In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.

This paper is concerned with the target tracking problem over a filtering network with dynamic cluster and data fusion. A novel distributed consensus-based adaptive Kalman estimation is developed to track a linear moving target. Both optimal filtering gain and average disagreement of the estimates are considered in the filter design. In order to estimate the states of the target more precisely, an optimal Kalman gain is obtained by minimizing the mean-squared estimation error. An adaptive consensus factor is employed to adjust the optimal gain as well as to acquire a better filtering performance. In the filter's information exchange, dynamic cluster selection and two-stage hierarchical fusion structure are employed to get more accurate estimation. At the first stage, every sensor collects information from its neighbors and runs the Kalman estimation algorithm to obtain a local estimate of system states. At the second stage, each local sensor sends its estimate to the cluster head to get a fused estimation. Finally, an illustrative example is presented to validate the effectiveness of the proposed scheme.

In this paper, the event-triggered asynchronous control problem of nonlinear multi-agent systems is investigated based on T-S fuzzy models. In order to rationally utilize network resources and elaborately avoid unnecessary continuous monitoring, an event-triggered mechanism with a new dynamic threshold parameter and a sampler are considered, respectively. Besides, an asynchronous operation method is adopted to deal with the mismatched premise variables between the fuzzy system and fuzzy controller. Based on the Lyapunov stability theory and appropriate inequality, some sufficient criteria in the form of linear matrix inequalities are obtained to ensure the stability of the closed-loop system. Finally, an illustrative example is provided to demonstrate the effectiveness and superiority of the proposed method.

The H∞ output tracking control problem of the networked control systems (NCSs) under an adaptively adjusted event-triggered scheme is investigated in this paper. Firstly, a novel adaptively adjusted event-triggered scheme developed in the NCSs with stochastic sensor faults is proposed to choose the necessary packets of sampled data to be transmitted through the networks. Then, the considered system is described as a time-delay system with a delay-distribution for investigation. Based on the established model, a novel stability criterion and the state-feedback controller design with a desired performance of H∞ output tracking control for the systems are both derived by using Lyapunov functional. An algorithm is also presented to explain the process of adaptively adjusted event-triggered scheme method. Finally, a satellite tracking case is provided to demonstrate the effectiveness of the proposed approach.

This paper addresses the distributed adaptive event-triggered H∞ filtering problem for a class of sector-bounded nonlinear system over a filtering network with time-varying and switching topology. Both topology switching and adaptive event-triggered mechanisms (AETMs) between filters are simultaneously considered in the filtering network design. The communication topology evolves over time, which is assumed to be subject to a nonhomogeneous Markov chain. In consideration of the limited network bandwidth, AETMs have been used in the information transmission from the sensor to the filter as well as the information exchange among filters. The proposed AETM is characterized by introducing the dynamic threshold parameter, which provides benefits in data scheduling. Moreover, the gain of the correction term in the adaptive rule varies directly with the estimation error and inversely with the transmission error. The switching filtering network is modeled by a Markov jump nonlinear system. The stochastic Markov stability theory and linear matrix inequality techniques are exploited to establish the existence of the filtering network and further derive the filter parameters. A co-design algorithm for determining H∞ filters and the event parameters is developed. Finally, some simulation results on a continuous stirred tank reactor and a numerical example are presented to show the applicability of the obtained results.

This paper deals with the sliding mode estimation for microgrid with time delays. Delay has a great impact on large power grids’ (LPG) management for microgrid, which terribly reduces the stability and quality of microgrid. Random delay caused by load dependent congestion, constant transmission delay and constant delay in microgrid are considered in this paper. To eliminate the adverse effects of delays, a novel sliding mode estimation-based controller is designed to predict time delays and microgrid states, and to reject the disturbance of estimation errors. The mathematical inverter model containing electrical characteristics is regarded as the model of practical microgrid system. Delay estimation with learning parameter and state estimation are derived according to the inverter model. By regarding estimation errors as disturbance of sliding mode control (SMC), the control signal of SMC is adaptively changed in the sliding mode estimation-based control loop to ensure the stability of system and accuracy of estimation. Exponential reaching law (ERL) is implemented to improve the chattering issues and reaching performance of SMC. Lyapunov approach is exploited to analyze the stability of sliding motion. Finally, the proposed SMC strategy is validated by simulation experiments of a microgrid with time delays.