Actuator and sensor fault isolation in a class of nonlinear dynamical systems

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault propagation.This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems.Actuator and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be determined.The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.

Rough-Granular Computing

Solving complex problems by multi-agent systems in distributed environments requires new approximate reasoning methods based on new computing paradigms. One such recently emerging computing paradigm is Granular Computing(GC). We discuss the Rough-Granular Computing(RGC) approach to modeling of computations in complex adaptive systems and multiagent systems as well as for approximate reasoning about the behavior of such systems. The RGC methods have been successfully applied for solving complex problems in areas such as identification of objects or behavioral patterns by autonomous systems, web mining, and sensor fusion.

Multilevel Characteristic Basis Function Method with ACA for Accelerated Solution of Electrically Large Scattering Problems

The multilevel characteristic basis function method(MLCBFM)with the adaptive cross approximation(ACA)algorithm for accelerated solution of electrically large scattering problems is studied in this paper.In the conventional MLCBFM based on Foldy-Lax multiple scattering equations,the improvement is only made in the generation of characteristic basis functions(CBFs).However,it does not provide a change in impedance matrix filling and reducing matrix calculation procedure,which is time-consuming.In reality,all the impedance and reduced matrix of each level of the MLCBFM have low-rank property and can be calculated efficiently.Therefore,ACA is used for the efficient generation of two-level CBFs and the fast calculation of reduced matrix in this study.Numerical results are given to demonstrate the accuracy and efficiency of the method.

Adaptive dynamic programming for online solution of a zero-sum differential game

This paper will present an approximate/adaptive dynamic programming(ADP) algorithm,that uses the idea of integral reinforcement learning(IRL),to determine online the Nash equilibrium solution for the two-player zerosum differential game with linear dynamics and infinite horizon quadratic cost.The algorithm is built around an iterative method that has been developed in the control engineering community for solving the continuous-time game algebraic Riccati equation(CT-GARE),which underlies the game problem.We here show how the ADP techniques will enhance the capabilities of the offline method allowing an online solution without the requirement of complete knowledge of the system dynamics.The feasibility of the ADP scheme is demonstrated in simulation for a power system control application.The adaptation goal is the best control policy that will face in an optimal manner the highest load disturbance.

Sparse Deep Neural Network for Nonlinear Partial Differential Equations

More competent learning models are demanded for data processing due to increasingly greater amounts of data available in applications.Data that we encounter often have certain embedded sparsity structures.That is,if they are represented in an appropriate basis,their energies can concentrate on a small number of basis functions.This paper is devoted to a numerical study of adaptive approximation of solutions of nonlinear partial differential equations whose solutions may have singularities,by deep neural networks(DNNs)with a sparse regularization with multiple parameters.Noting that DNNs have an intrinsic multi-scale structure which is favorable for adaptive representation of functions,by employing a penalty with multiple parameters,we develop DNNs with a multi-scale sparse regularization(SDNN)for effectively representing functions having certain singularities.We then apply the proposed SDNN to numerical solutions of the Burgers equation and the Schrödinger equation.Numerical examples confirm that solutions generated by the proposed SDNN are sparse and accurate.

Backstepping sliding mode tracking control of quad-rotor under input saturation

Purpose–The quad-rotor is an under-actuation,strong coupled nonlinear system with parameters uncertainty,unmodeled disturbance and drive capability boundedness.The purpose of the paper is to design a flight control system to regulate the aircraft track the desired trajectory and keep the attitude angles stable on account of these issues.Design/methodology/approach–Considering the dynamics of a quad-rotor,the closed-loop flight control system is divided into two nested loops:the translational outer-loop and the attitude inner-loop.In the outer-loop,the translational controller,which exports the desired attitude angles to the inner-loop,is designed based on bounded control technique.In consideration of the influence of uncertain rotational inertia and external disturbance,the backstepping sliding mode approach with adaptive gains is used in the inner-loop.The switching control strategy based on the sign functions of sliding surface is introduced into the design procedure with respect to the input saturation.Findings–The validity of the proposed flight control system was verified through numerical simulation and prototype flight experiment in this paper.Furthermore,with relation to the flying,the motor speed is kept in the predetermined scope.Originality/value–This article introduces a new flight control system designed for a quad-rotor.

Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow

In this work,we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck(TFP)equations.It is well known that solutions of such equations are probability density functions,and thus our approach relies on modelling the target solutions with the temporal normalizing flows.The temporal normalizing flow is then trained based on the TFP loss function,without requiring any labeled data.Being a machine learning scheme,the proposed approach is mesh-free and can be easily applied to high dimensional problems.We present a variety of test problems to show the effectiveness of the learning approach.

Computational simulation methods for fiber reinforced composites

Trefftz-finite element method(Trefftz-FEM),adaptive cross approximation BEM(ACA BEM)and continuous source function method(CSFM)are used for the simulation of composites reinforced by short fibers(CRSF)with the aim of showing the possibilities of reducing the problem of complicated and important interactions in such composite materials.