Extended Kalman Filter Observer Research Articles

State estimation and damping control for unmanned ground vehicles with semi-active suspension system

The new type of transportation based on intelligent driverless vehicles will bring great changes to people’s travel modes and put forward higher requirements for the ride comfortability of vehicles. This paper presents a new observing algorithm to estimate the suspension states in real time and cooperate with sliding mode controller to improve the ride comfortability. First, the nonlinear model of an air suspension system equipped with a continuously controllable damper is described in detail. Then, this nonlinear suspension model is linearized precisely based on the differential geometry theory; a linear Kalman filter observer is implemented for this linearized model; through the coordinate reverse transformation, the designed linear observer is transformed into a nonlinear one, which will be suitable for the original nonlinear system, so that, the proposed state observer can estimate all the states of the nonlinear quarter car suspension system. Then, in this nonlinear suspension system, a model reference sliding mode controller is designed to continuously control the damping force to improve the ride comfortability. Finally, the effectiveness and advantage of the proposed feedback linearization Kalman observer is illustrated by comparing with a traditional extended Kalman filter observer. The simulation research shows that the proposed feedback linearization observer enjoys a better estimation accuracy, higher operation efficiency, and greater control performance while cooperating with the sliding mode controller in ride comfortability control.

An Adaptive Approach to Real-Time Estimation of Vehicle Sideslip, Road Bank Angles, and Sensor Bias

Robust estimation of vehicle sideslip angle is essential for stability control applications. However, the direct measurement of sideslip angle is expensive for production vehicles. This paper presents a novel sideslip estimation algorithm which relies only on sensors available on passenger and commercial vehicles. The proposed method uses both kinematics and dynamics vehicle models to construct extended Kalman filter observers. The estimate relies on the results provided from the dynamics model observer where the tire cornering stiffness parameters are updated using the information provided from the kinematics model observer. The stability property of the proposed algorithm is discussed and proven. Finally, multiple experimental tests are conducted to verify its performance in practice. The results show that the proposed approach provides smooth and accurate sideslip angle estimation. In addition, our novel algorithm provides reliable estimates of bank angles and lateral acceleration sensor bias.

Design of model predictive controllers for PMSM drive system based on the extended Kalman filter observer

Considering some technical and economic reasons, it is not easy to directly measure the time-varying states which have a great impact on speed regulation performance in permanent magnet synchronous motor (PMSM) drive system. This paper proposes a method to estimate rotor speed, rotor position and load torque disturbance by utilising real-time estimations, based on the extended Kalman filter (EKF). In order to guarantee the accuracy and stability of PMSM drive system while running under load torque disturbance, this paper proposes a cascade closed-loop control strategy as a solution. The entire system is designed to track reference speed trajectory based on the estimated rotor speed, rotor position and load torque, using the model predictive control (MPC) algorithm based on linear PMSM model. Simulation results demonstrate the effectiveness of the states observer, and the cascade MPC structure shows the good performance in speed tracking of PMSM drive system.

Design of model predictive controllers for PMSM drive system based on the extended Kalman filter observer

Design of model predictive controllers for PMSM drive system based on the extended Kalman filter observer

Development of an EKF Observer for an automotive SCR system

Abstract In the recent years, engine developer attention is focused on the application of advanced control strategies for the SCR system, considered as the leading technology for the abatement of NOX emissions in Diesel engines. The most challenging task in SCR control is the optimization of the urea dosing strategy while reaching the balance between high NOX reduction efficiency and low NH3 slip in the tailpipe, especially during transients. In order to accomplish this task, an Extended Kalman Filter (EKF) estimator is developed, to be integrated in the closed loop control of SCR urea dosing. The EKF estimator is applied to a one-state model, in which the ammonia coverage ratio θ is the only dynamic process taken into account. To improve the model accuracy, an additional dynamics, related to total ammonia adsorption capacity Ω, is considered while conceiving the EKF. The recursive EKF algorithm allows achieving enhanced estimation accuracy with a slight increase of the computational burden that is still suitable with on-board application.

