Presence Of Time-varying Input Delay Research Articles

Event-triggered finite-time fuzzy control approach for fractional-order nonlinear chaotic systems with input delay

In this paper, a novel fuzzy event-triggered control approach with guaranteed performance of practical finite-time tracking convergence is constructed for uncertain fractional-order nonlinear chaotic systems in the presence of time-varying input delay. Firstly, fuzzy logic systems are employed to deal with immeasurable systematic information. Secondly, fractional-order command filters are incorporated to circumvent the problem of “complexity explosion” in backstepping controller construction. Meanwhile, a sequence of fractional-order finite-time compensation signals are introduced to counteract the adverse influence of time-varying input delay rapidly. Furthermore, a novel practical finite-time chaos control approach based on event-triggered strategy is proposed, which not only facilitates the decrease of sampling frequency and controller burden dynamically, but also ensures the convergence of all tracking errors towards a small region around the origin in finite time. Finally, through the simulation research, the transient response performance and the chaos-suppression ability of fractional-order chaotic systems stabilized via the suggested approach are rigorously testified.

Unknown System Dynamics Estimator for Active Vehicle Suspension Control Systems With Time-Varying Delay.

This article proposes a novel control method for vehicle active suspension systems in the presence of time-varying input delay and unknown nonlinearities. An unknown system dynamics estimator (USDE), which employs first-order low-pass filter operations and has only one tuning parameter, is constructed to deal with unknown nonlinearities. With this USDE, the widely used function approximators (e.g., neural networks and fuzzy-logic systems) are not needed, and the intermediate variables and observer used in the traditional estimators are not required. This estimator has a reduced computational burden, trivial parameter tuning and guaranteed convergence. Moreover, a predictor-based compensation strategy is developed to handle the time-varying input delay. Finally, we combine the suggested USDE and predictor to design a feedback controller to attenuate the vibrations of vehicle body and retain the required suspension performances. Theoretical analysis is carried out via the Lyapunov-Krasovkii functional to prove the stability of the closed-loop system. Simulation results based on professional vehicle simulation software Carsim are provided to show the efficiency of the proposed control scheme.

Adaptive Type-2 Fuzzy PID LFC for an Interconnected Power System Considering Input Time-Delay

This paper addresses the designing of an adaptive type-2 fuzzy PID (T2FPID) controller for load frequency control (LFC) of an interconnected nonlinear power system considering input time-delays. The input delay, which can commonly be raised by failure in the communication links and actuator operating lag may lead to control system instability. In the proposed adaptive T2FPID controller, output scaling factors have been chosen judiciously (according to a stable tuning algorithm) and thus improves the stability and performance of the controlled system. To perform this, the stability of the proposed method has been examined using the Lyapunov–Krasovskii (L-K) theorem, and the adaptation laws of the output scaling factors are derived. The proposed adaptation laws for the output scaling factors are simple and need a little knowledge about the structure of the power system. To demonstrate the efficiency of the proposed adaptive T2FPID controller, a comparison to the non-adaptive T2FPID and type-1 FPID (T1FPID) controllers is given for a two-area interconnected nonlinear power system with input time-delays. The obtained simulation results prove that the performance of the proposed T2FPID controller is superior compared to the other controllers in the presence of time-varying input delays, nonlinearities, and uncertainties.

Convex synthesis for optimal ℋ∞ consensus of input-delayed multi-agent systems via dynamic relative output feedback

In this paper, we address the leaderless consensus control problem for a class of linear multi-agent systems (MASs) subject to nonlinear time-varying input delays and unknown external disturbances. The MAS is considered to be non-introspective, meaning that each individual agent is not able to measure the absolute plant state/output of its own but can sense relative information from its neighbours. A novel dynamic relative output-feedback (DROF) controller structure is proposed by leveraging the integral quadratic constraints (IQCs) from robust control theory. This new DROF controller is compelling in the sense that it utilises not only the relative plant outputs, but also the relative controller states and relative IQC-induced dynamics states for distributed feedback control. Based on this, the associated consensus control synthesis conditions that guarantee optimal disturbance attenuation performance in the presence of time-varying input delays are derived and formulated as linear matrix inequalities (LMIs), which can be solved efficiently via convex optimisation. Effectiveness of the proposed theoretical results is demonstrated through simulation studies.