State Estimation Research Articles

Adaptive backstepping sliding mode compensation control for electro‐hydraulic load simulator with backlash links

AbstractWhen a mechanical backlash between the loading piston rod and the steering gear piston rod connection of the electro‐hydraulic load simulator (EHLS), it will lead to poor loading accuracy. To solve this problem, an adaptive backstepping sliding mode controller based on the backlash feedforward compensation and state observer is proposed. First, the mechanism of surplus force generation in the EHLS is analyzed, and a mathematical model containing position disturbance and backlash nonlinearity is established based on the analysis results. Second, a state observer is designed to estimate the system state and the parameter adaptive law is designed to estimate the system parameters. Third, the adaptive backstepping method is combined with the non‐singular terminal sliding mode control (NTSMC) strategy to deal with the position disturbance and parameter uncertainty and to enhance the system's dynamic performance. Finally, a backlash feed‐forward compensator is engineered to restrain the detrimental effects caused by the backlash nonlinearity, and the simulation experimental results show that the designed new method can realize the precise loading of the EHLS under different working conditions, and the surplus force is suppressed better at the same time.

Direct reconstruction of the temperature field of lithium-ion battery based on mapping characteristics

Lithium-ion (Li-ion) batteries are susceptible to the working temperature so that the monitoring is essential to the battery internal temperature. The electrochemical-thermal coupling model completely reflects the battery internal behavior. However, the high complexity and multi-field coupling make it difficult to be applied to reconstruct the battery temperature field based on state observer. Based on mapping characteristics, this work proposes a temperature field direct reconstruction scheme. And the electrochemical-thermal coupling model plays a large role in this scheme. Mapping characteristic vectors of the transient temperature field are obtained from the step response model of the Li-ion battery temperature field to charge and discharge current. Furthermore, the quantitative connection of the mapping characteristic vectors between the surface points of and the internal nodes of the battery is characterized by a relevance degree matrix. And a direct reconstruction model of the internal transient temperature field is established. The internal temperature field is reconstructed directly online and in real time via exploiting the temperature measurements on battery surface. In numerical experiments, the three-dimensional temperature field of the Li-ion battery charge and discharge processes is reconstructed. The effects of charge and discharge rates, model mismatch, and measurement errors on the reconstruction results are investigated. During the charge and discharge processes, the maximum values of the transient average error of the reconstructed temperature field are about 0.4 K and 0.3 K, respectively. And the transient average error is generally below 0.8 K when the standard deviation of measurement errors is 0.5 K.

Sliding mode tension control for the yarn winding process with extended state observer

In the textile industry, yarn tension control is directly related to product quality and production efficiency. Addressing the nonlinearities, uncertainties, and external disturbances encountered by tension control systems, this work initially establishes a mathematical model to describe the dynamic characteristics of yarn tension. Building upon this foundation, a yarn tension sliding mode control strategy based on an extended state observer (ESO) is proposed. This strategy employs the ESO to estimate and compensate for system uncertainties and disturbances. Subsequently, a sliding mode controller is designed for the compensated system, utilizing hyperbolic tangent functions for the reaching law, thereby enhancing the dynamic performance of the yarn tension control system. The stability of the controller is analyzed using Lyapunov theory. Recognizing the complexity of controller parameter tuning, an improved grey wolf optimizer (GWO) is introduced for further adjustment and optimization of the controller parameters. Finally, comparative simulations demonstrate that the designed controller maintains rapid response and high-precision dynamic characteristics even in the presence of external disturbances and noise. This underscores the promising application prospects of the proposed method in practical systems.

Model-free control of a magnetically supported plate

Established model-based methods often use a combination of state feedback and observer to control complex systems. They rely on detailed mathematical models that are often hard to derive. Nonetheless, such methods may achieve a high level of accuracy, which justifies the cumbersome modelling. An alternative approach is model-free control, in a form introduced by Fliess and Join, where the system is approximated in a short time interval by a low-order differential equation with unknown parts, a so-called ultra-local model. This control method is a powerful tool, but the parametrisation and the concrete implementation may require time, effort, and experience. The present paper investigates the systematic tuning of a model-free controller for a magnetically supported plate that is modelled as an unstable multiple-input multiple-output system. Furthermore, the incorporation of model information into the model-free controller is investigated. These adaptations ultimately improve results by simplifying parameter tuning and interpretation of estimates. Several experiments are carried out on a test bed to show the capabilities of the proposed algorithms for set point stabilisation and trajectory tracking. The effects of the different parameters in the model-free controllers are addressed, and excellent robustness with respect to actuator faults is demonstrated. Filters for estimating derivatives and unknown quantities are designed using an open-source toolbox.

