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The real-time estimation of the AC power system equivalent parameters enables the grid-connected voltage source converter (VSC) to achieve more effective stability control during a short-circuit fault. In this paper, a novel Thevenin equivalent (TE) parameter estimation method based on local PMUs is proposed and applied to the phase-locked loop (PLL) synchronization stability control of VSC. First, the relationship between the variation of the measured voltage and the error of the estimated impedance is strictly derived based on the rules of norm inequality. Then, according to the analysis of the upper bound of the system voltage change, a voltage threshold is proposed to select the PMU measurements to ensure the accuracy of the estimation during the large disturbance. Finally, using the estimated TE parameters, the current control instruction of VSC during the fault is calculated in real-time to ensure a synchronous and stable operation. The simulations reveal that the proposed estimation method can accurately track the TE parameters after a fault. Furthermore, the associated current control actions during a fault can ensure the PLL synchronization stability of VSC.

In this note, we rely on the theoretical foundations of Radial Basis Function Neural Networks (RBF-NNs) to form an adaptive parameter estimation problem, which we solve using the recently introduced Prescribed Performance Control methodology. Such combination results in a compact, user-congurableadaptive nonlinear system identication methodology that can be used to retrieve the open-loop nonlinear plant dynamics in any compact region of interest.

Most recent studies on adaptive hydraulic tracking control focus on trajectory tracking performance while the parameter convergence property is often unsatisfying. This paper proposes a composite learning adaptive position tracking controller with improved parameter convergence for electro-hydraulic servo systems. In the composite learning, a prediction error is formulated to exploit input-output memory data, and parameter estimates are driven simultaneously by tracking and prediction errors. Practical exponential stability of the closed-loop system, which implies the convergence of both the tracking and parameter estimation error, is established by a more realizable interval-excitation condition than the stringent persistent-excitation condition. Therefore, superior trajectory tracking is obtained compared with the classical adaptive hydraulic control. Besides, the initial fluid control volumes of hydraulic systems are assumed to be unknown a priori, which enhances the generality of the proposed control approach. The above two properties are generally not achievable in prevalent approaches to adaptive hydraulic control. Moreover, noisy acceleration signals and the time derivatives of pressure signals are not needed in the proposed approach, which improves its robustness against measurement noise. Extensive experimental results verify its superiority over currently available ones.

This paper aims to handle the optimal attitude tracking control tasks for rigid bodies via a reinforcement learning-based control scheme, in which a constrained parameter estimator is designed to compensate system uncertainties accurately. This estimator guarantees the exponential convergence of estimation errors and can strictly keep all instant estimates always within pre-determined bounds. Based on it, a critic-only adaptive dynamic programming (ADP) control strategy is proposed to learn the optimal control policy with respect to a user-defined cost function. The matching condition on reference control signals, which is commonly employed in relevant ADP design, is not required in the proposed control scheme. We prove the uniform ultimate boundedness of the tracking errors and critic weight's estimation errors under finite excitation conditions by Lyapunov-based analysis. Moreover, an easy-to-implement initial control policy is designed to trigger the real-time learning process. The effectiveness and advantages of the proposed method are verified by both numerical simulations and hardware-in-loop experimental tests.

This article studies the guaranteed performance synchronization control problem for networked bilateral teleoperation systems with system uncertainties. The communication channel connecting the master and the slave is subject to asymmetrical varying time delays with unknown upper bounds. The first result on prescribed performance synchronization control for the bilateral teleoperation system under such a weak assumption on the communication time delays is provided. Moreover, a novel composite adaptive control algorithm is proposed under a much weaker interval-excitation (IE) condition. More specifically, parameter adaptive estimation accuracy and speed are quantificationally ensured by employing a composite technique. Therefore, both steady-state performance and transient-state performance are achieved for the position synchronization and parameter estimation with the proposed control strategy. The Lyapunov function and the multidimensional small-gain framework are utilized to derive system stability criteria. It demonstrates that the allowable maximal derivatives of the transmission delays can be easily computed with the given parameters of the control algorithm and the nonlinear performance functions. Finally, both simulation and experimental results are provided to demonstrate the feasibility and superiority of the proposed composite adaptive strategy.

Transmission line parameters are fundamental parameters of power system models that influence the safety, stability, and economy of power utility grids. Transmission line parameter estimation is a basic and necessary function and module of a power grid energy management system. However, traditional offline parameter estimation methods cannot cope well with parameter uncertainties caused by transmission line aging, environmental factors, and modern power grid developments. The widespread deployment of phasor measurement units (PMUs) in power networks has paved the way for accurate real-time-measurement-based transmission line parameter tracking. This article proposes PMU-based parameter estimation models considering series and shunt compensators. An online parameter estimation method is developed based on recursive least squares with a variable forgetting factor, which can adapt to various parameter mutations and has strong robustness to outlier measurements. This article also analyzes the sensitivity of parameters with respect to synchrophasor data. The simulation results under different scenarios verify the effectiveness, robustness, and adaptivity of the proposed method, which exhibits significant advantages over other existing parameter estimation methods.

