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In this paper, the leader-following consensus problem is investigated for heterogeneous switched nonlinear multi-agent systems with switching topologies. First, because the whole state information cannot be obtained, two adaptive observers are, respectively, developed to accurately estimate the switched system states and the state of the leader for each follower. Then, a novel adaptive controller that employs these observers is developed for the heterogeneous switched nonlinear multi-agent systems, and the problems regarding switched systems and the switching topologies can be solved in the process of the adaptive controller design. Moreover, the switching rule of the switched systems satisfies the average dwell time as well as the assumed conditions of the communication topologies by only directing routes to all followers from the leader. Finally, a simulation analysis is conducted to demonstrate the validity of the presented methods. [ABSTRACT FROM AUTHOR]

An adaptive observer for frequency and magnitude of two-phase symmetrical sinusoidal signal is presented. It is designed based on control concept of internal model approach. The observer guarantees global exponential estimation and high performance. If the information about one of the two-phase signal components is missing, a modified structure of the observer is proposed. This case is equivalent to the measurement of a single-phase sinusoidal signal. The properties of local exponential stability of single-phase signal observer are proved using Lyapunov’s conversion theorem for disturbed systems. The robustness of the two-phase signal observer with respect to additive disturbances (highfrequency noise, varying frequency) is confirmed by simulation results. It is shown that the observer estimation speed can be arbitrarily increased by increasing the feedback gains. A method for single-phase harmonic signal frequency observer tuning is developed, which ensures the expansion of the stability region. [ABSTRACT FROM AUTHOR]

With unknown leader parameters and false or immeasurable tracking paths, the leader–follower tracking problem via adaptive control becomes more complex and challenging. Different from the existing work in the literature and in line with real-world applications, this challenging issue is solved in this paper for multi-agent networks with different kinds of unknown uncertainties in the followers such as partially known parameters, nonlinearities, external disturbances, communication weights, and sensor faults. The solution of this challenging issue is obtained in three steps. Firstly, the tracking path is estimated by designing distributed adaptive observers at all agents that have direct communication edges with the leader. Next, by designing local adaptive observers for all followers, the estimates of all followers' states can be obtained. Then, distributed adaptive controllers for all followers are designed using the estimated states of both followers and their neighbors in addition to the estimated tracking path. The tracking performances are provided via sufficient conditions for both the adaptive observers and the adaptive controllers. The proposed methods are evaluated through two examples that are commonly used in the literature. The obtained results prove the efficacy of the recommended adaptive approaches. • The leader–follower tracking problem for general nonlinear MASs is solved with an uncertain leader and false tracking paths. • Local-based adaptive observers with partially known system parameters, disturbances, and sensor faults are investigated. • The weighted graphs with unknown weights for nonlinear MASs described by different dynamics are considered. • The asymptotic stabilities of the estimation and the tracking errors are guaranteed based on the Lyapunov stability method. • The proposed approach is examined efficiently by applying it to a numerical example plus a group of Volkswagen GOLF V. [ABSTRACT FROM AUTHOR]

This paper presents a scheme to design an adaptive observer for uncertain nonlinear descriptor systems where both output disturbances and uncertainties are considered. The aim is to estimate the sensor and actuator faults and the state vector simultaneously. This study considers multiplicative actuator faults, and both actuator faults and sensor faults can be time-varying or abrupt. The stability conditions of the observer are derived based on the Lyapunov approach. These conditions are expressed as linear matrix inequalities (LMIs) without incorporating the Lipschitz constant in the LMIs. Therefore, the value of the Lipschitz constant does not restrict the method, and it can be unknown. By adding a proportional term to the fault estimator, the accuracy of fault estimation is improved. Two examples are presented to show the efficiency of the proposed observer for both normal state-space systems and descriptor systems. [ABSTRACT FROM AUTHOR]

