A mass and road slope integrated estimation strategy based on the joint iteration of least square method and Sage-Husa adaptive filter for autonomous logistics vehicle

2021 ◽  
pp. 095440702110417
Author(s):  
Weida Wang ◽  
Yuanbo Zhang ◽  
Ke Chen ◽  
Hua Zhang ◽  
Xiantao Wang ◽  
...  
Keyword(s):  
Adaptive Filter ◽  
Autonomous Vehicles ◽  
Least Square Method ◽  
Estimation Algorithm ◽  
Least Square ◽  
Recursive Least Square ◽  
Estimation Strategy ◽  
Vehicle Mass ◽  
Integrated Estimation ◽  
Road Slope

Autonomous logistics vehicles are characterised by large changes in mass and their performances are greatly influenced by slope. In addition, sensors on autonomous vehicles are expensive and difficult to be installed considering application environment. To address these problems, a novel integrated estimation strategy for vehicle mass and road slope, which is based on the joint iteration of multi-model recursive least square (MMRLS) and Sage-Husa adaptive filter with the strong tracking filter (SH-STF), is proposed by utilising information involving speed, nominal engine torque and inherent parameters of vehicles. Firstly, due to the separate slowly-changing and time-dependent characteristics, the vehicle mass and road slope are estimated by using MMRLS and SH-STF separately. Secondly, the longitudinal dynamics gain and the steering dynamics gain are calculated separately based on each model’s residual probability distribution. Then, the two estimations module are combined by employing an iterative algorithm. Finally, the proposed strategy is verified by simulation and real vehicle tests. The tests result reveals that the estimation algorithm can effective estimate vehicle mass and road slope in real-time under straight going and steering conditions.

scholarly journals An Effective Joint Soft-Sensing Strategy for Multi-Information under Diverse Vehicle Driving Scenarios

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 505
Author(s):  
Jianfeng Chen ◽  
Jiantian Sun ◽  
Shulin Hu ◽  
Yicai Ye ◽  
Haoqian Huang ◽  
...  
Keyword(s):  
Time Delay ◽  
Fast Response ◽  
Least Square ◽  
Accurate Information ◽  
Recursive Least Square ◽  
Soft Sensing ◽  
The Road ◽  
Vehicle Mass ◽  
Cubature Kalman Filter ◽  
Road Slope

A variety of accurate information inputs are of great importance for automotive control. In this paper, a novel joint soft-sensing strategy is proposed to obtain multi-information under diverse vehicle driving scenarios. This strategy is realized by an information interaction including three modules: vehicle state estimation, road slope observer and vehicle mass determination. In the first module, a variational Bayesian-based adaptive cubature Kalman filter is employed to estimate the vehicle states with the time-variant noise interference. Under the assumption of road continuity, a slope prediction model is proposed to reduce the time delay of the road slope observation. Meanwhile, a fast response nonlinear cubic observer is introduced to design the road slope module. On the basis of the vehicle states and road slope information, the vehicle mass is determined by a forgetting-factor recursive least square algorithm. In the experiments, a contrasted strategy is introduced to analyse and evaluate performance. Results declare that the proposed strategy is effective and has the advantages of low time delay, high accuracy and good stability.

Recursive field estimation and tracking for autonomous manipulation

Robotica ◽  
2011 ◽  
Vol 30 (5) ◽  
pp. 743-753 ◽  
Author(s):  
Soo Jeon
Keyword(s):  
Radial Basis Function Network ◽  
Least Square Method ◽  
Least Square ◽  
Source Tracking ◽  
Control Law ◽  
Task Space ◽  
Recursive Least Square ◽  
Wide Range ◽  
Field Estimation ◽  
Task Space Control

SUMMARYAutonomous operation of mechanical systems often requires the ability to detect and locate a particular phenomenon occurring in the surrounding environment. Being implemented to articulated manipulation, such a capability may realize a wide range of applications in autonomous maintenance and repair. This paper presents the sensor-driven task space control of an end-effector that combines the field estimation and the target tracking in an unknown spatial field of interest. The radial basis function network is adopted to model spatial distribution of an environmental phenomenon as a scalar field. Their weight parameters are estimated by a recursive least square method using collective measurements from the on-board sensors mounted to the manipulator. Then the asymptotic source tracking has been achieved by the control law based on the gradient of the estimated field. A new singularity tolerant scheme has been suggested to command the task space control law despite singular configurations. Simulation results using the three-link planar robot and the 6-revolute elbow manipulator are presented to validate the main ideas.

