Adaptive fuzzy prescribed-time connectivity-preserving consensus of stochastic nonstrict-feedback switched multiagent systems

An adaptive fuzzy prescribed-time connectivity-preserving consensus protocol is designed for a class of stochastic nonstrict-feedback multiagent systems, in which periodic disturbances, switched nonlinearities, input saturation, and limited communication ranges are taken into consideration simultaneously. The connectivity, determined by the limited communication ranges and initial positions of agents, is preserved by incorporating an error transformation. Further, a common Lyapunov function is considered to deal with the switching modes. By combining a reduced fuzzy logic system with Fourier series expansion, a novel approximator is constructed to deal with periodically disturbed nonlinearities and to surmount the difficulty brought by the nonstrict-feedback structure. More importantly, distinctly from the existing finite/fixed-time control strategies where the settling time is heavily dependent on the accurate value of the initial states and control parameters, the settling time of the proposed prescribed-time consensus is completely independent of the initialization and control parameters and can be given a priori only according to actual demands. Based on the Lyapunov stability theory, the designed controller ensures that the connectivity-preserving consensus is achieved in prescribed time and all the signals remain bounded in probability. To the end, the feasibility of the proposed consensus protocol is demonstrated by simulation

Adaptive command-filtered finite-time consensus tracking control for single-link flexible-joint robotic multi-agent systems

This article presents a command-filtered finite-time consensus tracking control strategy for the considered single-link flexible-joint robotic multi-agent systems. First, each agent system considered in this article is a nonlinear nonstrict-feedback system with unknown nonlinearities, so the traditional backstepping method cannot be directly applied to the design controller. However, by applying the unique structure of the Gaussian function in radial basis function neural networks, the challenges in controller design caused by the aforementioned nonstrict-feedback system have been overcome. Second, the problem of unknown nonlinearities in the system is solved by the approximation property of radial basis function neural network technology. In addition, the traditional backstepping approach often leads to an “explosion of complexity” resulting from repeated derivation of virtual control signals. Our design addresses this issue by employing command filtering technology, which simplifies the controller design process. Meanwhile, new compensation signals are designed, which successfully eliminate the error influence posed by the filters. It is seen that the control strategy presented in this article can guarantee the tracking errors converge to a small neighborhood of origin in a finite time, and all signals in the closed-loop systems remain bounded. Eventually, the simulation results show the validity of the acquired control scheme

T-S Fuzzy Model Based H

This paper presents a double loop controller for a 7-DoF automobile electrohydraulic active suspension via T-S fuzzy modelling technique. The outer loop controller employs a modified H-infinity feedback control based on a T-S fuzzy model to provide the actuation force needed to ensure better riding comfort and handling stability. The resulting optimizing problem is transformed into a linear matrix inequalities solution issue associated with stability analysis, suspension stroke limit, and force constraints. Integrating these via parallel distributed compensation method, the feedback gains are derived to render the suspension performance dependent on the perturbation size and improve the efficiency of active suspensions. Adaptive Robust Control (ARC) is then adopted in the inner loop design to deal with uncertain nonlinearities and improve tracking accuracy. The validity of improvements attained from this controller is demonstrated by comparing with conventional Backstepping control and a passive suspension on a 7-DoF simulation example. It is shown that the T-S fuzzy model based controller can achieve favourable suspension performance and energy conservation under both mild and malevolent road inputs

Adaptive Fuzzy Tracking Control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functions are designed, and a novel error transformation function is introduced to remove the initial value constraints and solve the singularity problem in existing works. Secondly, based on dynamic surface control methods, controllers with or without approximating structures are established to guarantee that the tracking error achieves prescribed transient performance and converges into a prescribed bounded set within prescribed time. In particular, the settling time and initial value of the prescribed performance function are completely independent of initial conditions of the tracking error and system parameters, which improves existing results. Moreover, with a novel Lyapunov-like energy function, not only the differential explosion problem frequently occurring in backstepping techniques is solved, but the drawback of the semi-global boundedness of tracking error induced by dynamic surface control can be overcome. The validity and effectiveness of the main results are verified by numerical simulations on practical examples

Synchronous MDADT-Based Fuzzy Adaptive Tracking Control for Switched Multiagent Systems via Modified Self-Triggered Mechanism

In this paper, a self-triggered fuzzy adaptive switched control strategy is proposed to address the synchronous tracking issue in switched stochastic multiagent systems (MASs) based on mode-dependent average dwell-time (MDADT) method. Firstly, a synchronous slow switching mechanism is considered in switched stochastic MASs and realized through a class of designed switching signals under MDADT property. By utilizing the information of both specific agents under switching dynamics and observers with switching features, the synchronous switching signals are designed, which reduces the design complexity. Then, a switched state observer via a switching-related output mask is proposed. The information of agents and their preserved neighbors is utilized to construct the observer and the observation performance of states is improved. Moreover, a modified self- triggered mechanism is designed to improve control performance via proposing auxiliary function. Finally, by analysing the re- lationship between the synchronous switching problem and the different switching features of the followers, the synchronous slow switching mechanism based on MDADT is obtained. Meanwhile, the designed self-triggered controller can guarantee that all signals of the closed-loop system are ultimately bounded under the switching signals. The effectiveness of the designed control method can be verified by some simulation results

A novel nussbaum functions based adaptive event-triggered asymptotic tracking control of stochastic nonlinear systems with strong interconnections

In this work, the issue of event-triggered-based asymptotic tracking adaptive control of stochastic nonlinear systems in pure-feedback form with strong interconnections is considered. First, a new decentralized control scheme is developed by introducing the new types of Nussbaum functions, which enables the output of each subsystem to asymptotically track the desired reference signal. Second, the nonaffine structures and the unknown control gains existing in the nonlinear systems are a part of the considered system model, which makes it more complicated to design the decentralized controllers. Therefore, the complexity caused by the nonaffine structures is faciliated by mean value theorem and the unknown control gains are handled by a novel Nussbaum function in our proposed design scheme. Meanwhile, the unknown nonlinearities of the system are approximated by using intelligent control technology. Furthermore, an event-triggered method is introduced in the design process to save communication resources effectively. It is shown that all signals of the closed-loop systems are bounded in probability and the tracking errors asymptotically converge to zero in probability. Finally, the simulation results illustrate the effectivity of the presented scheme