EKF Based Fuzzy PI Controlled Speed Sensorless Power Optimal Control of a Direct Drive Power System

Faced with wave irregularity, the corrosion of the mechanical sensor in wave power generation systems, hazardous working conditions, and the inaccuracy of conventional control methods in the shifting system, a new type of irregular wave maximum wave energy capture strategy are proposed. The motivation behind the strategy is speed sensorless power optimal control of a direct drive wave power system by an extended Kalman filter (EKF) and self-tuning fuzzy proportional integral (PI) control. In our strategy, a fast Fourier transform (FFT) is utilized to analyze the spectrum of the irregular wave excitation force, and the maximum superposition control condition of the wave energy extraction is constructed by the vector superposition principle. Taking the voltage and current parameters of the generator as the input, based on two stages of prediction and update, an EKF observer system is established to estimate the speed and position of the power generation system. The fuzzy parameters are used to dynamically adjust the PI parameters so as to achieve optimal power state tracking control. The simulation results show that the FFT can meet the power optimal tracking requirements of unknown irregular waves. The proposed speed sensorless control scheme has good dynamic characteristics, small degree and position observation errors, and strong robustness, and allows the system to follow a given value.

Robust integral backstepping control with extended Kalman filter of permanent magnet synchronous motor

Sensorless integral backstepping control combined with extended Kalman filter applied to permanent magnet synchronous motors (PMSM) is the topic of this paper. Moreover, a robust backstepping control is designed where an integral action is introduced in order to improve the robust properties of the controller. Our proposed control strategy is based on an accurate extended Kalman filter observer that estimates speed, position, stator currents and load torque. This Kalman filter is an optimal state estimator and is usually applied to a dynamic system that involves a random noise environment. The stability of the system with the observer-controller scheme is analysed and sufficient conditions are given to prove the practical stability. The simulation results are shown to illustrate the performance of the proposed scheme under parametric uncertainties and low speed.

Robust integral backstepping control with extended Kalman filter of permanent magnet synchronous motor

Sensorless integral backstepping control combined with extended Kalman filter applied to permanent magnet synchronous motors (PMSM) is the topic of this paper. Moreover, a robust backstepping control is designed where an integral action is introduced in order to improve the robust properties of the controller. Our proposed control strategy is based on an accurate extended Kalman filter observer that estimates speed, position, stator currents and load torque. This Kalman filter is an optimal state estimator and is usually applied to a dynamic system that involves a random noise environment. The stability of the system with the observer-controller scheme is analysed and sufficient conditions are given to prove the practical stability. The simulation results are shown to illustrate the performance of the proposed scheme under parametric uncertainties and low speed.

Bank of Extended Kalman Filters for Faults Diagnosis in Wind Turbine Doubly Fed Induction Generator

In order to increase the efficiency, to ensure availability and to prevent unexpected failures of the doubly fed induction generator (DFIG), widely used in speed variable wind turbine (SVWT), a model based approach is proposed for diagnosing stator and rotor winding and current sensors faults in the generator. In this study, the Extended Kalman Filter (EKF) is used as state and parameter estimation method for this model based diagnosis approach. The generator windings faults and current instruments defects are modelled, detected and isolated with the use of the faults indicators called residuals, which are obtained based on the EKF observer. The mathematical model of DFIG for both healthy and faulty operating conditions is implemented in Matlab/Simulink software. The obtained simulation results demonstrate the effectiveness of the proposed technique for diagnosis and quantification of the faults under study.

Rotor resistance estimation using Extended Kalman filter and spectral analysis for rotor bar fault diagnosis of sensorless vector control induction motor

The aim of this paper is the broken rotor bars fault detection for a sensorless vector controlled induction motor drive. The technique of control used is based on the field-oriented control (FOC), where the fault effect in the mechanical speed is compensated in order to ensure the operation continuity of the machine. The broken rotor fault is detected through the rotor resistance estimation using extended kalman filter observer and the fast Fourier transforms for analyzing a several electrical and mechanical quantities (i.e., rotor speed, quadratic current components of control and stator phase current). The spectral analysis is effectuated only if the variation of the rotor resistance estimation is significant. An EKF observer has proven to be an excellent mathematical tool for the fault indicator in the variable speed drive. Moreover, the spectral analysis using FFT transforms can be a useful solution to ensure that the resistance variation indicates to broken rotor bars fault. The effectiveness of the sensorless control and the fault detection techniques are presented in simulation and validated in a real-time implementation using Matlab/Simulink with the real-time interface (RTI) based on dSpace 1104 board.