In this article, a joint adaptive high‐gain observer design method is proposed for a class of nonlinear systems subject to sampled output data measurements. The considered class of system is characterized by a nonlinear term coupled with an unknown parameter that enters the system in both the outputs and the states equations. The fact that the considered system involves an output sampling process and an unknown parameter renders the design of the nonlinear adaptive observer more difficult. To overcome this difficulty, a novel closed‐loop output predictor is proposed. Based on the well‐known decoupling method between the state and unknown parameters, a joint adaptive high‐gain observer which can simultaneously guarantee the exponential convergence of the estimation of the unknown state and the unknown parameter is proposed in this article. The structure of the designed observer has been extended to the case of sampled and delayed data measurements. The effectiveness of our proposed observer is demonstrated through numerical simulations and performance comparison with another observer structure proposed in the literature. [ABSTRACT FROM AUTHOR]

This work addresses the problem of adaptive observer design for nonlinear systems satisfying incremental quadratic constraints. The output of the system includes nonlinear terms, which puts an additional strain on the design and feasibility of the observer, which is guaranteed under the satisfaction of an LMI, and a set of algebraic constraints. A particular case where the output nonlinearity matches the unknown parameter coefficient is also discussed. The result is illustrated through a numerical example for the chaos synchronization of the Rössler system.

Degenerative meniscus tissue has been associated with a lower elastic modulus and can lead to the development of arthrosis. Safe intraoperative measurement of in vivo elastic modulus of the human meniscus could contribute to a better understanding of meniscus health, and for developing surgical simulators where novice surgeons can learn to distinguish healthy from degenerative meniscus tissue. Such measurement can also support intraoperative decision-making by providing a quantitative measure of the meniscus health condition. The objective of this study is to demonstrate a method for intraoperative identification of meniscus elastic modulus during arthroscopic probing using an adaptive observer method. Ex vivo arthroscopic examinations were performed on five cadaveric knees to estimate the elastic modulus of the anterior, mid-body, and posterior regions of lateral and medial menisci. Real-time intraoperative force–displacement data was obtained and utilized for modulus estimation through an adaptive observer method. For the validation of arthroscopic elastic moduli, an inverse parameter identification approach using optimization, based on biomechanical indentation tests and finite element analyses, was employed. Experimental force–displacement data in various anatomical locations were measured through indentation. An iterative optimization algorithm was employed to optimize elastic moduli and Poisson's ratios by comparing experimental force values at maximum displacement with the corresponding force values from linear elastic region-specific finite element models. Finally, the estimated elastic modulus values obtained from ex vivo arthroscopy were compared against optimized values using a paired t-test. The elastic moduli obtained from ex vivo arthroscopy and optimization showcased subject specificity in material properties. Additionally, the results emphasized anatomical and regional specificity within the menisci. The anterior region of the medial menisci exhibited the highest elastic modulus among the anatomical locations studied (9. 97 ± 3. 20 MPa from arthroscopy and 5. 05 ± 1. 97 MPa from finite element-based inverse parameter identification). The paired t-test results indicated no statistically significant difference between the elastic moduli obtained from arthroscopy and inverse parameter identification, suggesting the feasibility of stiffness estimation using arthroscopic examination. This study has demonstrated the feasibility of intraoperative identification of patient-specific elastic modulus for meniscus tissue during arthroscopy. • Estimation of elastic moduli during arthroscopy using an adaptive observer method. • Arthroscopic elastic moduli were validated against moduli based on indentation test. • Meniscus shows subject specificity, as well as anatomical and regional differences. • Provide quantitative measure of meniscus health conditions. • Enable in vivo patient-specific measurement of meniscus elastic moduli. [ABSTRACT FROM AUTHOR]

