Event‐based adaptive sliding mode control for Euler–Lagrange systems with parameter uncertainties and external disturbances

Abstract

This article studies the robust tracking control problem for a class of uncertain Euler-Lagrange systems. An adaptive sliding mode control strategy is proposed to address the robust stability of the closed-loop systems with external disturbances and parameter uncertainties. An adaptive parameter estimator is constructed in the sensor node to deal with the unknown bound of uncertainties, and an event-triggering detector is located between the sensor node and the controller node. The introduction of event-triggering condition is introduced, reducing the utilization on communication resources and the number of control execution. In this event-triggered strategy, the closed-loop systems reach the practical sliding mode band and the band could be adjusted. Moreover, the event-triggering condition can make sure that Zeno behavior does not occur. Finally, simulation results on 2-link robotic manipulator are provided to illustrate the effectiveness and feasibility of the proposed control scheme.

Adaptive extreme learning machine‐based event‐triggered control for perturbed Euler–Lagrange systems

“…The tradeoff of system performance, control inputs, and communication/actuation resource can be clearly formulated by the designed event-triggered control method in this paper. From the form of the event triggering condition (22) and system state convergence boundary (33), it can be concluded that the parameters affecting the system performance indicators are 𝜆, 𝜂, and 𝜖. The bigger 𝜆 and the smaller 𝜂 and 𝜖 can result in fewer triggering times, which leads to higher the communication resource saving rates and smaller state convergence boundaries.…”

“…In other words, the event‐triggered control approaches can balance the control system performance and the usage of actuation and communication resources. Recently, the designs of trajectory tracking control under event‐triggered framework have been explored by a quality of works for Euler–Lagrange systems 33‐38 . In References 33‐35, adaptive technique combined by neural network‐based sliding mode control techniques was developed to construct event‐triggered control strategies for dealing with trajectory tracking control problems of the uncertain Euler–Lagrange system.…”

“…Recently, the designs of trajectory tracking control under event‐triggered framework have been explored by a quality of works for Euler–Lagrange systems 33‐38 . In References 33‐35, adaptive technique combined by neural network‐based sliding mode control techniques was developed to construct event‐triggered control strategies for dealing with trajectory tracking control problems of the uncertain Euler–Lagrange system. The designs in References 36‐38 carried out distributed event‐triggered control approaches for multiple Euler–Lagrange systems to suppress the impacts of state/input saturation, parametric uncertainties, lacking relative velocity measurements, respectively.…”

“…(2) It is obvious that there are some restrictive relationships among system performance, control inputs, and communication/actuation resource by utilizing event‐triggered control methods. This paper clearly formulates the relationships and adjusts the key parameters to obtain acceptable control inputs for the Euler–Lagrange system, which has not been investigated by the existing event‐triggered control approaches in Reference 33‐39. (3) The ELM network used in this paper has the advantages of randomly selecting for input weights and biases.…”

Target Enclosing and Coverage Control for Quadrotors with Constraints and Time-Varying Delays: A Neural Adaptive Fault-Tolerant Formation Control Approach

“…In addition, wind disturbance has a significant impact on the movement of small UAVs in the real world, particularly when the modeling is inaccurate. To circumvent this issue, the mainstream techniques typically include neural networks estimators [ 35 , 36 , 37 ], nonlinear observers [ 38 , 39 ] and adaptive estimators [ 40 , 41 ], etc. On the basis of the aforementioned factors, we neutralize the effect of disturbances, uncertainties and time-varying communication delays and achieve precise control of time-varying formations.…”