The extended Kalman filter (EKF), one of the most popular state estimators for nonlinear uncertain dynamic systems, is considered here in its continuous-discrete (CD) form. The CD version enhances the EKF's accuracy but requires robust and computationally efficient numerical integration methods. The purpose of this paper is to present several new efficient integration methods suited to the implementation of the CD EKF and to compare them to classical implementations such as the Dormand-Prince and Euler methods. They are first applied on two proposed nonlinear parameter estimation testbenches and to the stiff Curtiss and Hirschfelder model for state and parameter estimation. Then they are applied on the estimation of the iron losses of a high speed synchronous machine. The results show that for highly nonlinear dynamic systems or for a low to medium sampling frequency, the implicit implementation of the EKF provides better accuracy and convergence safety. For less nonlinear functions and for a higher sampling frequency, classical explicit methods obviously give similar results for a lower computation time.

This article focuses on the design of a novel adaptive fuzzy event-triggered tracking control approach for a category of high-order uncertain nonlinear systems with prescribed performance requirements, in which a high-order tan-type barrier Lyapunov function (BLF) is employed to handle and analyze the output tracking error, fuzzy systems are adopted to identify the totally unknown nonlinear functions, and only one gain function rather than parameter estimation functions is designed to cancel out all unknowns appearing in fuzzy systems. As a result, complicated calculations are avoided and a structured simple control is achieved. The proposed controller not only ensures that the tracking error is always within a predefined region but also reduces the communication burden from the controller to the actuator. Finally, comparison simulations are presented to verify the effectiveness of the proposed control schemes.

To achieve the precision contouring performance, this paper presents a contouring compensation approach for the biaxial motion system even under extreme contour tasks. Two permanent magnet linear synchronous motors (PMLSMs) are installed in the biaxial motion platform. First, the dynamics of the biaxial motion servo system with the uncertainties, such as model parameter variations, additive disturbance, and nonlinear friction are introduced. Since Newton algorithm is provided to realize dynamic contouring error estimation, accurate estimation of the contouring error can be achieved, even for the high-velocity and complex contour with sharp corners and large curves. Then, iterative learning control (ILC) is used to control the contouring error for contouring performance improvement. An adaptive PD-type learning law based on an asymmetric Gaussian-like function is applied to improve the performance of system. Moreover, adaptive jerk control (AJC) integrated with adaptive feedback gain is designed to address the negative effect of the uncertainties and enhance the robustness. The adaptive feedback gain is dominated and tuned by a novel adaption law without the need for prior bound knowledge of the uncertainties. Meanwhile, parameter estimation is employed to handle the model parameter variations. Finally, the experimental results are presented to validate the efficiency and the achievable contouring performance of the proposed Newton-ILC and AJC approach.

It is essential to estimate stator inductance and rotor flux linkage online for fault diagnosis and high-performance control of surface-mounted permanent magnet synchronous machine (SPMSM). Current injection method is a popular way to estimate the two parameters. However, the dead-time influence of voltage-source-inverter (VSI) and the stator inductance variation caused by injected current could not guarantee the converge of the estimation. To solve the above two problems, a method of injecting square-wave angle for online parameter estimation is proposed in this paper. Firstly, the theory of eliminating dead-time influence with and without square-wave angle injection is analysed. Next, the least square algorithm is adopted to estimate stator inductance without injection. Then the rotor flux linkage is estimated with injecting square-wave angle. The saturation effect on estimation is also analyzed and both parameters are estimated without acquiring any nominal values. At last, the appropriate amplitude and frequency of square-wave angle are determined by the parameter error and convergence analysis. The proposed method is evaluated with simulation and extensive experiments under various speed and load conditions. It is noteworthy that not less than two parameters are estimated without the dead-time influence and the injection will not cause inductance variation, which is different from the previous work.

A velocity model composing speed sensors and motion constraints is used as the direct and effective auxiliary information of GNSS/INS integrated systems to improve the land vehicle navigation accuracy under interfered or blocked GNSS signal environments. Precise determination of the mounting parameter (the geometric relationship between sensors), including mounting angle and lever arm, is necessary to realize the full potential of velocity assistance. We propose an optimal mounting parameter estimation scheme based on velocity vector observations. The quaternion-based optimal attitude determination method is used to estimate the mounting angle, and the weighted recursive least square is applied to estimate the lever arm. A land vehicle test and an agricultural tractor test are carried out to verify the feasibility and correctness, and IMUs of different grades, especially the MEMS IMUs, are used. The results show that attitude errors especially the heading error are the main factors influencing mounting angle estimation and that the statistical lateral velocity based on the estimated mounting parameters is better than 0.03 m/s, thus better satisfying vehicle motion constraints.

