Simultaneous estimation of state and unknown road roughness input for vehicle suspension control system based on discrete Kalman filter

2019 ◽  
Vol 234 (6) ◽  
pp. 1610-1622
Author(s):  
Gi-Woo Kim ◽  
Sun-Woo Kang ◽  
Jung-Sik Kim ◽  
Jong-Seok Oh
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
The State ◽  
Vehicle Suspension ◽  
Simultaneous Estimation ◽  
Unknown Input ◽  
State Variables ◽  
The Road ◽  
Road Roughness ◽  
Suspension Control

This study presents an improved discrete Kalman filter for simultaneously estimating both all state variables and the unknown road roughness input for a vehicle suspension control system that plays a key role in the ride quality and handling performance while driving the vehicle. The suspension system is influenced by the road roughness input, which causes undesirable vibrations associated with vehicle instability. It is therefore important to estimate the road roughness and state variables information when designing the model-based controller for the vehicle suspension control system associated with the vehicle’s vertical dynamics. However, the implementation of conventional estimation theories for the suspension control system is challenging because the road roughness acts as an unknown input and is difficult to be measured or estimated while driving. This study presents an improved Kalman filter with unknown input, which can simultaneously estimate the state variables and road roughness without any prior information about the vehicle suspension control system. The proposed road roughness input estimator is evaluated by using an in-vehicle test bed with a laser-type profilometer. Finally, the state estimation performance of the proposed estimator for a vehicle suspension control system is validated by using CarSim software.

scholarly journals Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter

2013 ◽  
Vol 10 (4) ◽  
pp. 5169-5224 ◽  
Author(s):  
V. R. N. Pauwels ◽  
G. J. M. De Lannoy ◽  
H.-J. Hendricks Franssen ◽  
H. Vereecken
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
The State ◽  
Bias Error ◽  
Simultaneous Estimation ◽  
State Variables ◽  
Two Stage ◽  
Forecast Bias ◽  
Error Covariance ◽  
Observation Bias

Abstract. In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the Discrete Kalman Filter, and the state variables using the Ensemble Kalman Filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware Ensemble Kalman Filter. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

scholarly journals Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter

2013 ◽  
Vol 17 (9) ◽  
pp. 3499-3521 ◽  
Author(s):  
V. R. N. Pauwels ◽  
G. J. M. De Lannoy ◽  
H.-J. Hendricks Franssen ◽  
H. Vereecken
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
The State ◽  
Simultaneous Estimation ◽  
State Variables ◽  
Two Stage ◽  
Forecast Bias ◽  
Error Covariance ◽  
Observation Bias

Abstract. In this paper, we present a two-stage hybrid Kalman filter to estimate both observation and forecast bias in hydrologic models, in addition to state variables. The biases are estimated using the discrete Kalman filter, and the state variables using the ensemble Kalman filter. A key issue in this multi-component assimilation scheme is the exact partitioning of the difference between observation and forecasts into state, forecast bias and observation bias updates. Here, the error covariances of the forecast bias and the unbiased states are calculated as constant fractions of the biased state error covariance, and the observation bias error covariance is a function of the observation prediction error covariance. In a series of synthetic experiments, focusing on the assimilation of discharge into a rainfall-runoff model, it is shown that both static and dynamic observation and forecast biases can be successfully estimated. The results indicate a strong improvement in the estimation of the state variables and resulting discharge as opposed to the use of a bias-unaware ensemble Kalman filter. Furthermore, minimal code modification in existing data assimilation software is needed to implement the method. The results suggest that a better performance of data assimilation methods should be possible if both forecast and observation biases are taken into account.

The use of models with improved characteristics in the missile control system

2020 ◽  
Keyword(s):  
Control System ◽  
Control Systems ◽  
A Priori ◽  
The State ◽  
State Variables ◽  
Control Loop ◽  
Fundamental Diagram ◽  
Management Efficiency ◽  
Control Matrix ◽  
Improved Characteristics

A fundamental diagram of a control system for missiles of various classes is investigated. A functional diagram of a control system with an intelligent component for long-range aerodynamic rockets returning to the atmosphere is developed. It is proposed to use in the control loop an ensemble of a priori missile models and models of external influences. It is proposed to improve the accuracy of control systems with an intelligent component by increasing the degree of controllability of the state variables for a priori models. The most convenient numerical criterion of controllability degree for of the state variables of the models is presented. The results of mathematical modeling showed a slight increase in the efficiency of missile control with an increase in the degree of controllability of the pitch angle by changing the coefficients of the control matrix. Keywords rocket; control system; intelligent component; an action acceptor; a priori model; controllability; degree of controllability; management efficiency

A study of electric vehicle suspension control system based on an improved half-vehicle model

2007 ◽  
Vol 4 (3) ◽  
pp. 236-242 ◽  
Author(s):  
Jiang-Tao Cao ◽  
Hong-Hai Liu ◽  
Ping Li ◽  
David J. Brown ◽  
Georgi Dimirovski
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Vehicle Suspension ◽  
Vehicle Model ◽  
Suspension Control

A Ship Flotation Control System Based on Pump-Driven Tank

2011 ◽  
Author(s):  
Juan Guo ◽  
Meng Tang ◽  
Zaojian Zou
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
Structural Damage ◽  
Model Simulation ◽  
Theoretical Calculations ◽  
Wave Disturbance ◽  
Ship Motion ◽  
State Variables ◽  
Initial State ◽  
Asymmetric Loading

