Inaccurate System Model Research Articles

Distribution System State Estimation Based on Power Flow-Guided GraphSAGE

Acquiring real-time status information of the distribution system forms the foundation for optimizing the management of power system operations. However, missing measurements, bad data, and inaccurate system models present a formidable challenge for distribution system state estimation (DSSE) in practical applications. This paper proposes a physics-informed graphical learning state estimation approach, to address these limitations by integrating power flow equations and GraphSAGE. The generalization ability of GraphSAGE for unknown nodes is used to perform inductive learning of measurement information. For unseen measurement points in the training set, the simulation proves that the proposed approach can still satisfactorily predict the state quantity. The training process is guided by power flow equations to ensure it has physical significance. Additionally, the possibility of applying the proposed approach to an actual distribution area is explored. Equivalent preprocessing of the three-phase voltage measurement data of the actual distribution area is conducted to improve the estimation accuracy of the transformer measurement points and simplify the computation required for state estimation.

Data-driven methods for the inverse problem of suspension system excited by jump and diffusion stochastic track excitation

Following a lengthy tenure of service, suspension systems may exhibit inaccurate model parameters due to the presence of complex conditions, which can result in a deterioration of control and the potential for operational failures. It is therefore imperative to estimate the key parameters of suspension systems. This paper introduces two data-driven methods for addressing the inverse problem in suspension systems subjected to stochastic track excitations. By employing physics-informed neural networks (PINNs) and Monte Carlo (MC) simulation, we are able to address the resulting integro-differential equation that arises from stochastic jump processes, thus avoiding the necessity for mesh grids. In order to mitigate the numerical challenges that arise from the system parameters, a residual-based adaptive sampling method is proposed. These methods effectively infer unknown parameters, addressing scenarios where data is directly available as a probability density function (PDF) or only sparse trajectories are given. In the latter case, a novel loss function employing Kullback-Leibler divergence facilitates learning from stochastic trajectories. Both methods successfully obtain solutions to the forward Kolmogorov equation, as validated by numerical experiments testing their robustness against added noise. The results demonstrate accurate parameter estimation under varying noise intensities, highlighting the methods’ robustness.

Online parameter identification and optimal input design using perturbed nonlinear programming

Flight tests are performed to gather data for flight vehicle parameter estimation. If an a priori model is available, the inputs during the runtime of the flight test experiment can be designed, such that the information content in the gathered data is maximized. These optimal inputs, however, are based on an inaccurate system model, and the improvement of the system model is the ultimate goal of the system identification and therewith, the flight tests. There is an implicit relationship between the optimal inputs and the system model. In this paper, we investigate a scheme to update the optimal inputs during the flight using an online optimization technique, while performing online parameter estimation. An extended Kalman filter is used to compute new parameter values during the maneuver runtime. Concurrent to that, an interior point NLP solver computes the optimal maneuver updates, as new parameters become available in each iteration. The method is validated via a numerical example.

Position and stiffness control of an antagonistic variable stiffness actuator with input delay using super-twisting sliding mode control

Motor dynamics in antagonistic variable stiffness actuator (AVSA) is generally disregarded in control system design. This ignorance can lead to an inaccurate system model, affecting the performance of the closed-loop system. The motor dynamics can be modeled as an input-delay in actuator model. In this paper, the motor dynamics is modeled as the input time-delay for an AVSA for the first time. The stiffness of AVSA is a nonlinear function of system states; thus, stiffness tracking for an AVSA is a challenging task. Specifically, many of the existing delay compensation controllers cannot be used for stiffness tracking when the model contains input delay. To handle this issue, a nonlinear transformation is introduced and a super-twisting sliding mode control is then utilized to reach position and stiffness tracking simultaneously. Prediction-based feedback is involved together with some disturbance observers for estimating the external disturbance to compensate for the input time-delay. Simulation results show that the proposed design approach is successful in position and stiffness tracking and simultaneously in attenuating the external disturbance effect.