The problem of effective use of Phasor Measurement Units (PMUs) to enhance power systems awareness and security is a topic of key interest. The central question to solve is how to use this new measurements to reconstruct the state of the system. In this paper we provide the first solution to the problem of (globally convergent) state estimation of multimachine power systems equipped with PMUs and described by the fourth order flux-decay model. This work is a significant extension of our previous result, where this problem was solved for the simpler third order model, for which it is possible to recover algebraically part of the unknown state. Unfortunately, this property is lost in the more accurate fourth order model, and we are confronted with the problem of estimating the full state vector. The design of the observer relies on two recent developments proposed by the authors, a parameter estimation based approach to the problem of state estimation and the use of the Dynamic Regressor Extension and Mixing (DREM) technique to estimate these parameters. The use of DREM allows us to overcome the problem of lack of persistent excitation that stymies the application of

Considering the issues of premature convergence and low solution accuracy in solving high-dimensional problems with the basic Chicken swarm optimization algorithm (CSO), an Adaptive simplified chicken swarm optimization algorithm based on inverted S-shaped inertia weight (ASCSO-S) is proposed. Firstly, a Simplified chicken swarm optimization algorithm (SCSO) is presented by removing all the chicks from the chicken swarm. Secondly, an inverted S-shaped inertia weight is designed and introduced into the updating process of the roosters and hens to dynamically adjust their moving step size and thus to improve the convergence speed and solution accuracy of the algorithm. Thirdly, in order to enhance the exploration ability of the algorithm, an adaptive updating strategy is added to the updating process of the hens. Simulation experiments on 21 classical test functions show that ASCSO-S is superior to the other comparison algorithms in terms of convergence speed, solution accuracy, and solution stability. In addition, ASCSO-S is applied to the parameter estimation of Richards model, and the test results indicate that ASCSO-S has the best fitting results compared with other three algorithms.

Aging and temperature changes in the passive components of an LCL-filter grid connected converter system (GCCs) may lead to parameter uncertainties, which can in turn influence its modeling accuracy for Finite-Control-Set Model Predictive Control (FCS-MPC). The presence of model errors will change the resonance point and deteriorate the power quality of the grid current, in turn degrading the active damping (AD) performance. in this situation, there is a serious possibility that the GCCs may malfunction and automatically disconnect from the grid, causing great challenges to the system stability. To solve this problem, firstly, prediction error analysis in FCS-MPC due to the model parameter errors is presented. Secondly, to achieve high accuracy and fast filter parameter estimation in utility, an adaptive online parameter identification method based on gradient descent optimization (GDO) has been proposed. Finally, to further reduce the searching time needed by the optimal iteration step, a variable iteration step searching method based on the RMSprop (Root-Mean-Square-Prop) gradient descent optimization (RMSprop-GDO) method is proposed. Experimental studies of an LCL-GCCs prototype in the laboratory have been conducted to validate the effectiveness of the proposed method.

In this paper, the distributed state and parameter estimation problem is addressed for a class of discrete-time nonlinear systems subject to sensor saturations over wireless sensor networks. In this regard, a novel estimation design is proposed that involves identification of an unknown parameter under event-triggering mechanism. A sufficient condition for exponential uniform ultimate boundedness of estimation error is established using Lyapunov stability theory. The estimator gains to ensure boundedness of error are linked to the solution of inequality constraints. Finally, the effectiveness of proposed estimator is demonstrated using a simulation example.

This article is concerned with modeling of a class of permanent-magnet-synchronous-motor-based drive systems with dead-zone input nonlinearity. The experimental testing method based on recursive least squares (RLS) is combined with the mechanism analysis method based on physical laws. With the structure of the analytical model first determined by the mechanism analysis method, an online RLS-based data-driven method is then exploited to obtain the unknown parameters of the analytical model. Since the unknown parameters are decoupled in the proposed method, all parameter estimates are available. An auxiliary model and a variable forgetting factor are constructed to ensure the high accuracy and the fast convergence of the proposed method, respectively. Besides, the asymptotic convergence property of the proposed method is analyzed, and the upper bound of the parameter estimation error is also presented. The effectiveness of the proposed method is verified by simulations and experiments on a self-designed computer numerical control engraving system.

This paper presents a fast phase-locking and amplitude detection scheme for single-phase applications. The proposed scheme employs a pseudo-rectification process in order to generate even harmonics from the grid signal. The DC-offset component present in the grid signal increases the sensitivity of the pseudo-rectification process, and the design of the pre-filtering stage must, therefore, be completely focused on the rejection of DC-offset and the even-harmonics. The application of a half-cycle pre-filter would consequently appear to be a highly attractive option as regards improving the dynamic response time and even harmonic rejection abilities. However, the elimination of the DC-offset component and the estimation of phase and amplitude information is still a very challenging task. The aforementioned issues are addressed by employing a half-cycle comb filter and a half-cycle non-adaptive Lyapunov orthogonal signal generator. The estimation of single-phase grid parameters is thereby made simpler by employing an improved open-loop frequency estimation, which is capable of providing accurate frequency deviation information with good precision. The research shown herein also reveals that the estimation of the amplitude and phase information differs slightly from the usual mathematical notations owing to the pseudo-rectification method adopted in the proposed work. Note that the pre-filtering stage is non-adaptive in nature, which may lead to steady-state errors in the amplitude and phase information under off-nominal frequency conditions. An online mathematical correction approach dependent on frequency deviation is, therefore, employed in order to obtain the error-free amplitude and phase information. Finally, the robustness of the current proposal is validated by experimentally comparing several known grid parameter estimation schemes using a real-time controller.

