Identification of continuous-time hybrid “Box–Jenkins” systems with multiple unknown time delays using two-stage parameter estimation algorithm

2019 ◽  
Vol 36 (6) ◽  
pp. 2111-2130
Author(s):  
Yamna Ghoul
Keyword(s):  
Continuous Time ◽  
Time Delays ◽  
Least Square ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Two Stage ◽  
Content Type ◽  
Single Output ◽  
Multiple Input ◽  
Unknown Time Delays

Purpose This study/paper aims to present a separable identification algorithm for a multiple input single output (MISO) continuous time (CT) hybrid “Box–Jenkins”. Design/methodology/approach This paper proposes an optimal method for the identification of MISO CT hybrid “Box–Jenkins” systems with unknown time delays by using the two-stage recursive least-square (TS-RLS) identification algorithm. Findings The effectiveness of the proposed scheme is shown with application to a simulation example. Originality/value A two-stage recursive least-square identification method is developed for multiple input single output continuous time hybrid “Box–Jenkins” system with multiple unknown time delays from sampled data. The proposed technique allows the division of the global CT hybrid “Box–Jenkins” system into two fictitious subsystems: the first one contains the parameters of the system model, including the multiple unknown time delays, and the second contains the parameters of the noise model. Then the TS-RLS identification algorithm can be applied easily to estimate all the parameters of the studied system.

Identification of continuous-time systems with multiple unknown time delays

2017 ◽  
Vol 36 (4) ◽  
pp. 1310-1324
Author(s):  
Yamna Ghoul ◽  
Kaouther Ibn Taarit ◽  
Moufida Ksouri
Keyword(s):  
Continuous Time ◽  
Time Delays ◽  
Identification Algorithm ◽  
Optimal Method ◽  
Output Measurement ◽  
Content Type ◽  
Continuous Time Systems ◽  
Ct System ◽  
Unknown Time Delays ◽  
Time Systems

Purpose The purpose of this paper is to present a separable identification algorithm for a multiple-input single-output (MISO) continuous-time (CT) system. Design/methodology/approach This paper proposes an optimal method for the identification of MISO CT systems with unknown time delays by using the Simplified Refined Instrumental Variable method. Findings Simulations results are presented to show the performance of the proposed approach in the presence of additive output measurement noise. Originality/value This paper presents an optimal and robust method to separable delays and parameter identification of a MISO CT system with unknown time delays from sampled input/output data.

Identification of continuous-time hybrid ‘Box-Jenkins’ systems having multiple unknown time delays

2018 ◽  
Vol 41 (2) ◽  
pp. 366-377
Author(s):  
Yamna Ghoul ◽  
Kaouther Ibn Taarit ◽  
Moufida Ksouri
Keyword(s):  
Continuous Time ◽  
Instrumental Variable ◽  
Time Delays ◽  
Monte Carlo Analysis ◽  
Noise Model ◽  
Practical Applications ◽  
Single Output ◽  
Noise Structure ◽  
Instrumental Variable Method ◽  
Unknown Time Delays

For many years, various methods for the identification of parameters of continuous-time models have been available and implemented in widely. However, most methods apply models where the output are contaminated by a white noise or without noise in some others cases, which are unrealistic in most practical applications owing to their associated noise structure. Some other methods neglect the presence of time delays. Then it can be shown that the estimates are not statistically efficient. To cope with this issue, this paper deals with the identification of multi-input single-output continuous-time hybrid ‘Box-Jenkins’ systems having multiple unknown time delays from sampled input/output data. The proposed work presents a based-instrumental variable method for the separable estimation of both process parameters, multiple unknown time delays and the noise model. The effectiveness of the proposed scheme is proven through a numerical example illustrated by Monte Carlo analysis.

scholarly journals Dynamic Model and Inverse Kinematic Identification of a 3-DOF Manipulator Using RLSPSO

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 416 ◽  
Author(s):  
Josias Batista ◽  
Darielson Souza ◽  
Laurinda dos Reis ◽  
Antônio Barbosa ◽  
Rui Araújo
Keyword(s):  
Dynamic Model ◽  
Inverse Kinematics ◽  
Lagrange Equation ◽  
Computational Cost ◽  
Dynamic Parameter ◽  
Search Space ◽  
Least Square ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Recursive Least Square

This paper presents the identification of the inverse kinematics of a cylindrical manipulator using identification techniques of Least Squares (LS), Recursive Least Square (RLS), and a dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by RLS (RLSPSO). A helical trajectory in the cartesian space is used as input. The dynamic model is found through the Lagrange equation and the motion equations, which are used to calculate the torque values of each joint. The torques are calculated from the values of the inverse kinematics, identified by each algorithm and from the manipulator joint speeds and accelerations. The results obtained for the trajectories, speeds, accelerations, and torques of each joint are compared for each algorithm. The computational costs as well as the Multi-Correlation Coefficient ( R 2 ) are computed. The results demonstrated that the identification accuracy of RLSPSO is better than that of LS and PSO. This paper brings an improvement in RLS because it is a method with high complexity, so the proposed method (hybrid) aims to improve the computational cost and the results of the classic RLS.

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