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A high-bandwidth nonlinear acceleration disturbance observer (NOADOB) is proposed to reject transient peak disturbances mainly arising from velocity commutation of an inertial stabilization-tracking platform (ISTP). The approach is essentially based on extracting acceleration peak signals and applying a saturation function. The observer structure employs acceleration measurements and combines velocity loop input and control quantity to acquire peak signals. Differing from the general disturbance observer, the extraction of acceleration peaks is used to quickly capture the change in error and effectively reduce the impact of signal noise for the observer implementation. To trade off the observer bandwidth and the system stability, a partial compensatory scheme of limiting the filter output with a nonlinear saturation function is adopted. The relationship between the saturation threshold and the filter bandwidth is analyzed for optimal balance. It is theoretically and experimentally proved the effectiveness of NOADOB in attenuating error peaks. Moreover, the proposal accelerates the dynamic response to peak disturbances, breaks through the constraint of accurate modeling, and improves disturbance rejection performance.

This article aims to provide a high-precision sensorless controller for permanent magnet in-wheel motors (PMIWMs) used in electric vehicles (EVs) when an unexpected failure of the position sensor occurs. To address this, we propose a position sensorless control method that combines a global fast terminal sliding mode observer (GFTSMO) with a phase-locked loop (PLL) estimation scheme. First, the GFTSMO is introduced to reduce the significant chattering that typically occurs in traditional sliding mode observers (SMO). This control scheme can help the state variable converge to an equilibrium state from any initial condition and minimize chattering. Second, we implement a PLL estimation scheme to replace the conventional arc-tangent estimation method. This method avoids the triggering of high-frequency oscillations, thereby improving the estimation precision and robustness of the control system. In addition, we discuss the stability of the proposed GFTSMO using the Lyapunov function. Simulation and tests on a motor platform demonstrate that the proposed position sensorless algorithm can track speed rapidly and accurately without overshooting. The proposed sensorless control can ensure the control stability of PMIWMs and can be applied in other occasions where installing motor sensors is challenging.

This paper studies the robust finite-time input-to-state stability (R-FT-ISS) including robust finite-time stability (R-FTS) via impulsive hybrid control (IHC) for uncertain dynamical systems (UDS) with disturbances. The notions of robust GKL-stability, R-FT-ISS, and R-FTS are proposed. Time-based IHC (T-IHC) and state-based IHC (S-IHC) are proposed, respectively. And based on the Hamilton-Jacobi inequalities of Lyapunov-like functions, less restrictive R-FT-ISS and R-FTS criteria are established for UDS under IHC (including T-IHC and S-IHC). And the event-triggered S-IHC schemes for R-FT-ISS and R-FTS are designed. Correspondingly, the estimates of settling time for R-FT-ISS and R-FTS are also obtained, respectively. Theoretical results and numerical simulations show that both T-IHC and S-IHC can achieve not only R-FT-ISS but also R-FTS for unstable systems with structural disturbances and external disturbances. Therefore, IHC (including T-IHC and S-IHC) can eliminate the impact on stability from disturbances and thus both T-IHC and S-IHC are anti-disturbance and robust, which improves the stabilization only to ISS (not to asymptotic stability) in the presence of disturbance in the literature. It is also shown that R-FT-ISS and R-FTS can be achieved by pure impulsive control, which improves the FTS results of impulsive systems in the literature. Moreover, compared with T-IHC, S-IHC has less number of impulses and lower cost than T-IHC while T-IHC has smaller settling time.

Virtual coupling (VC), aimed at achieving closer tracking distances and facilitating flexible train formations, has become an efficacious trend for alleviating the increasing demand for transportation capacity as the continuous advancement of urbanization. This paper investigates safety prescribed performance control (SPPC) for VC trains with multifold kinematic targets and switching constraints (SCs). In comparison to traditional continuous PPC methods, SPPC strategy incorporating discontinuous characteristics is designed to ensure that position and speed errors of VC train formation remain within the maximum overshoot limit within a finite time after the mutation of tracking target. It is achieved by separating the analysis of continuous intervals and discontinuous impulsive points, which ensures the stable properties of the closed-loop system through utilizing the Lyapunov method and strict mathematical justification. To ensure the safe train operation, multiple kinematic targets are considered throughout the entire process, from un-formation to final cooperative VC formation. To avoid the occurrence of unstable switching before the completion of the formation, the monitor output for the mode partition is made subject to the joint effect of position and speed targets. By imposing a minimum dwell time constraint, the train's real-time running state does not switch frequently within a single mode, which eliminates the Zeno phenomenon. Finally, based on implementing several scenarios for multi-train VC formation adhering to SCs, and incorporating the sample data sourced from the high-speed G1 train traversing from Beijing South Railway Station (BSRS) to Jinan West Railway Station (JWRS), the effectiveness of mode partition and the theoretical results in train operations are verified in numerical simulations.

