Recursive Identification Algorithm Research Articles

System identification in presence of undetectable packet losses in the actuation path

Identification of networked systems over communication links with packet losses is considered. It is assumed that there is no connection from the actuators to the system identifier such that no information is available on loss of data packets carrying the actuation signals. While the packet loss occurrences in the sensor links are naturally detectable by the system identifier as the receiving node, the packet loss occurrences in the actuation paths are not directly detectable. The existing system identification methods fail to be applicable without the information on packet loss occurrences. Presenting a solution for this issue is the objective of the present work. Two recursive algorithms are proposed to solve the problem in real time. For this purpose, ARMAX input–output representations of the system with Markovian jumping modes are developed. Then, filters are designed for estimation of the packet loss occurrences. The estimated mode is used in the recursive algorithms for identification of the whole system based on the maximum likelihood approach. A main algorithm and its simplified version are proposed that are capable of finding the various kinds of system parameters including the physical plant parameters and the communication mode transition probabilities. Effectiveness of the algorithms is verified through simulation examples where the system performance is evaluated and compared.

Variable recursive least square algorithm for online battery equivalent circuit model parameters identification for electric vehicles

Variable recursive least square algorithm for online battery equivalent circuit model parameters identification for electric vehicles

Decomposition and composition modeling algorithms for control systems with colored noises

SummaryThis article proposes a novel identification framework for estimating the parameters of the controlled autoregressive autoregressive moving average (CARARMA) models with colored noise. By means of building an auxiliary model and using the hierarchical identification principle, this article investigates a highly‐efficient parameter estimation algorithm. In order to meet the need for identifying the systems with large‐scale parameters, the whole parameters of the CARARMA system is separated into two parameter sets and a decomposition and composition recursive identification algorithm (i.e., hierarchical generalized extended least squares algorithm or decomposition‐based recursive generalized extended least squares algorithm) is presented. Moreover, this article analyzes the convergence of the proposed decomposition and composition recursive identification algorithm. The performance analysis shows that the proposed decomposition and composition identification algorithm can reduce the complexity of the identification algorithm compared with the algorithm without decomposition.

Pressure control for pneumatic pressing roller in automated fiber placement

In automated fiber placement (AFP), the contact pressure between the roller and the mold is an important process parameter. Too small or too large contact pressure will result in unacceptable laying defects. This paper focuses on the control method of contact pressure in AFP. Firstly, the dynamic model of the pressing foot mechanism is established, and a mathematical relationship of input air pressure and output force is developed. Then, a self-tuning PI control algorithm is used to implement servo control of the roller output force based on the Slowly Time-Varying Recursive Identification Algorithm. Also, using Abaqus/CAE, the mapping relationship between the contact area and measurements from displacement sensors is established to predict the contact area in real time. Finally, the contact pressure control method is established by combining the servo control method of contact force and the prediction method of contact area. Experimental results show that the constant pressure control method can effectively stabilize the contact pressure, with a pressure fluctuation of less than 4% under the experimental conditions set in this paper.

Prescribed-time Tracking Control for Robotic Manipulator with Time-varying Output Constraints

This paper introduces prescribed-time tracking control of nonlinear systems for robotic manipulator under time-varying output constraints (TVOC). For the robotic manipulator driven by DC motor, we will design a controller to realize: one is to realize prescribed-time tracking control; the other is no violation of the time-varying output constraints (TVOC). First, sufficient conditions for prescribed-time tracking control for nonlinear systems with TVOC are presented. Secondly, relying on the backstepping technique, the recursive design algorithm is applied to construct the controller, which then realizes the prescribed-time tracking without the violation of TVOC. Finally, the applicability of this method in robot manipulator is verified by simulation.

Performance of Recursive Least Squares Algorithm Configurations for Online Parameter Identification of Induction Machines in an Automotive Environment

The recursive least squares algorithm for online parameter identification of induction machines has a high potential to serve as a basis for an innovative electric vehicle diagnosis concept. Commonly used for control parameter tuning, this approach is established in numerous industrial applications. However, in the automotive environment, special machine designs are used and highly dynamic operation takes place in a wide speed and load range. This results in fast transient parameter behavior, which is challenging in terms of online parameter identification. Therefore, the algorithm performance must be rated in our field of application with suitable dynamic test profiles. In this work, we compare several algorithm extensions to a novel recursive least squares algorithm with multiple variable forgetting factors for induction machines. The algorithms are analyzed regarding their handling of the associated transient parameter behavior. In addition, different identification model structures are considered to deal with the dynamic speed operation and the associated transient iron losses. Special attention is given to the real-time performance of the overall identification algorithms as this is a major requirement for implementation in automotive embedded systems. For validation, both, simulation and experimental results are presented, and associated configuration recommendations are provided.