We consider the problem of tracking for a discrete-time plant with unknown plant parameters; we assume knowledge of an upper bound on the plant order, and for each admissible order we assume knowledge of a compact set in which the plant parameters lie. We carry out parameter estimation of an associated auxiliary model; indeed, for each admissible dimension, we cover the set of admissible parameters by a finite number of compact and convex sets and use an original-projection-algorithm-based estimator for each set. At each point in time, we employ a switching algorithm to determine which model and parameter estimates are used in the pole-placement-based control law. We prove that this adaptive controller guarantees desirable linear-like closed-loop behavior: exponential stability, a bounded noise gain in every p-norm, a convolution bound on the effect of the exogenous inputs, as well as exponential tracking for certain classes of reference and noise signals; this linear-like behavior is leveraged to immediately show tolerance to a degree of plant time-variations and unmodelled dynamics.

A feasible online test procedure is proposed in this paper to estimate the full set parameters of synchronous generators (SG). The recommended test procedure involves applying step perturbations to one of the power system stabilizer (PSS) inputs like active power or rotor speed, such that the stator and field winding signals change considerably, which makes them suitable for parameter estimation purposes. Furthermore, an accurate approach is suggested to estimate SG's parameters by utilizing the recorded data from the proposed test procedure. The effect of magnetic saturation, which can influence the estimation results, is also taken into account continuously during the parameter estimation. Finally, the proposed test is applied to a 200 MVA gas unit SG, and the parameters are estimated using a Genetic algorithm (GA) from the measurement data. The results show the effectiveness of the proposed method.

This article proposes a hybrid signal injection-based maximum torque per ampere (MTPA) control for dual three-phase permanent magnet synchronous machines (PMSMs). The proposed method consists of a real dc current injection in the harmonic subspace and a virtual ac current injection in the fundamental subspace. The real dc current injection in the harmonic subspace does not impact the average torque production, and induced losses are small enough to be neglected. In the meantime, to guarantee the accuracy of the proposed MTPA method, a novel machine parameter estimation scheme considering voltage source inverter nonlinearity effects is proposed in this article, which can track the variation of $q_{1}$ -axis inductance and resistance accurately. Therefore, the proposed method only requires command voltages and currents in the torque model, and the variations of machine parameters are compensated by the proposed parameter estimation scheme. As the command voltages have correspondence with the variations of machine parameters, the magnetic saturation and temperature effects have been taken into account. The proposed method is validated by simulations and experimental results under various torque, speed, and temperature conditions. Additionally, the accuracy of the proposed method is validated, and its performance is proven to be superior through comparison of the derived results with those obtained from various existing methods.

The design and analysis of active disturbance rejection control (ADRC) is considered for a globally Lipschitz nonlinear discrete-time system which is nonaffine to control inputs. A local dynamic linearization (LDL) is proposed to transfer the original nonaffined nonlinear system into a nonlinear system affine to control input such that the open problem of selecting a control gain in the feedback control law of ADRC is solved. Further, both a parameter updating law and an adaptive extended state observer are designed to estimate control gains and total uncertainty, respectively. The stability of ADRC for nonlinear nonaffine discrete-time processes is analyzed rigorously for the first time with the aid of contraction mapping principle. The proposed adaptive feedback controller considers desired reference trajectory, parameter adaption, error feedback and the compensation of total uncertainties caused by parameter estimation error and unknown nonlinearity simultaneously to achieve better control performance. The proposed method is nearly data driven since no other model information is needed except for the globally Lipschitz condition and the nonzero property of the partial derivative of the nonlinear system with respect to the control input. The theoretical result is verified by simulations.

This article presents a state-space model with time-delay to map the relationship between known input-output data for discrete systems. For the given input-output data, a model identification algorithm combining parameter estimation and state estimation is proposed in line with the causality constraints. Consequently, this article proposes a least squares parameter estimation algorithm, and analyzes its convergence for the studied systems to prove that the parameter estimation errors converge to zero under the persistent excitation conditions. In control system design, the U-model based control is introduced to provide a unilateral platform to improve the design efficiency and generality. A simulation portfolio from modeling to control is provided with computational experiments to validate the derived results.