In this article, through a combination of the deterministic learning (DL) method and the adaptive high gain observer (AHGO) technology, a fault identification approach for a class of nonlinear systems in canonical form is proposed. By using the DL method, the partial persistent excitation condition of the identification system is satisfied, and then, the AHGO technology is exploited to estimate the states and the neural network weights simultaneously. To analyze the convergence of the proposed method, we first analyze the uniformed completely observability (UCO) property of the linear part of the nonlinear identification system. Then, by using the Lipschitz property of the nonlinear item and the Bellman–Gronwall lemma, we show that the UCO property of the nonlinear identification system is depended on the UCO property of the linear part when the observer gain is chosen large. Therefore, by using the UCO property of the nonlinear identification system and the Lyapunov stability theorem, the convergence of the proposed learning observer is proven. The attraction of this article is based on the analysis of the UCO property of the identification system, and the convergence of the proposed learning observer can be directly proven. The simulation example is given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, a distributed adaptive event-triggered formation control strategy is proposed for unified nonlinear heterogeneous multiagent systems under uncertainties and disturbances to achieve time-varying formations. To reduce the frequency of data transmission, a distributed dual adaptive observer with an event-triggered strategy is developed to estimate the states of a reference exosystem. Without incurring prior global information about a communication graph, a novel robust formation controller, with dynamic distributed compensators for uncertainties and disturbances, is designed based on an observer result and a nonlinear internal control principle. Finally, both simulation and experiment are conducted for tracking and patrolling formation to verify the effectiveness of the proposed formation control strategy and its robustness. Note to Practitioners—This article addresses the collaborative formation problem of multiagent systems that has potential applications in transportation and disaster relief. The design of robust and energy-saving coordination strategies is challenging in heterogeneous multivehicle systems. The proposed distributed method is suitable for large-scale uncertain heterogeneous systems, and the use of the dual adaptive event-triggered strategy reduces the data transmission rate. [ABSTRACT FROM AUTHOR]

A flux observer for induction motors which is adaptive to the active rotor resistance variations is presented. Due to the added fluxes overestimation in the observer structure, the global exponential stability properties of the current and flux vector components and active rotor resistance estimation are ensured under conditions of persistency of excitation. The proposed observer has a simpler structure compared to existing solutions with the global stability properties. The simulation results of the observer dynamic performance investigation confirm its effectiveness. It is shown that even if the conditions of persistency of excitation are not met, the active rotor resistance is estimated correctly; consequently, the designed observer can be implemented as an identification algorithm in self-commissioning systems of induction electric drives. References 9, figures 5. [ABSTRACT FROM AUTHOR]

To improve the sensorless control performance of electrical drive systems, a nonsingular terminal sliding mode observer (NTSMO) and adaptive observer are proposed to solve the chattering and phase delay problems. Firstly, by defining a new nonsingular terminal sliding mode surface, the sliding mode observer based on the fast reaching law is designed to estimate the back electromotive force (EMF). The observer enhances the robustness and system performance eliminates the singularity and attenuates the chattering. Next, to obtain the accurate back-EMF signal, an adaptive observer is designed instead of a traditional low-pass filter to filter out the harmonics. The adaptive observer can avoid the phase delay problem and further improve the signal observation accuracy. Then, the rotor position and speed information are accurately tracked. The proposed method is applied to the speed control system of a permanent magnet synchronous motor (PMSM), and the effectiveness and feasibility of the proposed sliding mode observer are demonstrated by the experiment. [ABSTRACT FROM AUTHOR]

This article presents an active vibration control of seismically excited building structures. The control scheme is based on active disturbance rejection control, which is an attractive alternative technique for structural vibration suppression and practical motion control solution in the presence of parametric uncertainties and disturbances. The proposed active disturbance rejection control scheme uses a generalized proportional integral observer, which allows us to estimate in real time the unknown dynamics and disturbances in the building structure to cancel their effect using a part of the control signal. First, the active disturbance rejection control provides a proportional derivative controller with robustness to external disturbances and uncertainties, and its structure is expressed in a compact error-based form. An important advantage with respect to other methods is that the proposed scheme does not need the system parameters. Moreover, supposing that displacement and velocity cannot be measured directly, an online robust adaptive observer is introduced to estimate both data, required for the proportional derivative controller. The adaptive observer removes constant disturbance and attenuates measurement noise in acceleration data. Under this line, a second active disturbance rejection control scheme is introduced based on a proportional controller that, unlike proportional derivative, it only needs the velocities that can be directly estimated by integrating the acceleration signals and does not require the adaptive observer. An advantage of this scheme is its simplicity to be implemented because it only needs to tune the proportional gain. Furthermore, this scheme has a similar performance of the proportional derivative controller. The effectiveness of the proposed active disturbance rejection control schemes is demonstrated through experimental results of a reduced scale five-story building structure. The results are found to be a good step in that direction, confirming that the proposed method is promising for practical applications. [ABSTRACT FROM AUTHOR]