A Hybrid Strategy Enhanced Sub-Sampled Recursive Second Order Algorithm for Chemical Process Intelligent Identification

The accurate dynamic model of the chemical process is an important condition for the successful implementation of advanced control in the plant. In this paper, an efficient second order algorithm for long short-term memory (LSTM) network training is proposed for chemical process intelligent identification. A novel Hessian inverse recursion method is adopted to achieve fast convergence and avoid the high-cost operation of the classic second order optimization method. Besides, more information is back propagated since the proposed method retains the real curvature information of the neural network. Considering the large amount of chemical process data, a sub-sampled recursive second order-stochastic gradient descent (SRSO-SGD) algorithm which uses sub-sampling method and hybrid strategy is proposed. The identification experiment on a delayed coker fractionator shows that the proposed sub-sampled neural network second order training algorithm has better performance than other learning algorithms in terms of model identification accuracy and convergence speed. By adopting a hybrid strategy that performing Hessian inverse estimation every 3 training epochs, the expensive Hessian inverse calculation cost in the identification process is further reduced while low training and testing errors are maintained.

OPTIMAL RECURSIVE IDENTIFICATION TECHNIQUES FROM QUANTIZED OUTPUTS

Recursive identification algorithms for the parameter estimation of linear systems from multilevel quantized outputs are introduced in this paper. The proposed algorithms are proved to be optimal in the sense that they minimize the a posteriori parameter estimation error covariance matrix. Numerical simulations are carried out to illustrate their performance for different quantization steps and Signal-to-Noise Ratios, as well as their capability to track time varying parameters.

Adaptive parameter estimation for the expanded sandwich model

An expanded-sandwich system is a nonlinear extended block-oriented system in which memoryless elements in conventional block-oriented systems are displaced by memory submodels. Expanded-sandwich system identification has received extensive attention in recent years due to the powerful ability of these systems to describe actual industrial systems. This study proposes a novel recursive identification algorithm for an expanded-sandwich system, in which an estimator is developed on the basis of parameter identification error data rather than the traditional prediction error output information. In this scheme, a filter is introduced to extract the available system information based on miserly structure layout, and some intermediate variables are designed using filtered vectors. According to the developed intermediate variables, the parameter identification error data can be obtained. Thereafter, an adaptive estimator is established by integrating the identification error data compared with the classic adaptive estimator based on the prediction error output information. Thus, the design framework introduced in this research provides a new perspective for the design of identification algorithms. Under a general continuous excitation condition, the parameter estimation values can converge to the true values. Finally, experimental results and illustrative examples indicate the availability and usefulness of the proposed method.

Maximum likelihood interval-varying recursive least squares identification for output-error autoregressive systems with scarce measurements

The identification problem of output-error autoregressive (OEAR) systems with scarce measurements is considered in this paper. In order to overcome the massive absence of outputs, an interval-varying recursive identification algorithm is proposed through changing the sampling interval and skipping the missing outputs. Based on the maximum likelihood principle, a maximum likelihood interval-varying recursive least squares algorithm is proposed. The effectiveness of the proposed algorithm is tested by a numerical simulation example, and an application example about the heading motion control of underwater vehicle.

Filtering-based maximum likelihood hierarchical recursive identification algorithms for bilinear stochastic systems

Filtering-based maximum likelihood hierarchical recursive identification algorithms for bilinear stochastic systems

A regularised fast recursive algorithm for fraction model identification of nonlinear dynamic systems

The fraction model has been widely used to represent a range of engineering systems. To accurately identify the fraction model is however challenging, and this paper presents a regularised fast recursive algorithm (RFRA) to identify both the true fraction model structure and the associated unknown model parameters. This is achieved first by transforming the fraction form to a linear combination of nonlinear model terms. Then the terms in the denominator are used to form a regularisation term in the cost function to offset the bias induced by the linear transformation. According to the structural risk minimisation principle based on the new cost function, the model terms are selected based on their contributions to the cost function and the coefficients are then identified recursively without explicitly solving the inverse matrix. The proposed method is proved to have low computational complexity. Simulation results confirm the efficacy of the method in fast identification of the true fraction models for the targeted nonlinear systems.

