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In this article, two novel neural adaptive attitude control strategies are presented for the attitude tracking of rigid spacecraft affected by inertia uncertainty, external disturbance, and torque saturation with guaranteed prescribed performance. Under the backstepping framework, a neural adaptive state feedback controller is designed first by utilizing the neural network (NN) approximation and barrier Lyapunov function (BLF). Then, on the basis of the state feedback controller, a neural adaptive output feedback controller is exploited by integrating with a high-gain observer. Lyapunov stability analysis shows that all the closed-loop error signals are uniformly ultimately bounded under the both proposed neural adaptive attitude controllers. In comparisons with the most existing researches, the distinctive advantages of the proposed neural adaptive attitude controllers are threefold. (1) With the help of NN approximation, the proposed attitude controllers are model-independent and still applicable even when the spacecraft attitude dynamics are completely unknown. (2) Benefiting from the BLF, the proposed attitude controllers can guarantee the attitude tracking error always satisfies the preassigned performance. (3) Both the state feedback control and output feedback control are considered. The angular velocity information of the spacecraft is not required for the output feedback control design. At last, numerical simulations and comparisons are conducted to verify and highlight the proposed neural adaptive attitude control strategies.

This article is concerned with the neural network (NN)-based event-triggered control problem for discrete-time networked Markov jump systems with hybrid cyberattacks and unmeasured states. The event-triggered mechanism (ETM) is used to reduce the communication load, and a Luenberger observer is introduced to estimate the unmeasured states. Two kinds of cyberattacks, denial-of-service (DoS) attacks and deception attacks, are investigated due to the vulnerability of cyberlayer. For the sake of mitigating the impact of these two types of cyberattacks on system performance, the ETM under DoS jamming attacks is discussed first, and a new estimation of such mechanism is given. Then, the NN technique is applied to approximate the injected false information. Some sufficient conditions are derived to guarantee the boundedness of the closed-loop system, and the observer and controller gains are presented by solving a set of matrix inequalities. The effectiveness of the presented control method is demonstrated by a numerical example.

The article investigates full information control problem for switched neural networks subject to fault and disturbance. First, the main objective is realizing interval stability and zero tracking error under condition that neither of the neuron states' vectors including the plant and reference models is available. Second, the desired full information controller and neural networks' observer are designed to ensure observer-based dynamic error system mean-square exponentially stable with sufficient condition of strict weight ${H}_{∞} /{H}_-$ performance levels. Finally, we concentrate on stability analyses and fault tolerance for switched neural networks with fault accompanied by disturbance through linear matrix inequalities (LMIs), Lyapunov function, and average dwell time, discussing it according to different values of fault. Finally, simulation examples are listed to account for the availability and effectiveness of the research methodology.

Motion control is critical in mobile robot systems, which determines the reliability and accuracy of a robot. Due to model uncertainties and widespread external disturbances, a simple control strategy cannot match tracking accuracy with disturbance immunity, while a complex controller will consume excessive energy. For precise motion control with disturbance immunity and low energy consumption, a control method based on an enhanced reduced-order extended state observer (ERESOBC) is proposed to control the motor-wheels dynamic model of a differential driven mobile robot (DDMR). In this method, only unknown state error and negative disturbance are estimated by the enhanced reduced-order extended state observer (ERESO), which reduces the required energy of the observer. In addition, a simple state-feedback-feedforward controller is used to track the reference signal and compensate for negative disturbance. Through numerical simulation and application example, the tracking performance and disturbance rejection performance of DDMR are compared with the traditional control method based on enhanced extended state observer (EESOBC), and the results show the superiority of the ERESOBC method.

When making decisions, animals must trade off the benefits of information harvesting against the opportunity cost of prolonged deliberation. Deciding when to stop accumulating information and commit to a choice is challenging in natural environments, where the reliability of decision-relevant information may itself vary unpredictably over time (variable variance or "heteroscedasticity"). We asked humans to perform a categorization task in which discrete, continuously valued samples (oriented gratings) arrived in series until the observer made a choice. Human behavior was best described by a model that adaptively weighted sensory signals by their inverse prediction error and integrated the resulting quantities with a linear urgency signal to a decision threshold. This model approximated the output of a Bayesian model that computed the full posterior probability of a correct response, and successfully predicted adaptive weighting of decision information in neural signals. Adaptive weighting of decision information may have evolved to promote optional stopping in heteroscedastic natural environments. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

In this study, the output-feedback control (OFC) strategy design problem is explored for a type of Takagi-Sugeno fuzzy singular perturbed system. To alleviate the communication load and improve the reliability of signal transmission, a novel stochastic communication protocol (SCP) is proposed. In particular, the SCP is scheduled based on a nonhomogeneous Markov chain, where the time-varying transition probability matrix is characterized by a polytope-structure-based set. Different from the existing homogeneous Markov SCP, a nonhomogeneous Markov SCP depicts the data transmission in a more reasonable manner. To detect the actual network mode, a hidden Markov process observer is addressed. By virtue of the hidden Markov model with partly unidentified detection probabilities, an asynchronous OFC law is formulated. By establishing a novel Lyapunov-Krasovskii functional with a singular perturbation parameter and a nonhomogeneous Markov process, a sufficient condition is exploited to guarantee the stochastic stability of the resulting system, and the solution for the asynchronous controller is portrayed. Eventually, the validity of the attained methodology is expressed through a practical example.