In this article, we consider the formation tracking problem of nonholonomic multiagent systems only using relative bearing measurements between the agents. Such a practical and important yet challenging issue has been taken into limited consideration by existing approaches, which usually requires additional measurements such as relative positions. The contributions of this article are two-fold. First, a fully distributed reference velocity estimator is proposed. Under the proposed adaptive estimator, each agent can estimate the time-varying reference velocity asymptotically. Second, an input-to-state stable controller is designed according to the bearing rigid theory. Under the proposed controller, the formation with bearing-only constraints can be achieved. Finally, the proposed scheme is demonstrated and its effectiveness is verified by presenting some simulation and experimental tests. [ABSTRACT FROM AUTHOR]

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To realize the high-performance control of a servo system, parameter accuracy is very important for the design of the controller. Thus, the online parameter identification method has been widely researched. However, the nonlinearity of the inverter will lead to an increase in resistance identification error and the fluctuation of inductance identification results. Especially in the low-speed region, the influence of the inverter is more obvious. In this paper, an offline neural network is proposed considering the parasitic capacitance to identify the nonlinearity of the inverter. Based on the Kirchhoff equation in the static state of the motor, the nonlinear voltage equation is established, and the gradient direction of the weight coefficients has been re-derived. Using the gradient descent method, the identification error can converge to zero. Moreover, the d-axis voltage equation is established considering the nonlinearity of the inverter and an online adaptive observer was proposed. Based on the Lyapunov equation, the adaptive laws are derived. Further, the decoupling of the deadtime voltage and resistance voltage is realized by using the result of neural network identification. With the proposed algorithm, nonlinear identification of the inverter characteristics is realized, and the resistance and inductance identification accuracy in the low-speed region is improved. The effectiveness of the proposed methods is verified through experimental results. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive current control (MPCC) method based on current augmenter prediction model (CAPM) appropriate for interior permanent magnet synchronous motor (IPMSM) under high-speed condition. The accuracy of CAPM is improved by considering rotor movement during one control period especially under high-speed condition. Meanwhile, the use of the permanent magnetic flux linkage is eliminated. In order to further improve the robustness of the MPCC method, an adaptive observer based on CAPM is designed to estimate the model error. The proposed adaptive observer also eliminates the use of the permanent magnetic flux linkage, so the whole implementation process of the MPCC method with adaptive observer is independent of the permanent magnetic flux linkage. Moreover, since the adaptive observer with fixed adaptive integral gain cannot achieve satisfactory performance under different operating conditions of the motor, this paper designs a calculation method for the variable adaptive integral gains by considering the current estimation error, the amplitude of current and the angular velocity of the IPMSM. Finally, the proposed method is validated by means of the experimental results on a 20-kW test platform. [ABSTRACT FROM AUTHOR]

In this article, we propose to determine the dynamic model of a squirrel-cage induction motor from a reduced amount of information. An adaptive observer is also built from this model in order to obtain a speed estimation and to perform rotor fault monitoring by Tacholess Order Tracking (TOT). We also propose a generalization of the notion of angular sampling in order to adapt to this type of defect. The procedure is validated in the laboratory on a test bench dedicated to the study of rotor bar defects. [ABSTRACT FROM AUTHOR]

This brief presents a novel one-step adaptive parameter estimation framework for identification of unknown asymmetric dead-zone characteristic parameters (e.g., width and slopes) in the sandwich systems, which avoids using the intermediate variables or carrying out a two-step recursive identification procedure. By applying a continuous piecewise linear neural network (CPLNN), the asymmetric dead-zone nonlinearities can be represented into a parameterized form, where the dead-zone characteristic parameters can be derived based on the online updated CPLNN weights. Moreover, after representing the sandwich system into an augmented system, an adaptive observer is designed to reconstruct the immeasurable internal variables, and an adaptive law with guaranteed convergence is used to online update the CPLNN weights so as to estimate the dead-zone parameters. In this adaptive law, the parameter estimation error information is extracted using simple filters and introducing several auxiliary variables. It is proven that the observation error and estimation error both converge to a small set around zero. The efficiency of the estimation method is exemplified via practical experiments on an $X$ - $Y$ positioning platform. [ABSTRACT FROM AUTHOR]