Specified-performance active disturbance rejection control for position stabilization of the tip of gangways

In the presence of uncertain dynamics, uncertain wave-induced vessel motion disturbances, and uncertain mechanical friction, a position stabilization control scheme with specified performance is proposed for the tip of the gangway (ToG) to achieve wave-induced vessel motion compensation in this article. Based on a barrier function, a specified-time extended state observer (STESO) is innovatively constructed, which, in a specified settling time, provides the estimates of the total disturbances lumped by uncertain dynamics, uncertain wave-induced vessel motion disturbances, and uncertain mechanical friction. Further, based on the constructed STESO and another barrier function, an active disturbance rejection control law with specified performance for the position stabilization of the ToG is created such that the position stabilization errors of the ToG reach and stay within the specified tolerance error bands in the specified settling times. Therein, it is solved that in the existing literature, the bandwidth of the prescribed-time extended state observer or controller will approach infinity when the time approaches the specified settling time. This is because our properly designed envelops of the ToG position estimation errors of STESO and the ToG position stabilization errors guarantee that the bandwidths of the STESO and the controller are bounded. Simulation results with comparisons show the effectiveness and the superiority of the proposed control scheme.

Concurrent Learning Robust Adaptive Fault Tolerant Boundary Regulation of Hyperbolic Distributed Parameter Systems.

This article develops a robust adaptive boundary output regulation approach for a class of complex anticollocated hyperbolic partial differential equations subjected to multiplicative unknown faults in both the boundary sensor and actuator. The regulator design is based on the internal model principle, which amounts to stabilize a coupled cascade system, which consists of a finite-dimensional internal model driven by a hyperbolic distributed parameter system (DPS). To this end, a systematic sliding mode equipped with a backstepping approach is developed such that the robust state feedback control can be realized. Moreover, since the available information is a faulty boundary measurement at the right side point, state estimation is required. However, due to the presence of boundary unknown faults, we need to solve an issue of joint fault-state estimation. Restrictive persistent excitation conditions are usually required to guarantee the exact estimation of faults but are unrealistic in practice. To this end, a novel concurrent learning (CL) adaptive observer is proposed so that exponential convergence is obtained. It is the first time that the spirit of CL is introduced to the field of DPSs. Consequently, the observer-based adaptive boundary fault tolerant control scheme is developed, and rigorous theoretical analysis is given such that the exponential output regulation can be achieved. Finally, the effectiveness of the proposed methodology is demonstrated via comparative simulations.

State Estimation for Recurrent Neural Networks With Intermittent Transmission.

This work addresses the state estimation problem for recurrent neural networks over capacity-constrained communication channels. The intermittent transmission protocol is used to reduce the communication load, where a stochastic variable with a given distribution is used to describe the transmission interval. A corresponding transmission interval-dependent estimator is designed, and an estimation error system based on it is also derived, whose mean-square stability is proved by constructing an interval-dependent function. By analyzing the performance in each transmission interval, sufficient conditions of the mean-square stability and the strict (Q,S,R) - γ -dissipativity are established for the estimation error system. Finally, the correctness and the superiority of the developed result are illustrated by a numerical example.

Representation-Learning-Based CNN for Intelligent Attack Localization and Recovery of Cyber-Physical Power Systems.

Enabled by the advances in communication networks, computational units, and control systems, cyber-physical power systems (CPPSs) are anticipated to be complex and smart systems in which a large amount of data are generated, exchanged, and processed for various purposes. Due to these strong interactions, CPPSs will introduce new security vulnerabilities. To ensure secure operation and control of CPPSs, it is essential to detect the locations of the attacked measurements and remove the state bias caused by malicious cyber-attacks such as false data inject attack, jamming attack, denial of service attack, or hybrid attack. Accordingly, this article makes the first contribution concerning the representation-learning-based convolutional neural network (RL-CNN) for intelligent attack localization and system recovery of CPPSs. In the proposed method, the cyber-attacks' locational detection problem is formulated as a multilabel classification problem for CPPSs. An RL-CNN is originally adopted as the multilabel classifier to explore and exploit the implicit information of measurements. By comparing with previous multilabel classifiers, the RL-CNN improves the performance of attack localization for complex CPPSs. Then, to automatically filter out the cyber-attacks for system recovery, a mean-squared estimator is used to handle the difficulty in state estimation with the removal of contaminated measurements. In this scheme, prior knowledge of the system state is obtained based on the outputs of the stochastic power flow or historical measurements. The extensive simulation results in three IEEE bus systems show that the proposed method is able to provide high accuracy for attack localization and perform automatic attack filtering for system recovery under various cyber-attacks.

