Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.

METHODS FOR IDENTIFYING SUB-REGIONAL MATERIAL PARAMETERS OF CONCRETE DAMS USING MODAL DATA

Methods for identifying sub-regional material parameters of concrete damsusing incomplete rnodal data are presented. With the measurements of the first frequency andincomplete mode shape, identification methods were built by both the output error approach and theminimum deviation approach. The minimum deviation approach was introduced as physical constraints tothe output error approach, allowing the output error-minimum deviation coupled approach to bedeveloped. The simulated annealing-simplex shape algorithm was applied to solve the identificationmodels. Numerical simulations were carried out with noisy incomplete measurements to illustrate therobustness of the methods.

Fixed-Time Controller Design for a Quadrotor UAV with Asymmetric Output Error Constraints

In this article,a fixed-time tracking control strategy is proposed for a quadrotor UAV(QUAV)with external disturbance and asymmetric output error constraints.Firstly,a dynamic model of the QUAV is transformed into a strict feedback system with external disturbance,and it is decoupled into attitude subsystem and position subsystem for simplifying controller design.Secondly,an asymmetric tangent barrier Lyapunov function(ATBLF)is applied to solve the tracking error constraints problem,and a fixed-time control law is designed.Meanwhile,a fixed-time disturbance observer(FTDO)is designed to cope with external disturbance.Then,it is proved that the designed controller guarantees the tracking error remains within the constraint ranges and converges to zero in fixed-time by Lyapunov stability theory.Finally,the effectiveness of the proposed control scheme is verified by numerical simulations.

Quasi-synchronous control of uncertain multiple electrohydraulic systems with prescribed performance constraint and input saturation

This article focuses on the high accuracy quasi-synchronous control issue of multiple electrohydraulic systems(MEHS).In order to overcome the negative effects of parameter uncertainty and external load interference of MEHS,a kind of finite-time disturbance observer(FTDO)via terminal sliding mode method is constructed based on the MEHS model to achieve fast and accuracy estimation and compensation ability.To avoid the differential explosion in backstepping iteration,the dynamic surface control is used in this paper to guarantee the follower electrohydraulic nodes synchronize to the leader motion with a better performance.Furthermore,a timevarying barrier Lyapunov function(tvBLF)is adopted during the controller design process to constraint the output tracking error of MEHS in a prescribed performance with time-varying exponential function.As the initial state condition is relax by tvBLF,the input saturation law is also adopted during the controller design process in this paper to restrain the surges of input signals,which can avoid the circuit and mechanical structure damage caused by the volatile input signal.An MEHS experimental bench is constructed to verify the effectiveness of the theoretical conclusions proposed in this paper and the advantages of the proposed conclusions in this paper are illustrated by a series of contradistinctive experimental results.

N4SID and MOESP Algorithms to Highlight the Ill-conditioning into Subspace Identification

In this paper,an analysis for ill conditioning problem in subspace identifcation method is provided.The subspace identifcation technique presents a satisfactory robustness in the parameter estimation of process model which performs control.As a frst step,the main geometric and mathematical tools used in subspace identifcation are briefly presented.In the second step,the problem of analyzing ill-conditioning matrices in the subspace identifcation method is considered.To illustrate this situation,a simulation study of an example is introduced to show the ill-conditioning in subspace identifcation.Algorithms numerical subspace state space system identifcation(N4SID)and multivariable output error state space model identifcation(MOESP)are considered to study,the parameters estimation while using the induction motor model,in simulation(Matlab environment).Finally,we show the inadequacy of the oblique projection and validate the efectiveness of the orthogonal projection approach which is needed in ill-conditioning;a real application dealing with induction motor parameters estimation has been experimented.The obtained results proved that the algorithm based on orthogonal projection MOESP,overcomes the situation of ill-conditioning in the Hankel s block,and thereby improving the estimation of parameters.