In this article, a reinforcement learning (RL)‐based robust control strategy is proposed for uncertain heterogeneous multi‐agent systems to achieve optimal collision‐free time‐varying formations. Without using any global information, a fully distributed adaptive observer is developed to estimate both dynamics and states of the reference and disturbance systems. The observer parameters are found by an observed model‐based or a model‐free off‐policy RL algorithm. Using the internal model principle, a novel optimal robust formation control strategy is developed based on another proposed off‐policy RL algorithm. The algorithm addresses the nonquadratic optimization problem when the system model is completely unknown. Taking the bushfire edge tracking and patrolling task for an unmanned aerial vehicle‐unmanned ground vehicle heterogeneous system as an example, the effectiveness and robustness of the developed control strategy are verified by simulations. [ABSTRACT FROM AUTHOR]

This article proposes an observer-based methodology to detect and mitigate false data injection attacks in collaborative dc microgrids. The ability of observers to effectively detect such attacks is complicated by the presence of unknown nonlinear constant power loads. This article determines that, in the presence of unknown constant power loads, the considered attack detection and mitigation problem involves nonlinearities, locally unobservable states, unknown parameters, uncertainty, and noise. Taking into account these limitations, a distributed nonlinear adaptive observer is proposed to overcome these limitations and solve the concerned observation problem. The necessary conditions for the stability of the distributed scheme are found out. Moreover, numerical simulations are performed and then validated in a real experimental prototype, where communication delay, uncertainty, and noise are considered. [ABSTRACT FROM AUTHOR]

Summary: In this article, a joint adaptive observer design method is proposed for a class of affine nonlinear systems subject to sampled output data measurements. The considered class of system contains nonlinear terms which depend on unknown parameters. The considered unknown parameters enter the system in both the output and the system states equations which render the design of the sampled data observer for the affine nonlinear system more difficult to conceive. To solve this problem, and based on a new method of decoupling parameter estimation and state observation, a new online output sampling joint adaptive observer is proposed in this article, which can simultaneously guarantees the exponential convergence of the estimation of unknown state and parameter. The structure of the proposed observer has been extended to the case of sampled and delayed data measurements. To illustrate the performance of the proposed observer, a comparison is made with another observer with an output predictor on a satellite navigation system. And the observer proposed in this article is applied to the model‐free control of ultra‐local models. [ABSTRACT FROM AUTHOR]

This work addresses the problem of adaptive observer design for nonlinear systems satisfying incremental quadratic constraints. The output of the system includes nonlinear terms, which puts an additional strain on the design and feasibility of the observer, which is guaranteed under the satisfaction of an LMI, and a set of algebraic constraints. A particular case where the output nonlinearity matches the unknown parameter coefficient is also discussed. The result is illustrated through a numerical example for the chaos synchronization of the Rössler system. [ABSTRACT FROM AUTHOR]

Summary: The article addresses the problem of a robust adaptive observer design for multi‐input multi‐output linear time‐invariant plants subjected to bounded disturbances in the system dynamics and the output relation. An adaptive observer design is proposed based on the finite excitation leading to a switched estimation strategy thus relaxing persistence of excitation (PE) condition. The novel finite persistence excitation (f‐PE) condition introduced in this work is significantly milder than the PE condition in terms of excitation requirement and online verifiability. The switched estimator uses the finite excitation via input signal and ensures uniformly ultimately bounded stability of the estimation error dynamics. A multiple switching strategy, which continuously tracks the information richness (about the unknown parameters), is utilized to reduce the intermediate bounds and is proved to be instrumental in improving the result. [ABSTRACT FROM AUTHOR]

This article mainly focuses on the attack location and defense of cyber-physical power systems under dynamic load altering attacks. Taking the smart grid as an example, first, the cyber-physical power system model under attack is established to provide conditions for attack detection. On this basis, an adaptive observer with H ∞ performance is designed to realize detection and location through residual signal and attack estimation, respectively. Furthermore, a power redistribution strategy based on optimal economic scheduling is presented to realize attack defense by means of the observer data. Finally, theoretical analysis proves the feasibility of the algorithm, and simulation cases prove the effectiveness of attack location and defense strategies. [ABSTRACT FROM AUTHOR]