This paper addresses the problem of adaptive output feedback trajectory tracking for plants with an unknown input gain. To tackle this issue, a control method is proposed, which stems from the Active Disturbance Rejection Control (ADRC) and employs the dynamic inversion approach to generate the auxiliary control law in a transient state, while simultaneously taking advantage of the adaptive input gain identification to produce the tracking controller, ensuring asymptotic tracking in the steady state. Specifically, a smoothly saturated gradient law with projection is used to ensure the desired properties of the parameter estimates dynamics, and eliminate the parameter instability in the transient states. The boundedness of the trajectories of the system is established in the most general case, and an asymptotic convergence of tracking and estimation errors for a disturbance-free system is proven. The theoretical findings are validated by experimental trials employing two laboratory testbeds.

This paper focuses on the problem of event-triggered output feedback control for low-order stochastic nonlinear time-delay systems with stochastic inverse dynamics. Before proceeding, it is necessary to address several technical obstacles. Firstly, the strategy of varying the supply rate is paired with stochastic-input-to-state-stable (SISS) to address immeasurable stochastic inverse dynamics. Secondly, a novel reduced-order observer with a set of constant gains is designed. In addition, an event-triggered output feedback controller is developed and a new analytical approach is applied to show that the Zeno behavior does not happen. With the aid of the Lyapunov stability theory, it is proved that the closed-loop system is globally asymptotically stable in probability. Finally, a simulation example is presented to illustrate the effectiveness of the proposed event-triggered control scheme.

This article studied the actor-critic learning scheme for safe leader-follower affine formation maneuver control of networked quadrotors under external disturbances, sensor deception attacks, and injection attacks on the actuators. The followers aim to track formation maneuvers such as scaling, shearing, translation, and rotation determined by the leaders. Motivated by increasing safety and performance requirements during formation maneuvering, the dynamic states of the quadrotors are constrained within prescribed safety constraints. A barrier Lyapunov function is employed to ensure that the safety constraints are not violated. Then, a distributed sliding mode control with actor-critic learning is formulated to facilitate accurate leader-follower affine formation maneuvers and reject malicious cyber-attack signals. The input gains that appear due to the attacks might corrupt the control direction. The Nussbaum gain function is coupled to the controller to tackle this problem. The actor system estimates the uncertain dynamics and malicious attack signals, while the critic network evaluates the control performance through the estimated long-term performance index. The overall stability of the closed-loop system has been proven to be bounded using the Lyapunov stability theorem. Finally, simulation results demonstrate the capability of the presented control method.

This paper discusses the admissible consensus tracking problem for nonlinear singular multi-agent systems under sampled-data event-triggered mechanisms, where the nonlinear dynamics are unknown. In the sampled-data framework, both static and dynamic event-triggered mechanisms are designed based on the observers, and the dynamic event-triggered mechanism has superior performance. Moreover, in order to eliminate the effect of bounded consensus caused by the double estimation problem, this study proposes a distributed adaptive event-triggered control protocol. This protocol is designed to establish sufficient conditions for achieving admissible consensus tracking of singular multi-agent systems. Finally, the theoretical results are verified by three numerical examples.

This paper investigates the event-triggered disturbance rejection control problem for a class of uncertain Lipschitz nonlinear systems with time-varying disturbances. The design process to address this problem involves the construction of a state and disturbance joint observer, along with the design of an event-triggered controller that incorporates a designable minimum inter-event time. To design the joint observer, time-varying disturbances are initially transformed into multiple intermediate variables by employing a linear combination of system states and disturbance derivatives. Subsequently, the recursive observers are constructed to obtain intermediate variable estimations, while the recursive mechanism is utilized to compensate for the extra derivative term arising from disturbance transformation. Then, states and disturbance estimations are used to develop the event-triggered disturbance rejection controller. The event-triggered protocol includes a mandatory resting interval and a dynamic trigger variable that monotonically increases during the resting interval, ensuring a guaranteed minimum inter-event time while also decreasing the trigger frequency. Finally, we provide numerical examples to showcase the effectiveness of the proposed method. Simulation results show that the proposed method is able to reduce the steady-state error by 59.6% and extend the minimum trigger interval by at least 2.44 times compared to existing methods.

Resilient consensus control refers to a system's ability to maintain agreement among its components despite disruptions, failures, or malicious attacks. This paper introduces a resilient control algorithm for a group of robotic manipulators to achieve leader-follower consensus within their workspace, despite actuator faults and deception attacks affecting exchanged signals. The proposed approach leverages adaptive control algorithms to develop the control laws. It begins by introducing an adaptive fault-tolerant tracking control to ensure tracking performance despite model uncertainties, actuator faults, and external disturbances. An adaptive observer is then developed to mitigate the effects of false data injections caused by deception attacks. A key feature of this control framework is its ability to operate without requiring fault and attack detection, thereby improving the system's robustness and applicability. The stability of the network and the convergence of the filtered errors are demonstrated using Lyapunov techniques and the equivalence principle. The proposed control framework is validated through numerical simulations involving a network of four heterogeneous manipulators, with results confirming the approach's effectiveness in enhancing system reliability.