An official website of the United States government
To perform power system monitoring and control using synchrophasor measurements, various dynamic state estimators have been proposed in the literature, including the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). However, they are unable to handle system model parameter errors and any type of outliers, precluding them from being adopted for power system real-time applications. In this paper, we develop a robust iterated extended Kalman filter based on the generalized maximum likelihood approach (termed GM-IEKF) for dynamic state estimation. The proposed GM-IEKF can effectively suppress observation and innovation outliers, which may be induced by model parameter gross errors and cyber attacks. We assess its robustness by carrying out extensive simulations on the IEEE 39-bus test system. From the results, we find that the GM-IEKF is able to cope with at least 25% outliers, including in position of leverage.
more »
« less
Risk-Sensitive Extended Kalman Filter
Designing robust algorithms in the face of estimation uncertainty is a challenging task. Indeed, controllers seldom consider estimation uncertainty and only rely on the most likely estimated state. Consequently, sudden changes in the environment or the robot’s dynamics can lead to catastrophic behaviors. Leveraging recent results in risk-sensitive optimal control, this paper presents a risk-sensitive Extended Kalman Filter that can adapt its estimation to the control objective, hence allowing safe output-feedback Model Predictive Control (MPC). By taking a pessimistic estimate of the value function resulting from the MPC controller, the filter provides increased robustness to the controller in phases of uncertainty as compared to a standard Extended Kalman Filter (EKF). The filter has the same computational complexity as an EKF and can be used for real-time control. The paper evaluates the risk-sensitive behavior of the proposed filter when used in a nonlinear MPC loop on a planar drone and industrial manipulator in simulation, as well as on an external force estimation task on a real quadruped robot. These experiments demonstrate the ability of the approach to significantly improve performance in face of uncertainties.
more »
« less
- PAR ID:
- 10577616
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-8457-4
- Page Range / eLocation ID:
- 10450 to 10456
- Subject(s) / Keyword(s):
- robotics estimation uncertainty model predictive control Kalman Filter
- Format(s):
- Medium: X
- Location:
- Yokohama, Japan
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Unmanned aerial manipulators have been growing in popularity over the years, alongside the complexity of the tasks they undertake. Many of these tasks include physical interaction with the environment, where a force control or sensing component is desirable. In these types of applications, the forces and torques, or the wrench, acting on the robot by the environment must be known. This paper presents a wrench observer based on an Extended Kalman filter (EKF), and compares it against acceleration-based, momentum-based, and hybrid wrench observers. Simulations using each of these observers are conducted with an underactuated aerial manipulator composed of a hexarotor with coplanar propellers and a 2-DOF manipulator. Measurement noise on par with what is expected in real-world applications is added to the sensor signals, and results show that the EKF-based wrench observer is superior at noise reduction and wrench estimation in many cases compared to the other observers.more » « less
-
The lithium iron phosphate (LFP) battery has more nonlinear characteristic than other battery type. For this reason, when we use electrical equivalent circuit model and the extended Kalman filter (EKF) for estimating the SOC, the estimation performance can be decreased in the nonlinear region. This paper proposes an advance estimation method of state of charge (SOC) for lithium iron phosphate (LFP) batteries. To improve the model accuracy, this paper utilizes the nonlinear observer for identifying the internal parameters of batteries. Furthermore, to reduce the nonlinear effect of the LFP batteries, this paper recast the Kalman process. Therefore, through the proposed method, the performance of SOC estimation can be more accurate and the computational burden is decreased when we apply the embedded system.more » « less
-
This paper addresses challenges in agricultural unmanned aerial vehicle (A-UAV) positioning, emphasizing the significance of accurate position estimation for applications like coverage path planning under depended noises. The study introduces a solution involving a PCA-based maximum correntropy Kalman filter (PCA-MCKF) to mitigate issues such as lowaltitude flight control, inaccurate position estimation due to coloured noise, and non-Gaussian distribution, including wind effects. Comparative analysis with traditional methods, such as Kalman filter (KF), PCA-KF, and PCA-MCKF, is conducted using four rotor-wing UAVs with linear and nonlinear dynamical models. The paper employs interval type-2 Fuzzy PID as an intelligent controller method and constant acceleration and constant velocity manoeuvre models for estimation. Root mean square error is used as the accuracy metric, and real-time simulations in Webots demonstrate the superiority of the proposed PCA-MCKF in enhancing agricultural UAV applications.more » « less
-
Real-time control of a fleet of Connected and Automated Vehicles (CAV) for Cooperative Adaptive Cruise Control (CACC) is a challenging problem concerning time delays (from sensing, communication, and computation) and actuator lag. This paper proposes a real-time predictive distributed CACC control framework that addresses time delays and actuator lag issues in the real-time networked control systems. We first formulate a Kalman Filter-based real-time current driving state prediction model to provide more accurate initial conditions for the distributed CACC controller by compensating time delays using sensing data from multi-rate onboard sensors (e.g., Radar, GPS, wheel speed, and accelerometer), and status-sharing and intent-sharing data in BSM via V2V communication. We solve the prediction model using a sequential Kalman Filter update process for multi-rate sensing data to improve computational efficiency. We propose a real-time distributed MPC-based CACC controller with actuator lag and intent-sharing information for each CAV with the delay-compensated predicted current driving states as initial conditions. We implement the real-time predictive distributed CACC control algorithms and conduct numerical analyses to demonstrate the benefits of intent-sharing-based distributed computing, delay compensation, and actuator lag consideration on string stability under various traffic dynamics.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA57147.2024.10611266