Typical fault scenarios such as actuator stuck, wingtip damage, and sudden center of gravity change usually bring great challenge to fault-tolerant flight control laws. In this paper, adaptive sliding mode disturbance observer (SMDO) is studied under the background of designing a fault-tolerant flight controller. This paper proposes a switched adaptive SMDO for observation of system uncertainties considering fault accommodation. It consists of two adaptive SMDOs: baseline gain-adaptation SMDO and fault-oriented gain-adaptation SMDO. The former is designed for observing slow-changing uncertainties, and can guarantee that the observer gains are small when disturbances are small; the latter aims at achieving fast observing and accommodating of sudden and large faults. Thereafter, a switching logic is developed for the switch between these two adaptive SMDO modes to ensure that the fast gain-adaptation SMDO only works in the case that the uncertainties are large and fast-changing. Finally, the switched adaptive SMDO is integrated with an incremental terminal sliding mode controller, and are validated using simulation experiments based on the nonlinear F-16 model.

This paper presents an adaptive nonlinear observer for sensorless passivity based control applied to permanent magnet synchronous motor. The passivity based control approach is applied to a complex and coupled nonlinear mathematical model of permanent magnet synchronous motor without any approximation or cancellation of nonlinearities.A nonlinear adaptive observer is proposed to estimate the mechanical speed and the unmeasured load torque (unknown disturbance) that has an effect on the control performance; therefore, those estimated states are then used to improve the performance of the passivity based control for permanent magnet synchronous motor.The performance of the proposed controller-observer have been tested using MATLAB/SIMULINK, where those Simulation results show a perfect tracking of the mechanical speed and load torque.

Summary: We are considering the problem of sampled‐data observer design for nonlinear time‐varying systems that are state‐affine. The novelty lies in that both distributed and discrete delays are considered in the output equation. The latter is also subject to a parameter uncertainty of nonaffine nature due to output sampling. Interestingly, all system delays are modeled using a single distributed representation, involving a distribution function, allowing thus for a unified treatment of delays. A Kalman‐like observer is developed to cope with both state and parameter uncertainty. Its main components are (i) a time‐varying‐gain state‐estimator involving both output and parameter rate injections; (ii) a distributed‐nature adaptive output‐predictor that compensate for all delay effects, including that of output sampling; (iii) an parameter‐estimator that is optimized in the sense that it makes use of all available information. The resulting observer is shown to be exponentially convergent, for small delays and sampling intervals, provided the input signal is sufficiently exciting. The analysis is performed using a Lyapunov–Krasovskii functional, Halanay's lemma, Wirtinger's inequality, and other tools. [ABSTRACT FROM AUTHOR]

This brief proposes a solution to the long-standing problem of designing an adaptive control for the dc–dc boost converter with unknown load and unavailable input current measurement. The difficulty lies in the parametric uncertainty of the output dynamics, which poses a manifold of equilibria in the classical adaptive observer design. This is known as the detectability obstacle that imposes a restrictive assumption on the system behavior to ensure convergence. To overcome this problem, a class of saturated dynamic controllers is designed to guarantee the asymptotic regulation of the output voltage. An immersion- and invariance-based adaptive observer is proposed, which preserves the convergence property in conjunction with the controller with no need for an extra persistent excitation condition. To evaluate the transient behavior of the proposed controller, realistic simulations are provided, and then the performance comparison with two other well-known output feedback controllers is presented. Moreover, experimental results are concluded on a prototype dc–dc boost converter to support and verify the results of theory and simulations. [ABSTRACT FROM AUTHOR]

This paper presents a frequency-adaptive current control design for a grid-connected inverter (GCI) with an inductive-capacitive-inductive (LCL) filter in the presence of grid disturbance such as the grid frequency variation and grid voltage harmonic distortion as well as polytopic uncertainties in the LCL filter parameters. The grid current control is achieved by augmenting integral and resonant terms into the LCL-filtered inverter system model to constitute integral-resonant full-state feedback control for zero steady-state error and current harmonic attenuation. To realize the full-state feedback control, the information on all the state variables is essential. However, additional sensors for state measurements increase the implementation cost as well as the complexity. To overcome this issue, a full-state discrete-time observer is employed in the stationary reference frame. Furthermore, to maintain the quality of grid currents injected into the grid, a frequency-adaptive current control is introduced. For this aim, the grid frequency is estimated through an adaptive observer rapidly and precisely. Then, the estimated grid frequency is used to adaptively change the frequency information in the augmented resonant controller for the purpose of producing high-quality grid currents even under both distorted grid voltages and grid frequency variation. In addition, to ensure the robustness against LCL filter parameter perturbation, a linear matrix inequality-linear quadratic regulator (LMI-LQR) approach is proposed for polytopic uncertainties in the LCL filter parameters to design full-state feedback control as well as a full-state observer. To verify the effectiveness of the proposed control scheme, the simulation and experimental results are given.

