Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Adaptive NN Control for Multisteering Plane Aircraft with Dead Zone or Backlash Input Nonlinearity

Considering that many factors such as actuator input dead zone, backlash, and external disturbance could affect the exactness of trajectory tracking, therewith a robust adaptive neural network control scheme on the basis of control allocation is proposed for the sake of tracking control of multisteering plane aircraft with actuator input dead zone or backlash nonlinearity. First of all, an actuator input dead zone or backlash nonlinearity control assignment model is established and the control allocation equation is derived. Secondly, the system nonlinear uncertainty is compensated by means of radial basis function neural network, and a robust term is introduced to achieve robustness against external disturbance and system errors. Finally, by utilizing Lyapunov stability theorem, it has been proved that all the signals in the closed-loop system are bounded, and the tracking error converges to a small residual set asymptotically. Simulation results on ICE101 multisteering plane aircraft demonstrate the outstanding tracking performance and strong robustness as well as effectiveness of the proposed approach, which can effectively overcome the adverse influence of dead zone, backlash nonlinearity, and external disturbance on the system

Observer-based event-triggered and set-theoretic neuro-adaptive controls for constrained uncertain systems

In this study, several new observer-based event-triggered and set-theoretic control schemes are presented to advance the state of the art in neuro-adaptive controls. In the first part, six new event-triggered neuro-adaptive control (ETNAC) schemes are presented for uncertain linear systems. These comprehensive designs offer flexibility to choose a design depending upon system performance requirements. Stability proofs for each scheme are presented and their performance is analyzed using benchmark examples. In the second part, the scope of the ETNAC is extended to uncertain nonlinear systems. It is applied to a case of precision formation flight of the microsatellites at the Sun-Earth/Moon L1 libration point. This dynamic system is selected to evaluate the performance of the ETNAC techniques in a setting that is highly nonlinear and chaotic in nature. Moreover, factors like restricted controls, response to uncertainties and jittering makes the controller design even trickier for maintaining a tight formation precision. Lyapunov function-based stability analysis and numerical results are presented. Note that most real-world systems involve constraints due to hardware limitations, disturbances, uncertainties, nonlinearities, and cannot always be efficiently controlled by using linearized models. To address all these issues simultaneously, a barrier Lyapunov function-based control architecture called the segregated prescribed performance guaranteeing neuro-adaptive control is developed and tested for the constrained uncertain nonlinear systems, in the third part. It guarantees strict performance that can be independently prescribed for each individual state and/or error signal of the given system. Furthermore, the proposed technique can identify unknown dynamics/uncertainties online and provides a way to regulate the control input --Abstract, page iv