Compound control method for DC–DC converter

Considering the problems of internal and external disturbances, i.e. time-varying load, inaccurate system model and external uncertainties etc., a compound control strategy based on model predictive control (MPC) and non-linear extended state observer (ESO) are proposed to attenuate the output voltage fluctuation under continuous conduction mode (CCM) mode of the buck circuits. In addition, a non-linear ESO is used to observe all kinds of uncertainties caused by internal and external disturbances and to eliminate their effects by compensating these disturbances in the feed-forward channel. Compared with the pure MPC control, the simulation results show that the proposed composite control method has the better disturbance suppression effect and also can effectively improve the stability of the buck circuit.

Robust fading cubature Kalman filter and its application in initial alignment of SINS

Strapdown inertial navigation system (SINS) is a commonly used autonomous positioning instrument. As an integral calculating system, initial alignment is an essential work of SINS. Cubature Kalman filter (CKF) is a common algorithm for nonlinear initial alignment. Under the condition of inaccurate system model and non-Gaussian observation noise, the filter will appear to unstable or even divergence. In order to solve this problem, an anti-jamming improvement suitable for initial alignment is proposed in this paper. In which the prior covariance matrix and the observation noise covariance matrix are tuned by multiple fading factors. A Chi-square test method is designed to check the filter’s state, determining the introducing method of the fading factors autonomously. Experiment results show that, the proposed algorithm performs better in terms of robustness and adaptability even under the condition of inaccurate model and non-Gaussian observation noise.

Networked controller and observer design of discrete-time systems with inaccurate model parameters

This paper develops a novel off-policy Q-learning method to find the optimal observer gain and the optimal controller for achieving optimality of network-communication based linear discrete-time systems using only measured data. The primary advantage of this off-policy Q-learning method is that it can work for the linear discrete-time systems with inaccurate system model, unmeasurable system states and network-induced delays. To this end, an optimization problem for networked control systems composed of a plant, a state observer and a Smith predictor is formulated first. The Smith predictor is employed to not only compensate network-induced delays, but also make the separation principle hold, thus the observer and controller can be designed separately. Then, the off-policy Q-learning is implemented for learning the optimal observer gain and the optimal controller combined with the Smith predictor, such that a novel off-policy Q-learning algorithm is derived using only input, output and delayed estimated state of systems, not the inaccurate system matrices. The convergences of the iterative observer gain and the iterative controller gain are rigorously proven. Finally, simulation results are given to verify the effectiveness of the proposed method.

A Robust Single GPS Navigation and Positioning Algorithm Based on Strong Tracking Filtering

Kalman filter (KF) algorithm is a form of recursive optimal estimations, which utilizes information in time domain with less computational efforts to reduce the system errors. Recently, Kalman filtering technique has become a standard approach for reducing errors in a least squares sense and is widely applied in navigation and positioning fields. Unfortunately, the traditional KF needs accurate statistical information about mobile terminal; otherwise, it will result in lower precision and even divergence. To make up for the deficiency of KF algorithm, this paper presents a novel robust single global position system (GPS) navigation algorithm based on dead reckoning and strong tracking filter (STF), which still has strong tracking ability even when precise knowledge of the system models is not available. The proposed algorithm utilizes adaptive fading factor to adjust the gain matrix in real time to satisfy the orthogonality principle (OP), which indicates all useful information has been extracted from residual. Furthermore, we define residual covariance (RC) as an important index to evaluate the performance of the filtering algorithm and deduce the closed relationship of RCs in KF under the case of: 1) inaccurate system model; 2) erroneous initial value; and 3) abrupt change of states, respectively. We find that the RCs of the KF algorithm under all three above-mentioned cases have a bias compared with desired ones; as a result, they do not conform to the OP and have poor tracking ability, while the RC of STF conforms to the OP due to the fading factor which indicates the tracking performance is greatly improved in the proposed algorithm. The theoretical analysis and simulation results demonstrate that the performance of the proposed single GPS navigation algorithm based on STF outperforms that of the traditional KF algorithm.