The online estimation of the state-of-charge (SOC) of Li-ion battery using the adaptive Lyapunov-based observer is an attractive proposition due to the ensured stability, adaptability and reduced computing requirement. However, the observer requires the presence of the persistent excitation (PE) to guarantee the convergence of the battery model parameters to their correct values. The PE is satisfied using sufficiently rich (SR) signal that contains spectral components to excite the battery model. This paper revisits several important works that utilize such observer and highlights the absence of PE in their practical implementation. Since the previous works utilize dc excitation that lacks the SR characteristics, the validity of the published results is questionable. To rectify the problem, a scheme known as the forced excitation is proposed to estimate the battery parameters under dc or low excitation level. The SR signal is generated by chopping the battery current at a certain rate for specific interval. Moreover, the disruption of the load current (due to the chopping) is compensated using the supercapacitor. The concept is simulated by Matlab/Simulink and is realized experimentally using a Panasonic NCR18650B Li-ion battery. The forced excitation algorithm is implemented on the DS1104 dSPACE platform. The results show that the proposed method satisfies the PE condition and is able to correctly estimate the SOC even with low excitation signals.

Identification of abnormal source hidden in distributed parameter systems (DPSs) belongs to the category of inverse source problems. It is important in industrial applications but seldom studied. In this article, we make the first attempt to investigate the abnormal spatio-temporal (S-T) source identification for a class of DPSs. An inverse S-T model for abnormal source identification is developed for the first time. It consists of an adaptive state observer for source identification and an adaptive source estimation algorithm. One major advantage of the proposed inverse S-T model is that only the system output is utilized, without any state measurement. Theoretic analysis is conducted to guarantee the convergence of the estimation error. Finally, the performance of the proposed method is evaluated on a heat transfer rod with an abnormal S-T source. [ABSTRACT FROM AUTHOR]

This article proposes a straightforward design guideline and analysis of adaptive full-order observers for rotor position and speed estimation of interior permanent magnet synchronous motors (IPMSM) sensorless drives. The adaptive observer is built upon the IPMSM extended electromotive force model in the stationary reference frame. The design of the adaptation and feedback gains is performed by a cascade approach, where state observer and adaptive law operate at distinct frequencies. Since speed estimation error impacts the system stability, this issue is the focus of the high-bandwidth state observer design. On the other hand, the adaptive feedback loop, which operates at lower frequencies, ensures good rotor speed estimation performance. The effect of synchronous inductances and stator resistance variation on the estimation of the mechanical variables is investigated and analytically described. Experimental results validate the adaptive observer design method and the parameter variation analysis under sensorless vector control. [ABSTRACT FROM AUTHOR]

To improve the sensorless control performance of electrical drive systems, a nonsingular terminal sliding mode observer (NTSMO) and adaptive observer are proposed to solve the chattering and phase delay problems. Firstly, by defining a new nonsingular terminal sliding mode surface, the sliding mode observer based on the fast reaching law is designed to estimate the back electromotive force (EMF). The observer enhances the robustness and system performance eliminates the singularity and attenuates the chattering. Next, to obtain the accurate back-EMF signal, an adaptive observer is designed instead of a traditional low-pass filter to filter out the harmonics. The adaptive observer can avoid the phase delay problem and further improve the signal observation accuracy. Then, the rotor position and speed information are accurately tracked. The proposed method is applied to the speed control system of a permanent magnet synchronous motor (PMSM), and the effectiveness and feasibility of the proposed sliding mode observer are demonstrated by the experiment.