How to effectively realize the simulation, evaluation, and control of the photovoltaic (PV) system established on the actual measured voltage and current PV cells and module data has attracted widespread attention. The original SE possesses the disadvantages of slow convergence and poor accuracy in the parameter identification of PV cells and modules. This paper proposes an enhanced spherical evolution algorithm (SE) based on a novel dynamic sine-cosine mechanism (DSCSE). The introduction of the dynamic sine-cosine mechanism significantly promotes the information communication of disparate individuals and increases the diversity of diverse populations. To assess the performance of DSCSE, it is compared with ten comparative algorithms to estimate unknown parameters of PV cell and module at fixed and varying temperature and light conditions, including single diode model (SDM), double diode model (DDM), three diode model (TDM) and PV module. The experimental results indicate that the root mean square of the error (RMSE) gained by DSCSE outperforms most competing algorithms. The results of RMSE by DSCSE for SDM, DDM, and TDM of commercial solar cells R.T.C. France and PV module of Photowat-PWP201 is the percentage of improvement of 48.45%, 6.85%, 11.81%, and 4.73% compared to SE, respectively. Furthermore, for three manufacturers, including Mono-crystalline (SM55), Thin-film (ST40), and Multi-crystalline (KC200GT), the results of RMSE by DSCSE harvest the maximum and minimum increase of 96.1% and 31.36%. Therefore, DSCSE is expected to become a novel promising technology to estimate the parameter of PV cells and modules.

In this article, the improved chaotic electromagnetic field optimization (ICEFO) algorithm is adopted to generate the optimal values of unknown parameters of fuel cells (FCs). The mathematical model of polymer electrolyte membrane FC (PEMFC) considers nonlinear and complex optimization problems with different control variables. The sum of squared error between the measured and computed stack voltages is considered as the main objective function. The performance of the ICEFO algorithm is tested on three PEMFC stacks. Moreover, sensitivity and statistical measures are presented to confirm the reliability and accuracy of ICEFO. In addition, the effect of changing the cell temperature and reactants pressures is studied for more validation of ICEFO. Furthermore, the results obtained by ICEFO are competitive compared with other optimization methods. These results confirm the effectiveness of ICEFO in solving the optimization problem of PEMFC parameter estimation. Finally, based on the optimization results a Simulink model for PEMFC is developed to examine the dynamic performance of the PEMFCs.

The single-diode model (SDM) is widely used in photovoltaic (PV) system modeling and output power estimation because of its simplicity and accuracy. This article proposed an improved and comprehensive method for parameter identification and performance estimation by using manufacturer datasheet information for PV module. The temperature dependence of all physical parameters in SDM of PV module is comprehensively considered and investigated for varying operating conditions. In the proposed method, all physical parameters under reference condition and their temperature coefficients are calculated from information in manufacturer datasheet, which can be used for parameter calculation and output power estimation under varying operating conditions. To improve the accuracy and efficiency, the analytical and optimization methods are combined in calculation procedure of parameter extraction. By using analytical derivation from I–V equations at key operating points and their temperature coefficients, it reduces the dimensions of the search space and save computational cost for parameter optimization. The accuracy and effectiveness of the proposed method are validated by massive experimental data under varying operating conditions for different type PV modules. The proposed processes are simple and especially useful to calculate the actual performances of PV modules under operating conditions, making it scalable for direct online application.

Low-inertia microgrids may easily have a single load that can make up most of the total load, thereby greatly affecting stability and power quality. Instead of a static load model, a dynamic constant power load (DCPL) model is considered here. Next, a hybrid algorithm for parameter estimation (HAPE) is introduced. In order to verify the load model and the HAPE, two experiments are conducted with different DCPLs using a power-hardware-in-the-loop (PHiL) testbed. The PHiL testbed consists of a real-time computer working with a programmable power amplifier in order to perturb the input voltage's amplitude and frequency. Each connected DCPL in two separate experiments serves as the device under test. Using the captured experimental data as a reference, the HAPE is then invoked. The resulting parameter estimates are used to define simulation models. Both resulting DCPL models are simulated to produce waveforms that closely resemble experimental waveforms. Finally, the HAPE's resulting parameter estimates are presented, and the performance of the HAPE is discussed.

This paper focuses on presenting a new identification algorithm to estimate the parameters and state variables for two‐input two‐output dynamic systems with time delay based on canonical state space models. First, the related input‐output equation is determined and transformed into an identification oriented model, which does not involve in the unmeasurable states, and then a residual based least squares identification algorithm is presented for the estimations. After the parameters being estimated, the system states are subsequently estimated by using the estimated parameters. Through theoretical analysis, the convergence of the algorithm is derived to provide assurance for applicability. Finally, a selected simulation example is given for a meaningful case study to show the effectiveness of the proposed algorithm.

