Robust-Neural Observer Design for Discrete-Time Uncertain Non-Affine Nonlinear System

This paper proposed a new Nonlinear Discrete-Time Robust-Neural Observer (DTRNO) which capable to give estimation for the states of Discrete-Time Uncertain Non-affine Non-linear Systems in presence of external disturbances. The Neural network is a kind of discrete-time Multi Layered Perceptron (MLP) which Trained with an Extended Kalman-Filter (EKF) based algorithm, which this neural observer is robust in presence of external and internal uncertainties, using a parallel configuration.This work includes the stability proof of the estimation error on the basis of the Lyapunov approach, and for demonstrate observer performance an Uncertain Non-affine Nonlinear Systems have been simulated to formulations validate the theoretical. Simulation results confirm the proficiency of the DTRNO even at the different operating conditions and presence of parameters uncertainties.DOI:http://dx.doi.org/10.11591/ijece.v4i4.617

Analysis, filtering, and control for Takagi-Sugeno fuzzy models in networked systems

Copyright © 2015 Sunjie Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.The fuzzy logic theory has been proven to be effective in dealing with various nonlinear systems and has a great success in industry applications. Among different kinds of models for fuzzy systems, the so-called Takagi-Sugeno (T-S) fuzzy model has been quite popular due to its convenient and simple dynamic structure as well as its capability of approximating any smooth nonlinear function to any specified accuracy within any compact set. In terms of such a model, the performance analysis and the design of controllers and filters play important roles in the research of fuzzy systems. In this paper, we aim to survey some recent advances on the T-S fuzzy control and filtering problems with various network-induced phenomena. The network-induced phenomena under consideration mainly include communication delays, packet dropouts, signal quantization, and randomly occurring uncertainties (ROUs). With such network-induced phenomena, the developments on T-S fuzzy control and filtering issues are reviewed in detail. In addition, some latest results on this topic are highlighted. In the end, conclusions are drawn and some possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009, 61329301, 11301118 and 61174136, the Natural Science Foundation of Jiangsu Province of China under Grant BK20130017, the Fundamental Research Funds for the Central Universities of China under Grant CUSF-DH-D-2013061, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Output Consensus Control for Heterogeneous Multi-Agent Systems

We study distributed output feedback control of a heterogeneous multi-agent system (MAS), consisting of N different continuous-time linear dynamical systems. For achieving output consensus, a virtual reference model is assumed to generate the desired trajectory for which the MAS is required to track and synchronize. A full information (FI) protocol is assumed for consensus control. This protocol includes information exchange with the feed-forward signals. In this dissertation we study two different kinds of consensus problems. First, we study the consensus control over the topology involving time delays and prove that consensus is independent of delay lengths. Second, we study the consensus under communication constraints. In contrast to the existing work, the reference trajectory is transmitted to only one or a few agents and no local reference models are employed in the feedback controllers thereby eliminating synchronization of the local reference models. Both significantly lower the communication overhead. In addition, our study is focused on the case when the available output measurements contain only relative information from the neighboring agents and reference signal. Conditions are derived for the existence of distributed output feedback control protocols, and solutions are proposed to synthesize the stabilizing and consensus control protocol over a given connected digraph. It is shown that the H-inf loop shaping and LQG/LTR techniques from robust control can be directly applied to design the consensus output feedback control protocol. The results in this dissertation complement the existing ones, and are illustrated by a numerical example. The MAS approach developed in this dissertation is then applied to the development of autonomous aircraft traffic control system. The development of such systems have already started to replace the current clearance-based operations to trajectory based operations. Such systems will help to reduce human errors, increase efficiency, provide safe flight path, and improve the performance of the future flight

Disturbance Model Identification and Model Free Synthesis of Controllers for Multivariable Systems