Application of fuzzy adaptive control to a MIMO nonlinear time-delay pump-valve system

In this paper, a control strategy to balance the reliability against efficiency is introduced to overcome the common off-design operation problem in pump-valve systems. The pump-valve system is a nonlinear multi-input–multi-output (MIMO) system with time delays which cannot be accurately measured but can be approximately modeled using Bernoulli Principle. A fuzzy adaptive controller is applied to approximate system parameters and achieve the control of delay-free model since the system model is inaccurate and the direct feedback linearization method cannot be applied. An extended Smith predictor is introduced to compensate time delays of the system using the inaccurate system model. The experiment is carried out to verify the effectiveness of the control strategy whose results show that the control performance is well achieved.

Adaptive Cubature Strong Tracking Information Filter Using Variational Bayesian Method

For most practical nonlinear state estimation problems, the conventional nonlinear filters do not usually work well for some cases, such as inaccurate system model, sudden change of state-interested and unknown variance of measurement noise. In this paper, an adaptive cubature strong tracking information filter using variational Bayesian (VB) method is proposed to cope with these complex cases. Firstly, the strong tracking filtering (STF) technology is used to integrally improve performance of cubature information filter (CIF) aiming at the first two cases and an iterative scheme is presented to effectively evaluate a strong tracking fading factor. Secondly, the VB method is introduced to iteratively evaluate the unknown variance of measurement noise. Finally, the novel adaptive cubature information filter is obtained by perfectly combining the STF technology with the VB method, where the variance estimation provided by the VB method guarantees normal running of the strong tracking functionality.

Urban Oversaturated Traffic Network Control Based on Preference Multi-Objective Compatible Optimization Control

To solve the traffic congestion control problem on oversaturated network, the total delay is classified into two parts: the feeding delay and the non-feeding delay, and the control problem is formulated as a conflicted multi-objective control problem. The simultaneous control of multiple objectives is different from single objective control in that there is no unique solution to multi-objective control problems(MOPs). Multi-objective control usually involves many conflicting and incompatible objectives, therefore, a set of optimal trade-off solutions known as the Pareto-optimal solutions is required. Based on this background, a modified compatible control algorithm(MOCC) hunting for suboptimal and feasible region as the control aim rather than precise optimal point is proposed in this paper to solve the conflicted oversaturated traffic network control problem. Since it is impossible to avoid the inaccurate system model and input disturbance, the controller of the proposed multi-objective compatible control strategy is designed based on feedback control structure. Besides, considering the difference between control problem and optimization problem, user's preference are incorporated into multi-objective compatible control algorithm to guide the search direction. The proposed preference based compatible optimization control algorithm(PMOCC) is used to solve the oversaturated traffic network control problem in a core area of eleven junctions under the simulation environment. It is proved that the proposed compatible optimization control algorithm can handle the oversaturated traffic network control problem effectively than the fixed time control method.

Generator voltage stabilisation for the series-hybrid vehicle

This paper presents a simple, robust controller for use in speed control of an internal combustion engine for series-hybrid electric vehicle applications. Particular reference is made to the stability of the rectified DC link voltage under load disturbance and potentially inaccurate system models. In the system under consideration, the primary power source is a 4-cylinder normally aspirated gasoline internal combustion engine, which is mechanically coupled to a three-phase permanent magnet AC generator. The generated AC voltage is subsequently rectified to supply a lead-acid battery, and permanent magnet traction motors via three-phase full bridge power electronic inverters. Two complementary performance objectives exist. Firstly to maintain the internal combustion engine at its optimal operating point, and secondly to supply a stable 42V supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the internal combustion engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. An electronically operated throttle allows closed loop engine velocity control. System time-delays and non-linearities render closed loop control design extremely problematic. A model based controller is designed and shown to be robust in controlling the DC link voltage, resulting in well-conditioned operation of the hybrid vehicle.