Design and Realization on the Optimal Quadratic Controller for the Positional Servo System of Numerical Control System

2011 ◽  
Vol 317-319 ◽  
pp. 1960-1963
Author(s):  
Li Bing Zhang ◽  
Ting Wu
Keyword(s):  
Control System ◽  
Control Method ◽  
Servo System ◽  
Least Square Method ◽  
Least Square ◽  
Servo Control ◽  
Numerical Control ◽  
Recursive Least Square ◽  
Servo Control System ◽  
Position Servo

This paper presents a technique for the position servo system of numerical control (NC) machine tool by utilizing the optimal quadratic controller. The mathematical model of the position servo control system is structured, which of the plant model is identified by making use of recursive least square method. The fundamental method of designing the optimal quadratic controller is proposed. Simulation of the optimal quadratic controller and PID controller are implemented by using MATLAB. The results of simulation show that the proposed control method of positional servo control system has better dynamic characteristics and better steady performance.

A Method for Modal Parameter Identification of Time Varying Vibration Systems

2012 ◽  
Vol 479-481 ◽  
pp. 688-693
Author(s):  
Zi Ying Wu ◽  
Kun Shi
Keyword(s):  
Linear Time ◽  
Least Square Method ◽  
Degree Of Freedom ◽  
Least Square ◽  
Modal Parameters ◽  
Time Varying ◽  
Recursive Least Square ◽  
Modal Parameter ◽  
Linear Time Invariant ◽  
Prony Method

In this paper a new time varying multivariate Prony (TVM-Prony) method is put forward to identify modal parameters of time varying (TV) multiple-degree-of-freedom systems from measured vibration responses. The proposed method is based on the classical Prony method that is often used to identify modal parameters of linear time invariant systems. The main advantage of the propose approach is that it can analyze multi-dimensional nonstationary signals simultaneously. A modified recursive least square method based on the traditional one is presented to determine the TV coefficient matrices of the multivariate parametric model established in the proposed method. The efficiency and accuracy of the identification approach is demonstrated by a numerical example, in which a TV mass-string system with three-degree-of-freedom is investigated. Satisfied results are obtained.

scholarly journals Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Krasnosel'skii-Pokrovskii Model

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Miaolei Zhou ◽  
Shoubin Wang ◽  
Wei Gao
Keyword(s):  
Estimation Algorithm ◽  
Least Square ◽  
Variable Step Size ◽  
Hysteresis Model ◽  
Correction Algorithm ◽  
Recursive Least Square ◽  
Step Size ◽  
Least Square Estimation ◽  
Control Precision ◽  
Variable Step

As a new type of intelligent material, magnetically shape memory alloy (MSMA) has a good performance in its applications in the actuator manufacturing. Compared with traditional actuators, MSMA actuator has the advantages as fast response and large deformation; however, the hysteresis nonlinearity of the MSMA actuator restricts its further improving of control precision. In this paper, an improved Krasnosel'skii-Pokrovskii (KP) model is used to establish the hysteresis model of MSMA actuator. To identify the weighting parameters of the KP operators, an improved gradient correction algorithm and a variable step-size recursive least square estimation algorithm are proposed in this paper. In order to demonstrate the validity of the proposed modeling approach, simulation experiments are performed, simulations with improved gradient correction algorithm and variable step-size recursive least square estimation algorithm are studied, respectively. Simulation results of both identification algorithms demonstrate that the proposed modeling approach in this paper can establish an effective and accurate hysteresis model for MSMA actuator, and it provides a foundation for improving the control precision of MSMA actuator.

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