Augmented State Model Research Articles

Optimal control strategy for traffic platoon longitudinal coordination around equilibrium state enabled by partially automated vehicles

This paper presents an internal model-Kalman filtering-based optimal hybrid feedforward/feedback control strategy for traffic platoon control coordination enabled by SAE Automation Level 2 or Level 3 vehicles, i.e., partially automated vehicles (PAVs). Based on the Helly linear car-following model, a PAV platoon is established. Taking each vehicle’s characteristic polynomial as the dominant internal model polynomial, an augmented system state model with filtered inputs is formed, and then both the system states and the external disturbance/internal perturbation from previous vehicle can be estimated by a delicately designed Kalman filter. By using a linear quadratic regulation approach, a distributed hybrid optimal feedforward/feedback controller utilizing the local estimated states is constructed to achieve a practically decoupled platoon regulation around an equilibrium state. String stability of the control system thus obtained is further analyzed. Finally, extensive numerical simulations verified the theoretical analysis in decoupling between successive vehicles, in suppressing the influence of various external disturbances, and in maintaining string stability.

Deception attack monitoring in vulnerable hydroelectric generator system

AbstractThis article deals with unknown deception attacks detection of hydroelectric generator. Such cyber attacks are switching disturbances that affect the input information transmitted from the power system stabilizer to the plant through a constrained communication network. The generator dynamics can be modeled as an augmented state model, whereas the unknown input reconstruction is obtained from a modified Kalman filter recursions under communication constraints. The obtained design ensures the minimization of disturbances with accurate cyberattack monitoring.

High‐Order Extended Kalman Filter Design for a Class of Complex Dynamic Systems with Polynomial Nonlinearities

A novel High-order extended Kalman Filter (HEKF) is designed for a class of complex dynamic systems with polynomial nonlinearities. The state and measurement models are represented by multi-dimensional high-order polynomials, respectively. All high-order polynomials in the state model are defined as implicit variables. By combining original variables with implicit variables, the state model is equivalently formulated to be a pseudo-linear form. By modeling dynamic relationship between implicit variables and combining original variables with all implicit variables, a new linear augmented state model is established correspondingly. The measurement model can be equivalently formulated as a linear form. On the basis of the new linear state and linear measurement models, the HEKF is designed and derived in detail. Simulation results demonstrate the effectiveness of the proposed estimator.

Simultaneous spatiotemporal bias and state estimation for asynchronous multi‐sensor system

Elimination of spatiotemporal bias of asynchronous multiple sensors is the foundation of accurate multi-sensor data fusion. In this article, a sequential processing simultaneous spatiotemporal bias and state estimation algorithm for asynchronous multi-sensor system is proposed. The spatiotemporal bias is combined with target state to obtain an augmented state vector, and the augmented state model is formulated. For asynchronous multi-sensor with known but different sampling periods, a sequential processing method is proposed to handle the multiple measurements. The relationship of sensor measurements, target state and spatiotemporal bias is analysed, and the corresponding measurement model is formulated. A novel algorithm, in which the unscented Kalman filter is used to handle the nonlinearity between the augmented measurement and state vectors, is proposed to jointly estimate the spatiotemporal bias and target state. Simulation results verify the effectiveness of the proposed algorithm.

Identification of an Autonomous Underwater Vehicle Hydrodynamic Model Using the Extended, Cubature, and Transformed Unscented Kalman Filter

Having an accurate mathematical model is essential in design, control, and navigation process of an autonomous underwater vehicle (AUV). Due to the modeling simplifications, the available mathematical models suffer from the uncertainty of their parameters and they usually need an identification phase for improving the modeling accuracy. In case of AUVs, the hydrodynamic coefficients of the model play an important role and they should be identified using the available experimental data. In this paper, a 6-DoF dynamic model of an AUV is presented and then some of its parameters including viscous damping, the body lift, and control input coefficients that have the highest effects on the modeling error are identified by augmented state model method. The extended Kalman filter (EKF), cubature Kalman filter (CKF), and transformed unscented Kalman filter (TUKF) are used as the estimation filters. To verify and compare the three estimation filters with consideration of the predetermined hydrodynamic coefficients and spiral maneuver AUV results, these three methods are evaluated. The results indicate that the TUKF identifies the best hydrodynamic model due to solving both the CKF nonlocal sampling problem and the EKF linearization problem.