Extensive development in ship motion control strategies and systems in recent decades has called for higher requirements in control system accuracy and reliability. In this paper, a ship flotation control system based on pump-driven active tank is established. A special case is discussed, where the ship is heeling under an asymmetric loading either by structural damage or asymmetric consumption of ammunition. The purpose of the control system is to keep the ship in upright floating position in waves by transferring liquid between the tanks. Kalman filter is designed to eliminate the wave disturbance, in order to identify the heeling angle caused by asymmetric loading change. The effect of wave disturbance at different wave encounter angles, wave heights, as well as ship speeds is analyzed. Tuning of filter parameters such as initial state variables, initial error covariance and noise covariance is performed to achieve better filtering performance for different parameters of waves and ship motion. To verify the control model, simulation is conducted for a 3340t ship and the simulation results are compared with the theoretical calculations. The research results show the applicability and efficiency of Kalman filter. The concept of the control system presented in the paper is helpful to improve ship stability and safety when ship upright floating condition is disturbed.

scholarly journals Design of and Research into a Multiple-Fuzzy PID Suspension Control System Based on Road Recognition

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2190
Author(s):  
Xinkai Ding ◽  
Ruichuan Li ◽  
Yi Cheng ◽  
Qi Liu ◽  
Jilu Liu
Keyword(s):  
Control System ◽  
Pid Control ◽  
Good Control ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
The Road ◽  
Suspension Control ◽  
Neural Network Algorithm ◽  
Road Grade ◽  
Differential Control

By analyzing the shortcomings of the traditional fuzzy PID(Abbreviation for Proportional, Integral and Differential) control system (FPID), a multiple fuzzy PID suspension control system based on road recognition (MFRR) is proposed. Compared with the traditional fuzzy PID control system, the multiple fuzzy control system can identify the road grade and take changes in road conditions into account. Based on changes in road conditions and the variable universe and secondary adjustment of the control parameters of the PID controller were carried out, which makes up for the disadvantage of having too many single input parameters in the traditional fuzzy PID control system. A two degree of freedom 1/4 vehicle model was established. Based on the suspension dynamic parameters, a road elevation algorithm was designed. Road grade recognition was carried out based on a BP neural network algorithm. The experimental results showed that the sprung mass acceleration (SMA) of the MFRR was much smaller than that of the passive suspension system (PS) and the FPID on single-bump and sinusoidal roads. The SMA, suspension dynamic deflection (SDD) and tire dynamic load (TDL) of the MFRR were significantly less than those of the other two systems on roads of each grade. Taking grade B road as an example, compared with the PS, the reductions in the SMA, SDD and TDL of the MFRR were 40.01%, 34.28% and 32.64%, respectively. The control system showed a good control performance.

scholarly journals Noise control with Kalman filter for active headrest

2021 ◽  
Vol 39 (5) ◽  
pp. 937-944
Author(s):  
Lei WANG ◽  
Kean CHEN ◽  
Jian XU ◽  
Wang QI
Keyword(s):  
Kalman Filter ◽  
Noise Control ◽  
Active Noise Control ◽  
Noise Signal ◽  
State Equation ◽  
The State ◽  
Convergence Speed ◽  
State Variables ◽  
Gradient Based ◽  
Convergence Performance

A control strategy with Kalman filter (KF) is proposed for active noise control of virtual error signal for active headset. Comparing with the gradient based algorithm, KF algorithm has faster convergence speed and better convergence performance. In this paper, the state equation of the system is established on the basis of virtual error sensing, and only the weight coefficients of the control filter are considered in the state variables. In order to ensure the convergence performance of the algorithm, an online updating strategy of KF parameters is proposed. The fast-array method is also introduced into the algorithm to reduce the computation. The simulation results show that the present strategy can improve the convergence speed and effectively reduce the noise signal at the virtual error point.

Design of Extended Kalman Filter for a Shape Memory Alloy Manipulator

2003 ◽  
Author(s):  
Mohammad H. Elahinia ◽  
Hashem Ashrafiuon ◽  
Mehdi Ahmadian ◽  
William T. Baumann
Keyword(s):  
Kalman Filter ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Extended Kalman Filter ◽  
State Space Model ◽  
Angular Position ◽  
The State ◽  
State Variables ◽  
Time Step ◽  
Noisy Measurements

This paper presents an Extended Kalman Filter (EKF) for estimation of the state variables of a single degree of freedom rotary manipulator actuated by Shape Memory Alloy (SMA). A state space model for the SMA manipulator is presented. The model includes nonlinear dynamics of the manipulator, constitutive model of Shape Memory Alloy, and the electrical and heat transfer behavior of SMA wire. In the experimental setup, angular position of the arm is the only state variable that is measured. The other state variables of the system are arm’s angular velocity, SMA wire’s stress, temperature and the Martensite factor, which are not available experimentally due to measurement difficulties. Hence, a model-based state estimator that works with noisy measurements is presented based on the Extended Kalman Filter. This estimator predicts the state vector at each time step and corrects its prediction based on the angular position of the arm which can be measured experimentally. The state variables collected through model simulations are also used to evaluate the performance of the EKF. Several EKF simulations are presented that show accurate, and robust performance of the estimator for different types of inputs.

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