The consensus problem of general linear multiagent systems (MASs) is studied under switching topologies by using observer-based event-triggered control method in this article. On the basis of the output information of agents, two kinds of novel event-triggered adaptive control schemes are designed to achieve the leaderless and leader-follower consensus problems, which do not need to utilize the global information of the communication networks. Finally, two simulation examples are introduced to show that the consensus error converges to zero and Zeno behavior is eliminated in MASs. Compared with the existing output feedback control research, one of the significant advantages of our methods is that the controller protocols and triggering mechanisms do not rely on any global information, are independent of the network scale, and are fully distributed ways.

This article is concerned with the problem of the $H_{∞}$ output feedback control for a class of event-triggered networked systems subject to multiple cyber attacks. Two dynamic event-triggered generators are equipped at sensor and observer sides, respectively, to lower the frequency of unnecessary data transmission. The sensor-to-observer (STO) channel and observer-to-controller (OTC) channel are subject to deception attacks and Denial-of-Service (DoS) attacks, respectively. The aim of the addressed problem is to design an output feedback controller, with the consideration of the effects of dynamic event-triggered schemes (DETSs) and multiple cyber attacks. Sufficient condition is derived, which can guarantee that the resulted closed-loop system is asymptotically mean-square stable (AMSS) with a prescribed $H_{∞}$ performance. Moreover, we provide the desired output feedback controller design method. Finally, the effectiveness of the proposed method is demonstrated by an example.

This article studies the adaptive fuzzy output-feedback decentralized control problem for the fractional-order nonlinear large-scale systems. Since the considered strict-feedback systems contain unknown nonlinear functions and unmeasurable states, the fuzzy-logic systems (FLSs) are used to model unknown fractional-order subsystems, and a fuzzy decentralized state observer is established to obtain the unavailable states. By introducing the dynamic surface control (DSC) design technique into the adaptive backstepping control algorithm and constructing the fractional-order Lyapunov functions, an adaptive fuzzy output-feedback decentralized control scheme is developed. It is proved that the decentralized controlled system is stable and that the tracking and observer errors are able to converge to a neighborhood of zero. A simulation example is given to confirm the validity of the proposed control scheme.

The distributed observer problem is motivated by the case where the output information of the system is decentralized in different subsystems. In this scene, all the subsystems form an observer network, and each of them has access to only a part of output information and the information exchanged via the given communication networks. Due to the limitation of communication conditions among subsystems, the communication network is often time varying and disconnected. However, the existing research about the aforementioned scene is still not enough to solve this problem. To this end, this article is concerned with the challenge of the distributed observer design for linear systems under time-variant disconnected communication networks. The design method is successfully established by fixing both completely decentralized output information and incompletely decentralized output information into account. Our work overcomes the limitation of the existing results that the distributed observer can only reconstruct the full states of the underlying systems by means of fast switching. In the case of completely decentralized output information, a group of sufficient conditions is put forward for the system matrix, and it is proved that the asymptotical omniscience of the distributed observer could be achieved as long as anyone of the developed conditions is satisfied. Furthermore, unlike similar problems in multiagent systems, the systems that can meet the proposed conditions are not only stable and marginally stable systems but also some unstable systems. As for the case where the output information is not completely decentralized, the results show with the observable decomposition and states reorganization technology that the distributed observer could achieve omniscience asymptotically without any constraints on the system matrix. The validity of the proposed design method is emphasized in two numerical simulations.

This work investigates the observer-based asynchronous control of discrete-time nonlinear systems with network-induced communication constraints. To avoid the data collisions and side effects in a constrained communication channel, a novel dynamic event-based weighted try-once-discard (DEWTOD) protocol is proposed. In contrast to the existing protocols, the DEWTOD scheduling regulates whether the sampling instant to release and which node to transmit the sampling instant simultaneously. In light of a hidden Markov model, the time-varying detection probability matrix is characterized by a polytopic set. By resorting to the polytopic-structured Lyapunov functional, sufficient conditions are derived such that the closed-loop dynamic is mean-square exponentially stable, and the observer-based controller is designed. In the end, two numerical examples are provided to explicate the validity of the attained methodology.