This paper presents a novel adaptive RISE (robust integral of the sign of the error) control method for the asymptotic rigid-body attitude tracking with inertia uncertainties and bounded unstructured external C2 disturbances. By virtue of a specially constructed synthesis of modified dynamic scaling mechanism, integration by parts, and finite escape analysis, the new design provides a simple but intuitive solution to the long-standing open problem in almost all RISE-based control schemes, that is, establishing global asymptotic tracking results without information about exact bounds of the external disturbances and its derivatives. Besides significant improvement on robustness, the proposed adaptation mechanism itself can also be regarded as a new kind of non-CE (non-certainty-equivalent) adaptive formulation, where very few extra dynamic extensions are required to largely recover ideal parameter estimation performance in commonly seen non-CE designs. The structure of the proposed controller is also equivalent to an adaptive feedforward compensator plus a stable low-pass filter whose input contains high gain feedback components, which allows flexible adjustments to establish ideal transient response and avoid chattering. Numerical simulations demonstrate that the above unique features of the proposed method lead to notable advantages over the composite adaptive RISE method.

This article addresses the problem of controlling discrete-time linear time-invariant systems with parametric uncertainties in the presence of hard state and input constraints. A suitably designed gradient-descent-based indirect adaptive controller, used to handle parametric uncertainties, is combined with a model predictive control (MPC) algorithm, which guarantees constraint satisfaction. An estimated model of the actual uncertain plant is used for predictions of the future states. The parameters of the estimated model are updated using a gradient-descent-based adaptive update law. The errors arising due to the model mismatch between the estimated plant model and the actual uncertain plant are accounted for using a constraint tightening method in the MPC algorithm. The proposed adaptive MPC strategy is proved to be recursively feasible and the closed-loop system is proved to be bounded at all instants and asymptotically converging to the origin.

Flight testing to characterize aircraft stability and control is a complex and expensive task that involves restrictive practical constraints. Methods are examined for optimizing the inputs applied to aircraft controls during flight test maneuvers to produce informative flight data. Theory underpinning optimal input design for stability and control flight testing is explained. The merits and limitations of optimal inputs are discussed, along with optimization approaches and practical considerations involved in applying optimal inputs in a real flight test environment. Simulated and flight test case studies of optimal multiple-input design for aircraft stability and control flight testing are presented and discussed.

In processes with slow dynamics or subjected to frequent disturbances, the detection of a steady-state operation might be rare, which hinders the application of the classic RTO. To overcome this issue, the Hybrid RTO (HRTO) is a proposition where a dynamic observer substitutes the Steady-State Detection (SSD) and the static parameter estimation steps. Recent progress has been made in HRTO frameworks. Still, all of them start from the assumption that a reliable mechanistic dynamic model is available, which is not true in many applications. In fact, the development of a dynamic process model is not considered in most typical RTO design projects. Therefore, the present work proposes an HRTO framework aiming, mainly, to overcome this first assumption. We present a complete control framework where the basic assumption is that a reliable mechanistic dynamic model is not available, but a static process model is at hand. The HRTO is made possible by using an approximate dynamic model that takes advantage of the static process model and identified linear dynamics from the plant in a Hammerstein structure. Three models are proposed based on the Hammerstein structure. These models are used in the dynamic observer, a proposed variation of an Extended Kalman Filter (EKF), and as the controller’s internal model. The economic objectives are introduced into the control layer by the self-optimizing variables in an adaptive infinite-horizon MPC formulation. The framework presents full model compatibility between the observer, controller, and optimizer layers. A validation case study is developed in the Williams–Otto reactor with two proposed EKF tunings. The open-loop results provide evidence that the proposed Hammerstein structure is adequate as an approximate dynamic model, preserving the observability characteristics of the static model. The closed-loop results show that our framework outperforms the classic RTO structure in economic return, especially in the transient regions. Moreover, the proposed approach has a very natural way of handling active constraint changes. This effect is shown in the results at the moment that a constraint becomes active. The economic performance of the RTO is impaired compared to our proposed HRTO scheme. Finally, the average computational cost of each iteration of our framework is twice as fast as the classic RTO framework, which suggests the potential applicability of the proposed methodology on an industrial scale.

A novel approach to solve the problem of distributed state estimation of linear time-invariant systems is proposed in this article. It relies on the application of parameter-estimation-based observers, where the state observation task is reformulated as a parameter estimation problem. In contrast with existing results, our solution achieves convergence in finite time, without injection of high gain, and imposes very weak assumptions on the communication graph—namely, the existence of an open Hamiltonian walk. It is shown that this assumption is strictly weaker than the usual strong connectivity requirement. The scheme is shown to be robust vis-a-vis external disturbances and communication delays.

Online identification of a battery model can capture the parameter variations in real time, thus, providing an accurate model under various conditions, such as a wide temperature range, different aging degree in electric vehicle application. However, the measurement noise and sampling asynchronization of voltage and current are usually inevitable in the battery management system (BMS), which can lead to a large identification error. In this article, the mechanism of sampling asynchronization and measurement noise is analyzed first, and then the identification sensitivity analyses on noise and sampling asynchronization are carried out. Simulation results indicate that even a small fluctuation as small as 5 mV or the sampling latency between voltage and current within 10 mS can cause relatively large identification errors, especially for polarization parameters. In order to guarantee the online identification accuracy and robustness in the BMS application, an improved robust recursive least-squares (RLS) algorithm with adaptive outlier boundary is proposed to eliminate the input outlier caused by sampling latency and suppress the effect of measurement noise utilizing the bias compensation method. The experimental results demonstrate that the proposed method can provide sufficient accuracy and robustness under noise interference and sampling latency in BMS application.