A Robust EKF Based Speed Estimator and Fuzzy Optimization Technique for Sensorless Induction Motor Drives

<p>The speed estimation technique of induction machines has become a non-trivial task. For estimating the speed of an induction motor precisely and accurately an optimum state estimator is necessary. This paper deals with the performance analysis of induction motor drives using a recursive, optimum state estimator. This technique uses a full order state space Extended Kalman Filter (EKF) model where the rotor flux, rotor speed and stator currents are estimated. A major challenge with induction motor occurs at very low and at near zero speed. In such cases, information about the rotor parameters with respect to stator side become unobservable while using the synchronously rotating reference frame. To overcome this lost coupling effect, EKF observer linearizes the non-linear parameter in every sampling period and estimates the states and machine parameters simultaneously. The proposed algorithm is tuned to obtain least error in estimated speed. Any error found is further optimized using a non-linear fuzzy controller to obtain improved performance of the drive.</p>

Adaptive robust control of cancer chemotherapy with extended Kalman filter observer

Abstract In this paper we control the amount of three major biological cell types (normal, immune and tumor cells) under uncertainty in cancer model parameters, using different chemotherapy drug dosages. To achieve this goal, an adaptive robust controller is proposed for a third order nonlinear model, which consists of the interaction between normal, immune and tumor cells. We adjust the drug dosages to control the tumor growth and maintain immune and normal cells in their desired values. Due to tumor micro-environmental and biological changes and measurement inaccuracies, the exact quantity of the model parameters is not available. Therefore, it is necessary to design the controller in a way that it is robust against parameters uncertainty and variations, the proposed robust adaptive controller manipulates the drug dosages and estimates the parameters of the model, simultaneously. The resulting system is robust against parameters uncertainty and variations. The global stability and tracking convergence of the controller is proved using time-varying Lyapunov function. Moreover, extended Kalman filter observer is applied to estimate the immune cells, due to the difficulty measuring them during the biological in vivo experiments. The performance of the proposed controller and observer are investigated by computational results. Computational results show the desired effect of drug dosage injections on the normal, immune and tumor cells. We observe that the controller guarantees the robust performance against the parameters uncertainty. The extended Kalman filter observer has effective performance and estimates the immune cells with high accuracy. This approach could impact robust tumor control using appropriate drug dosages while the parameters of the model change over time in a patient and across different patients.

Sensorless field oriented control of PMSM based on the extended KALMAN filter observer

Sensorless field oriented control of PMSM based on the extended KALMAN filter observer

Estimation of road friction coefficient using extended Kalman filter, recursive least square, and neural network

This paper presents an appropriate method for estimating road friction coefficient. The method uses measured values from wheel angular velocity and yaw rate sensors of a vehicle so that it could estimate the road friction coefficient. The estimation process is done in three steps: first, vehicle lateral and longitudinal velocities along with yaw rate value are identified by an extended Kalman filter observer when lateral acceleration and yaw rate values are subjected to process and measurement noises, respectively. Then, lateral and longitudinal tire forces are estimated using a recursive least square algorithm so that to be used in a neural network designed based on well-known Magic Formula tire model. In the final stage, using a multilayer perceptron neural network and estimated values of the previous stages, the road friction coefficient is estimated. Finally, the set of estimators is evaluated using 14 degrees of freedom full vehicle dynamic model and the obtained results are compared with their actual values of vehicle model for two different maneuvers of vehicle.

Integrated control of active front steering and electric power steering system with EKF estimation of lateral force

Good vehicle handling characteristics are essential to ensure traffic safety. In the integrated system of electric power steering (EPS) and active front steering (AFS), the steering wheel torque may change abruptly with the interference of active steering. The additional front road wheel angle generated by AFS gives rise to the change of tyre lateral force, which results in the change of steering wheel torque finally. Although the tyre forces are difficult to measure, the extended Kalman filter (EKF) algorithm can be used to estimate it. The problem of steering wheel torque change can be resolved by tyre forces estimate based on the EKF observer in nonlinear system. The vehicle nonlinear mathematical model is presented and a nonlinear tyre lateral force estimator is also established basing on EKF. The estimated tyre lateral forces can be applied to compensate the abrupt change of steering wheel torque, which realises the combination of vehicle states estimation and vehicle control. The simulation results in MATLAB/Simulink show the effectiveness of proposed control strategy.

