlinear differential equations need to be solved numerically), thus making their distributed implementation difficult.Team DeSchutte

The Set-Invariance Paradigm in Fuzzy Adaptive DSC Design of Large-Scale Nonlinear Input-Constrained Systems

This paper proposes a novel set-invariance adaptive dynamic surface control (DSC) design for a larger class of uncertain large-scale nonlinear input-saturated systems. The peculiarity of this class is that no a priori bound on the continuous control gain functions is assumed (i.e., their boundedness cannot be assumed before obtaining system stability). This requires a new design. Differently from the available methods, the proposed design involves the construction of appropriate invariant sets for the closed-loop trajectories, which allows to remove the restrictive assumption of a priori bounds of the control gain functions. Furthermore, we show that such set-invariance design can handle input constraints in the form of input saturation. In line with the DSC methodology, semi-globally uniformly ultimate boundedness is proven: however, differently from the standard methodology, stability analysis requires the combination of Lyapunov and invariant set theories.</p

Neural-network-based adaptive tracking control for nonlinear pure-feedback systems subject to periodic disturbance

This paper presents an adaptive neural control to solve the tracking problem of a class of pure-feedback systems with non-differentiable non-affine functions in the presence of unknown periodically time-varying disturbances. To handle with the design difficulty from non-affine structure of pure-feedback system, a continuous and positive control gain function is constructed to model the periodically disturbed non-affine function as a form that facilitates the control design. As a result, the non-affine function is not necessary to be differentiable with respect to control variables or input. In addition, the bounds of non-affine function are unknown functions, and some appropriate compact sets are introduced to investigate the bounds of non-affine function so as to cope with the difficulty from these unknown bounds. It is proven that the closed-loop control system is semi-globally uniformly ultimately bounded by choosing the appropriate design parameters. Finally, comparative simulations are provided to illustrate the effectiveness of the proposed control scheme.</p

Nonrecursive Control for Formation-Containment of HFV Swarms With Dynamic Event-Triggered Communication

This article proposes an output-feedback control protocol for hypersonic flight vehicle (HFV) swarms considering dynamic event-triggered communication. The peculiarities of the proposed method over existing ones consist in the following: 1) While carrying out scheduled maneuvers, the outputs of follower HFVs converge inside the convex hull spanned by leader HFVs whose task is to maintain a geometric space configuration; 2) a simple nonrecursive output-feedback design is established without involving any intermediate control laws or requiring full-state information; 3) an error-dependent monotonically decreasing exponential term is incorporated into the dynamic event-triggered threshold to reduce the communication bandwidth while preserving the desired track performance and excluding Zeno behavior. Comparative simulation results validate the effectiveness of the proposed methodology

Consensus in high-power multiagent systems with mixed unknown control directions via hybrid Nussbaum-based control

This work investigates the consensus tracking problem for high-power nonlinear multiagent systems with partially unknown control directions. The main challenge of considering such dynamics lies in the fact that their linearized dynamics contain uncontrollable modes, making the standard backstepping technique fail; also, the presence of mixed unknown control directions (some being known and some being unknown) requires a piecewise Nussbaum function that exploits the a priori knowledge of the known control directions. The piecewise Nussbaum function technique leaves some open problems, such as Can the technique handle multiagent dynamics beyond the standard backstepping procedure? and Can the technique handle more than one control direction for each agent? In this work, we propose a hybrid Nussbaum technique that can handle uncertain agents with high-power dynamics where the backstepping procedure fails, with nonsmooth behaviors (switching and quantization), and with multiple unknown control directions for each agent.</p