Optimization methods in cyber-physical systems do not involve parameter uncertainties in most existing literature. This paper considers adaptive optimization problems in which searching for optimal solutions and identifying unknown parameters must be performed simultaneously. Due to the dual roles of the input signals on achieving optimization and providing persistent excitation for identification, a fundamental conflict arises. In this paper, a method of adding a small deterministic periodic dither signal to the input is deployed to resolve this conflict and provide sufficient excitation for estimating the unknown parameters. The designing principle of the dither is discussed. Under dithered inputs, the authors show that simultaneous convergence of parameter estimation and optimization can be achieved. Convergence properties and convergence rates of parameter estimation and optimization variable updates are presented under the scenarios of uncertainty-free observations and systems with noisy observation and unmodeled components. The fundamental relationships and trade-off among updating step sizes, dither magnitudes, parameter estimation errors, optimization accuracy, and convergence rates are further investigated.

The rotor flux linkage of permanent-magnet synchronous motor (PMSM) is of great importance for monitoring and high-performance control. However, most of the existing online estimation methods need prior knowledge of other PMSM parameters such as stator resistance and dq axis inductance, which are difficult to realize in the rank-deficient models of electrical machines. This article proposes a simple online estimation method of rotor flux linkage for the dual three-phase surface-mounted PMSM (SPMSM) drives with the assistance of injected position offset. The independent estimation model has been developed for the rotor flux linkage by appropriate setting of positon offset between two sets of three-phase windings of dual three-phase SPMSM drives. The proposed method not only has little influence on the output torque and high signal-to-noise ratio for observation, but also offers the superior performance in estimation accuracy irrespective of the influence of VSI nonlinearity. In addition, the proposed position-offset-based method is suitable for the full-speed region including the field-weakening operation. Finally, comprehensive experimental results are presented to verify validity of the proposed method under different working conditions.

Degradation data have been widely used for the remaining useful life (RUL) prediction of systems. Most existing works apply a preset model to capture the degradation process and focus on the degradation process without shocks or constant shock effects. More generally, the actual degradation path is unobservable due to the existence of measurement uncertainty, which interferes with the determination of the degradation model. Besides, the effect of random shocks is usually fluctuating. Given these problems, a general degradation model with the random shock fluctuant effects considering the measurement uncertainty is first developed to describe the degradation process, and a two-step approach combining the arithmetic average filter and the Bayesian information criterion is adopted to identify the degradation path. Subsequently, the transfer processes of the actual degradation state and the abrupt change caused by shocks are depicted using a two-dimensional state-space model, and an expectation-maximization algorithm combined with the particle filtering is developed for parameter estimation. Furthermore, the explicit solution of RUL distribution is obtained when only considering harmful shocks, while a simulation method of RUL distribution is provided when both harmful and beneficial shocks exist. Finally, the effectiveness of the proposed method is verified by a numerical example and two practical case studies.

Thispaper addresses the problem of carrying an unknown nonuniform payload by multiple quadrotor agents. The load is modeled as a rigid body with unknown weight and position of Center of Gravity (CG) for the agents, and is included in their dynamic equations of motion. The agents and the load are assumed to be connected to each other by taut ropes. The Udwadia–Kalaba equation is used to calculate the constraint forces on the ropes acting on each quadrotor. Inner-loop and outer-loop controllers for quadrotors position and attitude control are presented. For the outer loop, an estimation algorithm based on the invariance and immersion adaptive control is utilized to estimate the unknown physical parameters of the payload including mass and CG position without using multi-axes force/torque sensors. The inner-loop controller employs an adaptive controller. Simulation results, for two and four agents carrying a nonuniform rod and cubic payload, show the effectiveness of the proposed algorithm. A case study is also performed to investigate the effect of quadrotors positioning on flight endurance of the cooperative aerial team carrying a nonuniform payload.

Most control systems require a fairly accurate model of dynamic system to design or implement a controller. When system dynamics change, the dynamic model must undergo online or offline re-estimati...

In view of the high difficulty in coupling of various electric vehicle parameters, intractable parameter estimation, and unreasonable distribution of vehicle driving torque, the four-wheel hub motor is applied to drive electric vehicles, which can instantly obtain the torque and speed of the hub motor and achieve precise control of the torque of each wheel. According to the vehicle longitudinal dynamics model, a progressive RLS (PRLS) algorithm for real-time estimation of vehicle mass and road gradient is proposed. Meanwhile, by means of taking the longitudinal acceleration of the vehicle and the road gradient obtained from the estimation algorithm as the parameter of the torque distribution at the front and rear axles, a dynamic compensation and distribution control strategy of the front and rear axle torques is designed. Moreover, based on hardware-in-the-loop real-time simulation and real-vehicle tests, the effectiveness of the proposed estimation algorithm and the rationality of the real-time distribution control strategy of driving torque are verified.