Hierarchical linear dynamical systems for unsupervised musical note recognition

In this paper we develop a new framework for time series segmentation based on a Hierarchical Linear Dynamical System (HLDS), and test its performance on monophonic and polyphonic musical note recognition. The center piece of our approach is the inclusion of constraints in the filter topology, instead of on the cost function as normally done in machine learning. Just by slowing down the dynamics of the top layer of an augmented (multilayer) state model, which is still compatible with the recursive update equation proposed originally by Kalman, the system learns directly from data all the musical notes, without labels, effectively creating a time series clustering algorithm that does not require segmentation. We analyze the HLDS properties and show that it provides better classification accuracy compared to current state-of-the-art approaches.

Backstepping Control of Electro-Hydraulic System Based on Extended-State-Observer With Plant Dynamics Largely Unknown

In this paper, an output position feedback control of the electro-hydraulic system (EHS) is proposed based on an extended-state-observer (ESO) with backstepping. On the basis of the augmented state model of the EHS, the ESO is designed to handle the unknown load disturbance and uncertain nonlinearity. Then, an observer bandwidth constraint is derived to compromise between the dynamic performance and the maximal load capability of EHS. The effectiveness of the proposed control for the suppression of largely unknown disturbances has been demonstrated by comparative experimental study, which indicates the proposed approach can achieve better dynamic performance on the motion control of two-degree-of-freedom robotic arm.

Systematic error real-time registration based on modified input estimation

In order to estimate the systematic error in the process of maneuvering target adaptive tracking, a new method is proposed. The proposed method is a linear tracking scheme based on a modified input estimation approach. A special augmentation in the state space model is considered, in which both the systematic error and the unknown input vector are attached to the state vector. Then, an augmented state model and a measurement model are established in the case of systematic error, and the corresponding filter formulas are also given. In the proposed scheme, the original state, the acceleration and the systematic error vector can be estimated simultaneously. This method can not only solve the maneuvering target adaptive tracking problem in the case of systematic error, but also give the system error value in real time. Simulation results show that the proposed tracking algorithm operates in both the non-maneuvering and the maneuvering modes, and the original state, the acceleration and the systematic error vector can be estimated simultaneously.

Medium access scheduling for input reconstruction under deception attacks

This article presents a novel approach of state filtering scheme and the sensor scheduling co-design for cyber-physical systems subject to random deception attacks. Such cyber attacks are switching disturbances that affect the input information transmitted from the controller to the plant through a constrained communication network. The cyber-physical system dynamics can be modelled as an augmented state model, while the unknown input reconstruction is obtained from a modified Kalman filter recursions under communication constraints. To deal with limited bandwidth constraint, a periodic sensor scheduling strategy is proposed in order to ensure the minimization of disturbances and cyber-attack effects.

Fault‐Tolerant Control of a Distributed Database System

Optimal state information‐based control policy for a distributed database system subject to server failures is considered. Fault‐tolerance is made possible by the partitioned architecture of the system and data redundancy therein. Control actions include restoration of lost data sets in a single server using redundant data sets in the remaining servers, routing of queries to intact servers, or overhaul of the entire system for renewal. Control policies are determined by solving Markov decision problems with cost criteria that penalize system unavailability and slow query response. Steady‐state system availability and expected query response time of the controlled database are evaluated with the Markov model of the database. Robustness is addressed by introducing additional states into the database model to account for control action delays and decision errors. A robust control policy is solved for the Markov decision problem described by the augmented state model.

Optimal trajectory tracking of dynamic multistage ROT cooling process with stochastic disturbances

Random disturbance input is bound to cause a state uncertainty. In particular, when the random disturbance input is time correlated, the impact on the state uncertainty becomes more severe. Time correlation, which could also be expressed in terms of an interstage correlation, implies that the value of a one time instant is correlated with the value of another time instant; it suggests that the random disturbance is nonwhite. A run-out-table cooling is a complex dynamic multistage process with intrinsic characteristics such as, long time delay and time-correlated disturbance, therefore it constitutes an exceptional case to investigate stochastic optimization methods, in order to optimally track the temperature trajectory along its stages. Stochastic optimization is applied on an augmented state model, which is a combination of a dynamic disturbance model and the system equation. The merit of the optimal solutions obtained by this approach is confirmed by simulation experiments. The results demonstrate the state propagation and robust tracking performance in the presence of stochastic time-correlated disturbance inputs.