This article develops a robust fault tolerant (FT) control scheme for an $n$ -link uncertain robotic system with actuator failures. In order to eliminate the influence of both the uncertainties and actuator failures on the system performance, the Gaussian radial basis function neural networks are used to compensate for the actuator failures and uncertain dynamics. An adaptive observer is designed to compensate for external disturbance. In addition, in order to accelerate the recovery of system stability after failure, a nonsingular fast terminal sliding mode is given. Finally, the simulation results on a two-link manipulator confirms the superior performance of the proposed neural networks-based FT controller, and the experiment results on the Baxter robot further verify the effectiveness of the control method. [ABSTRACT FROM AUTHOR]

Summary: This article deals with the problem of adaptive estimation, that is, the simultaneous estimation of the state and time‐varying parameters, in the presence of measurement noise and state disturbances, for a class of uncertain nonlinear systems. An adaptive observer is proposed based on a nonlinear time‐varying parameter identification algorithm and a sliding‐mode observer. The nonlinear time‐varying parameter identification algorithm provides a fixed‐time rate of convergence, to a neighborhood of the origin, while the sliding‐mode observer ensures ultimate boundedness for the state estimation error attenuating the effects of the external disturbances. Linear matrix inequalities are provided for the synthesis of the adaptive observer while the convergence proofs are given based on the Lyapunov and input‐to‐state stability theory. Finally, some simulation results show the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

This brief proposes an adaptive observer based on the super-twisting algorithm (STA) and its discrete-time realization with a predefined convergence time. In contrast to conventional adaptive STA that tries to adaptively reduces the gain sizes as much as possible in accordance with external disturbances, the proposed adaptive observer increases the gain sizes such that the convergence time is ensured to be within the predefined convergence-time period. The numerical chattering associated with these large gains is suppressed by employing the proposed discrete-time realization based on an implicit Euler discretization method. While keeping the property of predefined convergence time, the observation precision of the proposed discrete-time scheme is consistent with the STA, i.e., standard asymptotical second-order accuracy level. The superiority of the adaptive observer and its realization scheme is demonstrated through a circuit system example. [ABSTRACT FROM AUTHOR]

The design of an adaptive observer for a class of multi‐input multi‐output Lipschitz nonlinear systems with uncertainties and multirated outputs is proposed in this paper. The sampling instants of the different outputs can be asynchronous and irregular. The exponential convergence of the proposed observer is proved with the help of a Lyapunov approach. The stability is ensured, provided that some conditions, including a persistent excitation condition and a maximum value condition on the sample rate of each output, are verified. The performances of the observer are illustrated through simulations of the attitude estimation problem using sensors from an inertial measurement unit and considering gyro bias. [ABSTRACT FROM AUTHOR]

This study investigates and controls the optimal operating methanol concentration for direct methanol fuel cell (DMFC) system, which effectively improves the efficiency of DMFC system. Firstly, a DMFC output characteristic model and a nonlinear stack methanol concentration model were established. Moreover, an adaptive observer was developed to reconstruct unmeasurable stack methanol concentration to estimate methanol concentration. Then, in order to achieve the minimum methanol consumption under required power, the reference methanol concentration was determined by analyzing DMFC system with its maximum energy conversion efficiency. Finally, the sliding mode algorithm was adopted to track reference optimal methanol concentration. The estimated performance of adaptive observer and non-adaptive observer for methanol concentration was compared through experimental bench data. Furthermore, the methanol consumption and energy conversion efficiency under proposed optimal methanol concentration strategy and fixed methanol concentration were compared. The comprehensive result demonstrates that the adaptive observer has better performance than non-adaptive observer in terms of estimated performance. Moreover, the proposed strategy for optimal methanol concentration was compared with fixed methanol concentration of 0.3 mol/L and 0.4 mol/L, and the fuel consumption of DMFC system was reduced by 7.9% and 30.9% respectively. • Establish DMFC stack methanol concentration model and analysis its characteristic. • Describe quantitatively energy conversion efficiency of DMFC system. • Propose adaptive observer to monitor DMFC stack methanol concentration with good robustness and adaptability. • Design the strategy of maximum energy conversion efficiency and improve methanol utilization. [ABSTRACT FROM AUTHOR]