Observer‐based controller design for blood glucose regulation for type 1 diabetic patients with disturbance estimation: A backstepping approach

AbstractIn this paper, an observer‐based nonlinear controller is proposed to regulate blood glucose concentration (BGC) in type 1 diabetes mellitus (T1DM). The considered virtual patient model is the extended Bergmann minimal model, which is augmented by a meal disturbance and adapted to represent the insulin–glucose homeostasis of T1DM. The backstepping (BS) technique is used to design a closed‐loop feedback controller. The proposed controller does not need to measure insulin, plasma concentrations, and external disturbances while improving control performance and robustness against uncertainty. Insulin concentrations and plasma levels are estimated using state observers and disturbance using a disturbance estimator. These estimations are used as feedback to the controller. The asymptotic stability of the observer‐based controller is proved using the Lyapunov theorem. Furthermore, it is proven that the system is bounded input–bounded output (BIBO) stable in the presence of uncertainties caused by uncertain parameters and external disturbances. For real situations, we consider only the BGC available for measurement, and in addition, inter‐ and intra‐patient variability of system parameters is considered. The results confirm that the proposed controller can asymptotically regulate BGC through appropriate injection of insulin under meal disturbance and of variations in system parameters.

Sliding model control of active magnetic bearing rotor system based on state observer

This paper addresses a novel sliding mode control based on state observer for active magnetic bearing rotor system. Firstly, the state-space model of a radial AMB rotor system is established with considering unbalance disturbance and gyro effect for a vertical flywheel energy storage system. Then a sliding mode function and switching surface are constructed based on an observer. Meanwhile, a separation and decoupling strategy based on Finsler’s lemma is proposed. Through this method, the constraint relationship between the controller gain, active magnetic bearing matrices and the Lyapunov variables is eliminated. After that a method for chattering reduction in the sliding-mode controller is raised. Relied on these techniques, new sufficient conditions for the stability of AMB rotor system are given in the framework of linear matrix inequalities. Finally, the effectiveness of the proposed sliding mode controller is validated on the experimental platform of the flywheel energy storage system.

SAFENESS: A Semi-Supervised Transfer Learning Approach for Sea State Estimation Using Ship Motion Data

SAFENESS: A Semi-Supervised Transfer Learning Approach for Sea State Estimation Using Ship Motion Data

Towards Robust and Accurate Cooperative State Estimation for Multiple Rigid Bodies

Towards Robust and Accurate Cooperative State Estimation for Multiple Rigid Bodies

Primal-Dual Decomposed State Estimation for Multi-Energy Systems Leveraging Variational Bayesian Approximation

Primal-Dual Decomposed State Estimation for Multi-Energy Systems Leveraging Variational Bayesian Approximation

Robust Smith Predictor-Based Internal Model Nonlinear Control With Lead Compensation Extended State Observer for PMLSM Servo Systems Considering Time Delay

Robust Smith Predictor-Based Internal Model Nonlinear Control With Lead Compensation Extended State Observer for PMLSM Servo Systems Considering Time Delay

Temperature regulation for liquid-cooled fuel cell based on adaptive sliding mode control

The liquid cooling system (LCS) of the proton exchange membrane (PEM) fuel cell suffers from large time delay, coupling, uncertainties, and various disturbances, making it susceptible to temperature regulation overshoot and control oscillation. This article focuses on a composite control scheme for the LCS that has effectively addressed the aforementioned issues. We first propose a two-layer control approach for the pump to minimize coupling, and an average delay model (ADM) for radiator whose parameter uncertainties are removed by equilibrium optimizer (EO). To overcome disturbances, an adaptive sliding mode control (ASMC) based on extended state observer (ESO) is presented, and its finite-time convergence is demonstrated. Additionally, the indirect regulation for the stack temperature is designed to considerably reduce the time delay in the fan control loop. Co-simulation results indicate that the proposed ASMC significantly suppresses the chattering phenomenon compared with regular sliding mode control (SMC). In contrast to incremental fuzzy control, the proposed scheme possesses superiorities in response speed, tracking accuracy, anti-disturbance, as well as suppression of overshoot and oscillation.