In this work, two different problems are addressed. In the first part, the problem of synthesizing a set of stabilizing controllers for unknown multivariable systems using direct data is analyzed. This is a model free approach to control design and uses only the frequency domain data of the system. It is a perfect complement to modern and post modern methods that begin the control design with a system model. A three step method, involving sequential design, search for stability boundaries and stability check is proposed. It is shown through examples that a complete set of stabilizing controllers of the chosen form can be obtained for the class of linear stable multivariable systems. The complexity of the proposed method is invariant with respect to the order of the system and increases with the increase in the number of input channels of the given multivariable system. The second part of the work deals with the problem of identification of model uncertainties and the effect of unwanted exogenous inputs acting on a discrete time multivariable system using its output information. A disturbance model is introduced which accounts for the system model uncertainties and the effect of unwanted exogenous inputs acting on the system. The frequency content of the exogenous signals is assumed to be known. A linear dynamical model of the disturbance is assumed with an input that has the same frequency content as that of the exogenous input signal. The extended model of the system is then subjected to Kalman filtering and the disturbance states estimates are used to obtain a least squares estimate of the disturbance model parameters. The proposed approach is applied to a linear multivariable system perturbed by an exogenous signal of known frequency content and the results obtained depict the efficacy of the proposed approach

Neural networks-based command filtering control for a table-mount experimental helicopter

This paper presents neural networks based on command filtering control method for a table-mount experimental helicopter which has three rotational degrees-of-freedom. First, the controller is designed based on backstepping technique, and further command filtering technique is used to solve the derivative of the virtual control, thereby avoiding the effects of signal noise. Secondly, the model uncertainty of the table-mount experimental helicopter's system is estimated by using neural networks. And then, Lyapunov stabilization analysis proves the stability of the table-mount experimental helicopter closedloop attitude tracking system. Finally, the experiment is carried out to clarify the effectiveness of the proposed method. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved

Practical Solutions to the Non-Minimum Phase and Vibration Problems Under the Disturbance Rejection Paradigm

This dissertation tackles two kinds of control problems under the disturbance rejection paradigm (DRP): 1) the general problem of non-minimum phase (NMP) systems, such as systems with right half plane (RHP) zeros and those with time delay 2) the specific problem of vibration, a prevailing problem facing practicing engineers in the real world of industrial control. It is shown that the DRP brings to the table a refreshingly novel way of thinking in tackling the persistently challenging problems in control. In particular, the problem of NMP has confounded researchers for decades in trying to find a satisfactory solution that is both rigorous and practical. The active disturbance rejection control (ADRC), originated from DRP, provides a potential solution. Even more intriguingly, the DRP provides a new framework to tackle the ubiquitous problem of vibration, whether it is found in the resonant modes in industrial motion control with compliant load, which is almost always the case, or in the microphonics of superconducting radio frequency (SRF) cavities in high energy particle accelerators. That is, whether the vibration is caused by the environment or by the characteristics of process dynamics, DRP provides a single framework under which the problem is better understood and resolved. New solutions are tested and validated in both simulations and experiments, demonstrating the superiority of the new design over the previous ones. For systems with time delay, the stability characteristic of the proposed solution is analyze

Practical Solutions to the Non-Minimum Phase and Vibration Problems Under the Disturbance Rejection Paradigm

This dissertation tackles two kinds of control problems under the disturbance rejection paradigm (DRP): 1) the general problem of non-minimum phase (NMP) systems, such as systems with right half plane (RHP) zeros and those with time delay 2) the specific problem of vibration, a prevailing problem facing practicing engineers in the real world of industrial control. It is shown that the DRP brings to the table a refreshingly novel way of thinking in tackling the persistently challenging problems in control. In particular, the problem of NMP has confounded researchers for decades in trying to find a satisfactory solution that is both rigorous and practical. The active disturbance rejection control (ADRC), originated from DRP, provides a potential solution. Even more intriguingly, the DRP provides a new framework to tackle the ubiquitous problem of vibration, whether it is found in the resonant modes in industrial motion control with compliant load, which is almost always the case, or in the microphonics of superconducting radio frequency (SRF) cavities in high energy particle accelerators. That is, whether the vibration is caused by the environment or by the characteristics of process dynamics, DRP provides a single framework under which the problem is better understood and resolved. New solutions are tested and validated in both simulations and experiments, demonstrating the superiority of the new design over the previous ones. For systems with time delay, the stability characteristic of the proposed solution is analyze

State and Parameter Estimation for a Class of Nonlinearly Parameterized Systems Using Sliding Mode Techniques

In this study, a class of nonlinear parameterized systems is considered where the unknown parameters are parameterized nonlinearly. A stability criteria for time-varying systems is developed based on Perron-Frobenius theorem, and used for designing observers. A particular sliding mode observer with an update law, which can ensure that the sliding motion converges to zero asymptotically, is designed to estimate states and unknown parameters. The developed result is applied to a three-phase inverter system used by China high-speed trains to verify the effectiveness