Efficiency improved sensorless control scheme for electric vehicle induction motors

This study presents an energy efficient and speed-sensorless control scheme for electric vehicle induction motors (IMs). The IM drive is designed to feature characteristics such as fast dynamic response, high efficiency and low cost. Hybrid field orientation control (FOC) and direct torque control (DTC) is employed as the main control method of IM drive. This method enjoys the advantages of both FOC and DTC and eliminates certain disadvantages. A field weakening algorithm is developed and utilised within the control strategy to ensure robust operation at high speeds. The cost is lowered by eliminating the speed encoder and estimating the speed using an extended Kalman filter observer. Furthermore, a loss minimisation algorithm is associated with the main control strategy to ensure high IM drive efficiency. The effectiveness of the proposed method is verified through simulation and experimental results.

Adaptive fuzzy terminal sliding-mode control of MEMS z-axis gyroscope with extended Kalman filter observer

This paper presents a new terminal sliding-mode control (TSMC) for the micro electro-mechanical systems (MEMS) z-axis gyroscope. However, TSMC may chatter when uncertainty values are overestimated or may exhibit a steady-state error when uncertainty values are underestimated. In this paper, an adaptive fuzzy terminal sliding-mode controller is designed to retain the advantages of the terminal sliding-mode controller and to reduce the chattering occurred with the terminal sliding-mode controller. Stability analysis of the TSMC is presented in the presence of external disturbance and model uncertainties. Moreover, an extended Kalman filter (EKF) observer is designed to estimate the angular velocity and all of the gyroscope parameters and convergence analysis of the proposed EKF algorithm is presented. Numerical simulations using the nonlinear dynamic model of an MEMS z-axis gyroscope with uncertainties demonstrate the effectiveness of the approach in fast trajectory tracking problems and robustness in estimating the gyroscope parameters and also the angular velocity.

Field programmable gate array-based sensorless control of a brushless synchronous starter generator for aircraft application

Sensorless control of synchronous machine (SM) is a well-investigated research topic. However, avionics manufacturers still have restrictions about its applications due to the complexity of the on-board systems. This study presents a study investigating the use of an extended Kalman filter (EKF) to estimate the position and speed of a brushless exciter synchronous starter/generator. In spite of the machine complexity and the critical choice of the EKF covariance matrices, the EKF observer presents satisfying simulation results in estimating the position and the velocity of the mentioned motor during the start-up. Experimental results performed on an SM with an EKF implemented on a field programmable gate array are also shown to prove the efficiency of this type of implementation.

Simultaneous rotor and stator resistance estimation of squirrel cage induction machine with a single extended kalman filter

Accurate knowledge of rotor and stator resistance variations in a squirrel-cage induction motor (SCIM) is crucial for the performance of sensorless control of SCIM over a wide range of speeds. This study seeks to address this issue with a single Extended Kalman Filter (EKF) based solution, which is also known to have accuracy limitations when a high number of parameters/states are estimated with a limited number of inputs. To this aim, different from the author's previous approach in operating several EKFs in an alternating manner (the so-called braided EKF), an 8^{th}-order EKF is implemented in this study to test its performance for the simultaneous estimation of rotor and stator resistances with a single algorithm. Beyond the resistances, the EKF observer also estimates the load torque, rotor and stator fluxes and speed in the wide speed range (-n_{max}

Model‐Based Nonlinear Control of Batch Fluidized Bed Dryers

AbstractControl of a batch fluidized bed dryer is required in many industries. In this paper, the optimization and feedback control of material moisture content in a batch fluidized bed dryer are studied. The optimal operating conditions of the dryer have been found and a generic model control (GMC) strategy has been implemented to achieve a desired drying rate for wet materials. As it is usually impractical to measure moisture content directly, an extended Kalman filter (EKF) observer is used to estimate the moisture content on‐line in order to detect the drying end‐point and to provide a state estimate for feedback control. Studies show that the EKF observer and the GMC controller can provide satisfactory estimation and control of the particle moisture content even if the process model error is significant.