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In this paper, an event-triggered dynamic surface controller is proposed for strict-feedback nonlinear networked systems with mismatched uncertainties. Towards this aim and first of all, an event-based dynamic surface control law is designed. Then, the closed-loop error dynamics is derived and rewritten as a linear dynamics with nonlinear perturbation terms. Next, these terms are classified into vanishing and nonvanishing perturbations. Finally, by imposing some constraints on the nonvanishing terms, an event-triggering mechanism is constructed. By formulating the imposed constraints in the form of linear matrix inequalities (LMIs) and using the ultimate boundedness theory, the ultimate error boundedness and quadratic tracking objectives are addressed in the framework of convex optimisation. The absence of the Zeno behaviour, which is an important feature of any event-triggered methodology, is also proven. The validity of the theoretical results is illustrated by applying the proposed method to a remotely operated underwater vehicle in a simulation study as well as an experimental implementation on a magnetic levitation system. [ABSTRACT FROM AUTHOR] more...

In this paper, the consensus problem in linear multi‐agent systems with uncertain topology is investigated. The uncertainty in the communication topology is represented by granular fuzzy numbers obtained from expert knowledge, and the required calculations are performed with multidimensional relative–distance–measurement interval arithmetic. The concept of granular fuzzy graph and fuzzy Gershgorin disc theorem is introduced. Then a fuzzy consensus protocol is proposed for the multi‐agent system with fuzzy granular uncertain topology. A two‐step algorithm is also proposed to build the consensus protocol. We also propose a theorem for calculating the final value of fuzzy consensus, which shows the final value of consensus as a fuzzy number. Simulation results show the efficiency of the suggested algorithm. [ABSTRACT FROM AUTHOR] more...

In this paper, an event‐triggered dynamic surface control technique is proposed for strict‐feedback uncertain nonlinear networked systems in the presence of network‐induced time delays. First, an event‐based dynamic surface control law is designed. Then, by rewriting the control law as a communication‐delay‐distribution‐dependent model, the closed‐loop error dynamics is derived and modeled as a linear time‐delay system with perturbation terms. Next, a periodic event‐triggering condition is designed by imposing some inequality constraints on the perturbations. A theorem related to the H∞$$ {H}_{\infty } $$ ultimate boundedness and quadratic tracking of the closed‐loop system is presented. The proposed technique is utilized to design a decentralized dynamic surface control structure with event‐triggered inter‐vehicle communications for a vehicle platoon. [ABSTRACT FROM AUTHOR] more...

This paper addresses L 2 -gain analysis and robust H ∞ control of nonlinear sampled-data systems described by a polynomial linear parameter varying (PLPV) model. First, conditions for L 2 -gain analysis of sampled-data PLPV systems are derived using a modified Krasovskii functional in which the distance between the real and measured parameters are considered as uncertainties. The resulting conditions are formulated in terms of parameterised linear matrix inequalities (PLMI). Second, a solution approach for satisfying PLMI conditions over the set of whole parameters and uncertainties is proposed using the sum-of-squares decomposition method. Third, conditions for robust H ∞ sampled-data controller synthesis for nonlinear systems described by PLPV models are derived in terms of parameterised bilinear matrix inequalities (PBMI) with the maximum allowable sampling period as the parameter. To make the resulting bilinear matrix inequality (BMI) problems tractable, an iterative algorithm is developed and utilised to solve the problem. Finally, the effectiveness of the proposed approach is verified using several real-world examples. [ABSTRACT FROM AUTHOR] more...

Abstract: In this paper we investigate memory control of stochastic linear delayed systems with both additive and multiplicative noises. A new formula is first presented to obtain the prediction vector from the system dynamics and then it is used for feedback to reduce the input delay in the original delayed system. To ensure the stability of closed‐loop system, some matrix inequality conditions are given that in the case of feasibility provide the stabilizing gain of the predictor controller. The proposed method is applied to stochastic quarter‐car model of an active suspension system to show the effectiveness of the approach. [ABSTRACT FROM AUTHOR] more...

This study investigates the prediction-based (dynamic) stabilization of linear systems with input delay in the presence of external disturbances and multiplicative noise modelled as Itô type stochastic differential equations. Conventional memory-less (static) controllers are widely used for the stabilization of both deterministic and stochastic delayed systems. However, using these methods the upper bound for delay is strongly restricted. Motivated by acceptable performances of dynamic controllers for deterministic delayed systems, the extension of these methods for stochastic delayed systems is considered in this paper. The structure of the dynamic controller for stabilization of stochastic delayed systems is firstly derived utilizing the prediction vector. Then two sufficient conditions are given in the form of linear matrix inequalities that in the case of feasibility provide the stabilizing gain of the controller. Finally, simulation results are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR] more...