In this research, an extended state observer (ESO) based fast finite-time control problem for nonstrict-feedback system is considered. A novel tanh-type ESO is proposed to estimate the unknown nonlinearities, and a tanh-type fast finite-time filter is proposed to address the computational complexity issue. Through the utilisation of hyperbolic tangent function in ESO and proposed filter, the jitter phenomenon induced by the sign function is eliminated. The event-triggered mechanism based on relative threshold tactic is introduced to minimise communication bandwidth. By combining dynamic surface control (DSC) with the backstepping control technique, the strategy proposed in this article can make all the states in the considered system maintain bounded in finite time. Meanwhile, the tracking error and estimation errors of ESO converge to an interval near zero within the finite time. Finally, an intelligent ship autopilot model is introduced to attest the feasibility of the designed scheme.

With the increased need for information exchanges, the communication part of stochastic nonlinear multiagent systems (MASs) suffers from the influence of limited communication resources, which brings challenges to the efficiency of the information exchange between agents. This paper investigates the synchronous self-triggered adaptive tracking control problem for stochastic nonlinear MASs. To begin with, a novel synchronous self-triggered mechanism is constructed based on nonlinear impulsive dynamics, which ensures simultaneous updates of agent states and adaptive laws without continuous monitoring. Besides, this mechanism ensures the consistency of triggering instants for the same-order states of multiple agents, thereby effectively avoiding the communication congestion problem caused by asynchronous triggering. Next, a new class of adaptive triggering update law is designed, which expands the feasible parameter domain and enhances system flexibility. In addition, a first-order filter is introduced into the controller design to address the discontinuity of virtual control signal caused by the triggering mechanism. Based on the Lyapunov stability theory, it is shown that the designed controller can ensure that all signals in the closed-loop system stay bounded in probability. Finally, a simulation example is conducted to further demonstrate the validity of the presented control method.

Adaptive fuzzy finite-time observer-based control for nonlinear cyber-physical systems with actuator hysteresis and sensor deception attacks

Inverse optimal design of input-to-state stabilizing homogeneous controllers for nonlinear homogeneous systems

Neighbor-error-based distributed cooperative learning control for nonlinear multi-agent systems with multi-bridge-hole constraints

Dynamic event-triggered optimal safety control of interconnected nonlinear systems with discontinuous state constraints by adaptive dynamic programming

Global low-complexity hybrid tracking control of MIMO nonlinear systems with discontinuous references

Self-triggered adaptive dynamic programming for optimal control of multi-input nonlinear systems

Adaptive fuzzy wavelet network control for nonlinear cooperative load transportation systems

Nonlinear dynamic behavior, impact suppression, and stability control of rotor active magnetic bearing systems: a comparative study of fixed and adjustable surplus current strategies

Adaptive optimal control for nonlinear networked systems under encoding-decoding mechanism: A generalized policy iteration approach

CBF-based safety design for adaptive control of uncertain nonlinear strict-feedback systems

Distributed fixed-time adaptive neural fault-tolerant tracking control of uncertain nonlinear multiagent systems under communication link faults

Backstepping-based fault-tolerant control for strict-feedback nonlinear multi-agent systems: An encoding–decoding scheme

An adaptive policy iteration learning algorithm for optimal tracking control of nonlinear systems with state inequality constraints

This article proposes a unified suboptimal controller design method for unknown general nonlinear systems subject to multiple constraints, including state, input, and output constraints. All inequality constraints are transformed into equality constraints using slack functions and Pade approximation. An unconstrained augmented system is then defined to describe the dynamics of original system and the equality constraints, where the optimal controller of the augmented system can be viewed as a suboptimal controller for the original system. Furthermore, considering the unmodeled dynamics of the original system, neural networks (NNs) are utilized and a data-based solution strategy of integral reinforcement learning (IRL) is presented for the augmented system. Ultimately, the simulation results are given to reflect the effectiveness of the unified design method.

Inverse compensation-based optimized intelligent control for nonlinear systems driven by hysteretic actuators

Hierarchical optimization-based online learning control for nonlinear systems with unknown disturbances

Safety constrained digital control of nonlinear systems with state delays

Prescribed-time output feedback control for nonlinear systems with unknown measurement sensitivity