Analysis and design of control systems via parameter‐based approach

Analysis and design of control systems via parameter‐based approach

Identification of dual‐rate sampled nonlinear systems based on the cycle reservoir with regular jumps network

SummaryThere are two main difficulties for identification of dual‐rate sampled nonlinear systems; one is the unknown nonlinear properties and the other is the dual‐rate sampled data. Considering the above two points, this paper proposes a cycle reservoir with regular jumps (CRJ) network based recursive identification algorithm. Unlike a traditional method, the proposed one does not require a priori knowledge of the nonlinearity and can handle dual‐rate data; therefore, its applicability can be guaranteed. Firstly, the CRJ network is used to describe the nonlinear characteristics of the target systems. However, the update of the CRJ network requires the missing outputs, thus the auxiliary model identification theory is adopted and the missing outputs are replaced by the estimated outputs of the network. In this process, the output weights of the CRJ network are updated at a slow rate, while the estimates of the missing outputs are updated quickly, resulting in an interactive estimation computation process. Then, to improve the identification accuracy, the hyper‐parameters of the CRJ network are optimized by means of the particle swarm optimization algorithm. Finally, simulation examples are presented to demonstrate the effectiveness of the proposed algorithm.

Adaptive fuzzy fault-tolerant control for active seat suspension systems with full-state constraints

In this paper, an adaptive fuzzy fault-tolerant control (FTC) scheme is proposed for active seat suspension systems. The addressed system contains electromagnetic actuator faults. In addition, the constraints of the displacements of the car seat, active suspension, and wheel, their vertical vibration speeds and current intensity are included. In the control design, since the systems have dynamic characteristics of complexities and spring non-linearities, the fuzzy logic systems are utilized to approximate the unknown nonlinear dynamics. By the aid of Barrier Lyapunov functions and based on the adaptive backstepping recursive design algorithm, a novel adaptive fuzzy FTC scheme is formulated. When the electromagnetic actuator fails, the developed control scheme can guarantee that all the vertical vibration states are stable. Eventually, the effectiveness of the presented control method is offered to illustrate by a random road surface.

A Generalized Stochastic Approximation for the Recursive System Identification

This paper presents an approach for constructing probabilistic approximation type algorithms used in system identification systems. The proposed approach allows obtaining recursive identification algorithms under fairly mild assumptions about noise and disturbances that distort the system. The obtained algorithm does not require inversion of the Hessian of the identification criterion and is robust to changes in the order of the Hessian. This example demonstrates good convergence properties of the obtained algorithm compared to conventional recursive systems.

Recursive adaptive filtering algorithms for sparse channel identification and acoustic noise reduction

Recursive adaptive filtering algorithms for sparse channel identification and acoustic noise reduction

Recursive algorithms for identification of dual Youla-Kucera parametrized plant models in closed loop

The growing interest in using dual Youla Kucera plant parametrization for modeling plant uncertainties raises the need for recursive identification algorithms dedicated to the identification of these structures in closed loop in view of developing appropriate iterative tuning and adaptive control strategies. The paper presents recursive algorithms for identification in closed loop of dual Youla-Kucera parametrized plant models. These algorithms assure global asymptotic stability in the deterministic environment and allow to obtain unbiased parameter estimation in the presence of measurement noise when the plant model is in the model set. The paper also re-visit the Hansen scheme which allows to associate open loop type recursive identification algorithms for the identification of these structures in closed loop. When the plant model is not in the model set, comparison of the various algorithms is done in terms of the bias distribution. Further comparisons and performance evaluation is provided by simulations on some relevant examples and experimental identification in closed loop of a test bench for active noise control.

Prescribed-time stabilisation design for a class of nonlinear systems with state constraints

This paper will investigate the prescribed-time stabilisation problem for a class of nonlinear systems with state constraints. A recursive design algorithm is proposed to solve the prescribed-time stabilisation problem. Firstly, a barrier Lyapunov function (BLF) is employed and a time-varying coordinate transformation is utilised. Then, a modified method of -backstepping is proposed, and a novel stabilising function by adding a fractional term proposed in this paper which is capable of decreasing the BLF to the origin within any desired settling time and then achieving the prescribed-time stabilisation. Such a fractional term plays an important role in achieving prescribed-time stabilisation. Next, the analysis of the prescribed-time stabilisation with state constraints is presented. Finally, an example shows the effectiveness of the proposed method. The feature of this paper is that the settling time is not only independent of the design parameters, nor does it depend on the initial conditions, and can be set according to per our will. Other is that the constraints of all the states are not violated.

A recursive identification algorithm for linear discrete periodic systems

The parameter identification problem of linear discrete period time-varying (LDPV) systems is studied in this paper and a two-step identification algorithm based on the recursive least squares principle is proposed. The recursive principle-based identification algorithm is mostly used to deal with the parameter identification of time-invariant systems. Combine with the cyclic reconstruction method, the LDPV system is converted to a linear time-invariant system. Then, based on this and combined with recursive least squares identification, a discrimination algorithm is proposed for the parameter estimation of the LDPV system. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed algorithm.