In this paper, an iterative method to identify first-order plus dead-time (FOPDT) models is proposed. The proposed method uses two frequency points as equality constraints to estimate the model parameters using the least-squares identification algorithm. The time delay term is approximated by a second-order Taylor expansion. The iterative method is applied to reduce the effect of errors caused by the time delay approximation, thus improving the model estimate. Simulation examples are used to illustrate the effectiveness and robustness of the proposed iterative procedure for parameter estimation. In addition, experimental results from a thermoelectric process illustrate the practical applicability of the proposed iterative procedure.

This paper presents brain emotional controller (BEC)-based adaptive mechanism in Model Reference Adaptive System (MRAS) to control sensorless permanent magnet synchronous motor (PMSM) drive. BEC is a bio-inspired intelligent controller developed using neurobiological connections of mammalian brain by inspiring limbic system. The developed controller is applied as speed controller and parameter estimation (i.e. rotor speed and rotor position) of PMSM drive. The proposed BEC stability is proved to guarantee convergence characteristics. The performance of BEC-based adaptive mechanism is evaluated in simulation environment, and obtained results are compared in real time in hardware-in-loop (HIL) environment. In order to hold the effective performance of the proposed method compared with fuzzy logic controller with similar tests, further integral performances are evaluated for BEC and fuzzy logic controllers. Results demonstrated in HIL and in simulation show applicability, robust and sensitiveness of the proposed BEC method.

In this paper the solution was proposed for the state variables estimation problem in the mathematical model of the DC switch-mode power converter built according to the Ćuk scheme. Pulse converters are one of the main components of most modern electrical devices and the circuit proposed by Slobodan Ćuk in the 70s of the 20th century is still relevant and demanded. Traditionally, PI (proportional-integral) controllers or proportional-integral adaptive control algorithm (PI-PBC), based on passification methods and superior to standard PI controllers in accuracy, are used as the control algorithm for power converters. However, you need to know the entire vector of the state variables of the converter to build a PI-PBC controller, and moreover, all its parameters must be accurately known. Unfortunately, in practice, such assumptions are not fulfilled, since parametric drifting is possible, and measurements of the converter’s state require additional sensors, which in some cases does not justify itself. Thus, there is a need to develop additional observers or estimators that allow obtaining data on all variables of the converter, as well as its parameters. The solution is based on the GPEBO method (generalized parameter estimation-based observers). The problem was solved under assumption that only the output signal (the output voltage of the converter) is measurable and some of the mathematical model parameters are unknown. An important aspect of the observer design is the development of an algorithm for unknown parameters and the state vector of a mathematical model estimation that ensures convergence in a finite time. Finite-time convergence is extremely important when designing observers since transients in pulse converters occur very quickly.

The heat distribution is one of the key factors which affects the performance of the atomic sensor. Analysis of component temperature is indispensable when determining the need for heat preservation or cooling. Finite element method (FEM) is a commonly thermal analysis method that is widely used in many fields and finite element analysis software is also an essential tool in various thermal analysis cases. Both need accurate parameters to solve the thermal problems. However, some parameters are difficult to be measured in the atomic sensor. To obtain more accurate parameters, a heat transfer model based on the thermal network method is available for parameter estimation that only needs simple conditions to solve problems, which can be measured by experiment. Based on the classical heat transfer theory and thermal network method, the heat transfer model is developed to analyze the heat distribution in the atomic sensor and achieve the parameter estimation of FEM. The model accuracy has been validated by the finite element analysis software and experimental results. The contour maps of the temperature are obtained by finite element analysis software. It is shown that the maximum error of parameter estimation is within 7.8% and the optimal results can achieve 2.1%. The heat transfer model based on the thermal network method can obtain the parameters that can’t be measured by experiment under limited boundary conditions, effectively achieve parameter estimation, and make the simulation results closer to the experiment. With the parameters from heat transfer model, the results of FEM become more accurate.

Photovoltaic (PV) systems are more efficient when operated in its maximum power point (MPP). Even with the existence of high number of MPP tracking (MPPT) techniques, it is possible to improve the MPPT efficiency. One way to achieve this improvement is to use model-based MPPT techniques, since they can estimate the MPP with reasonable accuracy, accelerating the MPP search process. Based on this motivation, the main objective of this article is to propose an efficient double-diode model-based + perturb and observe (PO a parameter estimation technique that uses a combination of analytical equations and pattern search optimization algorithm is also presented for this unique model; and an irradiance estimation algorithm is also proposed, avoiding the irradiance sensor usually needed in model-based and hybrid MPPT techniques. Experimental results demonstrate that the integration of the model, the irradiance estimator, and reduced step P&O algorithm is an effective hybrid MPPT solution for PV systems.