Robust Voltage Control With Resonant Extended State Observer for Hybrid Fuel Cell System

Robust Voltage Control With Resonant Extended State Observer for Hybrid Fuel Cell System

The Enhanced Anti-anxiety Potentiality and Relevant Material Basis for the Extracts of Bile Processed Coptidis Rhizoma

Objective Bile-processed Coptidis Rhizoma (CR) exhibits stronger cold properties and central effect. However, the commonly used bile includes pig/cattle/sheep bile. It is currently unclear which kind of bile performs the best synergistic effect, and the enhanced efficacy is mainly contributed by what components. In this work, a comparative study on pig/cattle/sheep bile processed CR (pCR/cCR/sCR) was carried out. Materials and Methods Firstly, the anxiety model of sthenic heat type was established. Then, pharmacological research involving general state observation, biochemical examination (succinate dehydrogenase, glucokinase, corticosterone, adrenocor ticotropic hormore), pathological evaluation (liver and hippocampus tissue), and behavioral tests (elevated plus maze, open field test) was implemented. Finally, chemical assessment involving high-performance liquid chromatography (HPLC) fingerprints of pCR/cCR/sCR extracts and mass spectrograms of pig/cattle/sheep bile were performed in combination. Results cCR performed the most prominent enhancement in improving hyperactive energy metabolism in liver and abnormal hyperactivity of hypothalamic–pituitary–adrenal axis axis. As for chemical assessment, HPLC fingerprints of pCR/cCR/sCR extracts indicated that alkaloid components detected in cCR extracts were relatively low, mass spectrograms of pig/cattle/sheep bile suggested that bile acids in cattle bile were relatively abundant, and the contents of taurocholic acid, glycocholic acid, and glycodeoxycholic acid were particularly high. Conclusion Cattle bile could be specified as the processing excipient for bile-processed CR, and the high levels of taurocholic acid, glycocholic acid, and glycodeoxycholic acid mainly contributed to the enhanced efficacy of cCR.

The active disturbance rejection control method for small-scale turbojet engine

To enhance the disturbance resistance of the control system for a small-scale turbojet engine, research has been conducted on the active disturbance rejection control (ADRC) method. With gas generator speed N g as the control parameter, an ADRC control scheme was established. Tracking Differentiator (TD), Nonlinear State Error Feedback (NLSEF), and Extended State Observer (ESO) were analyzed in detail, and their parameters were designed. The ADRC method was compared with the Expert PID control method through simulation experiments. The results show that ADRC has a small overshoot and strong disturbance rejection capabilities, allowing for a faster recovery to a steady state when disturbances occur.

A New Grey Wolf Optimizer Tuned Extended Generalized Predictive Control for Distillation Process.

The distillation process plays an essential role in the petrochemical industry. However, the high-purity distillation column has complicated dynamic characteristics such as strong coupling and large time delay. To control the distillation column accurately, we proposed an extended generalized predictive control (EGPC) method inspired by the principles of extended state observer and proportional-integral-type generalized predictive control method; the proposed EGPC can adaptively compensate the system for the effects of coupling and model mismatch online and performs well in controlling time-delay systems. The strong coupling of the distillation column needs fast control, and the large time delay requires soft control. To balance the requirement for fast and soft control at the same time, a grey wolf optimizer with reverse learning and adaptive leaders number strategies (RAGWO) was proposed to tune the parameters of EGPC, and these strategies enable RAGWO to have a better initial population and improve its exploitation and exploration ability. The benchmark test results indicate that the RAGWO outperforms the existing optimizers for most of the selected benchmark functions. Extensive simulations show that the proposed method in terms of fluctuation and response time is superior to other methods for controlling the distillation process.

Full state observer-based pole placement controller for pulse width modulation switched mode voltage-controlled buck Converter

Switched mode DC-DC converters play an important role in today's renewable energy systems, electric Vehicles, consumer electrical appliances, and many more. While many researchers have designed controllers for DC-DC converters, achieving low implementation cost remains a significant hurdle. This paper proposes a cost-effective observer-based pole placement controller for a PWM voltage-controlled Buck converter with clear step by step design approach. The state space averaged model of the DC-DC power converter is used to design pole placement with integrator compensator and full state observer. Pole placement is employed to improve the transient and steady state behavior of Voltage reference tracking response and full state observer is applied to extract the inductor current with the aim of removing the actual current sensor. Design specifications are set to a settling time of less than 5 msec and an overshoot of less than 5 %. While designing Observer-Controller combination, the separation principle, designing the controller and observer independently, is utilized by placing the observer poles far to the left of the controller poles for the Observer to converge faster. The designed controller and observer is verified with simulation in MATLAB/SIMULINK software and design of developed controller and observer are realized using low-cost analog electronics circuit components The results shows that the proposed system's strength in tracking the reference output voltage even in the presence of input voltage disturbance and the mean value of inductor current is well extracted having only the output voltage and MOSFET gate signal.