In this paper, we investigate the enforcement of opacity via supervisory control in the context of discrete-event systems. A system is said to be opaque if the intruder, which is modeled as a passive observer, can never infer confidentially that the system is at a secret state. The design objective is to synthesize a supervisor such that the closed-loop system is opaque even when the control policy is publicly known. In this paper, we propose a new approach for enforcing opacity using non-deterministic supervisors. A non-deterministic supervisor is a decision mechanism that provides a set of control decisions at each instant, and randomly picks a specific control decision from the decision set to actually control the plant. Compared with the standard deterministic control mechanism, such a non-deterministic control mechanism can enhance the plausible deniability of the controlled system as the online control decision is a random realization and cannot be implicitly inferred from the control policy. We provide a sound and complete algorithm for synthesizing a non-deterministic opacity-enforcing supervisor. Furthermore, we show that non-deterministic supervisors

In this article, a rapid sensor fault diagnosis (SFD) method is presented for a class of nonlinear systems. First, by exploiting the linear adaptive observer technology and the deterministic learning method (DLM), an adaptive neural network (NN) observer is constructed to capture the information of the unknown sensor fault function. Second, when the NN input orbit is a period or recurrent one, the partial persistent excitation (PE) condition of the NNs can be guaranteed through the DLM. Based on the partial PE condition and the uniformly completely observable property of a linear time-varying system, the accurate state estimation and the sensor fault identification can be achieved by properly choosing the observer gain. Third, a bank of dynamical observers utilizing the experiential knowledge is constructed to achieve rapid SFD and data recovery. The attractions of the proposed approach are that accurate approximations of sensor faults can be achieved through the DLM, and the data that are destroyed by the sensor faults can be recovered by using the learning results. Simulation studies of a robot system are utilized to show the effectiveness of the proposed method.

This article studies the observer-based event-triggered containment control problem for linear multiagent systems (MASs) under denial-of-service (DoS) attacks. In order to deal with situations where MASs states are unmeasurable, an improved separation method-based observer design method with less conservativeness is proposed to estimate MASs states. To save communication resources and achieve the containment control objective, a novel observer-based event-triggered containment controller design method based on observer states is proposed for MASs under the influence of DoS attacks, which can make the MASs resilient to DoS attacks. In addition, the Zeno behavior can be eliminated effectively by introducing a positive constant into the designed event-triggered mechanism. Finally, a practical example is presented to illustrate the effectiveness of the designed observer and the event-triggered containment controller.

In this article, the finite-time consensus problem is investigated for a group of heterogeneous nonlinear multiagent systems with input delays. Different from the existing results, input delays are assumed to be nonidentical and time-varying. To overcome the difficulty of nonidentical input delays, a novel compensation mechanism is introduced. Moreover, in order to save communication resources, a distributed finite-time self-triggered observer is constructed. By employing the observer and the dynamic surface control strategy, the finite-time adaptive neural control protocol is proposed. It is proved that not only the followers’ outputs can be synchronized with the leader in finite time, but also continuous communication among followers is avoided. Finally, two simulation examples are provided to illustrate the validity of the proposed control strategy.

In this article we investigates the practical fixed-time consensus tracking problem for multiple Euler–Lagrange systems, which is subject to, concurrently, stochastic packet losses in directed communication networks (DCNs) and input/output constraints. In practice, the packet losses in the communication network may lead to communication failure and control accuracy degradation. To address this impact, a novel fully distributed observer is established to estimate the leader’s states within fixed time. Subsequently, the state constraint function is introduced so that the designed adaptive control protocol can be used in a uniform structure with unconstrained or asymmetric/symmetric output constraints without changing the adaptive structure. Combining the backstepping technique with fuzzy-logic systems, an adaptive fixed-time local control protocol is constructed to drive each agent to track the leader’s states estimated by the observer. Besides, a novel auxiliary system is introduced to solve the constraint problem of the control input. Furthermore, through rigorous theoretical analysis, it is verified that all signals converge to the compact set close to zero with guaranteed fixed-time convergence rate. At last, a representative simulation is carried out to demonstrate the practicability of the proposed method.

Time/space separation-based modeling methods have been widely researched for estimating lithium-ion battery (LIB) thermal dynamics. However, these methods have been developed in an offline environment and may not perform well in real-time application since the battery systems in electric vehicles (EVs) are usually subject to external disturbances. Furthermore, the onboard measurements of temperature are often corrupted by significant error. To address these problems, we present a reduced model-based observer design for online temperature distribution estimation in LIBs. First, an extreme learning machine (ELM) based offline spatiotemporal model is constructed to approximate the thermal dynamics of LIB. Second, an adaptive reduced order observer is designed based on the offline model developed in the previous step. The offline model is then updated with the estimation results of the observer. As the performance of the estimator is highly related to the placement of sensors, a genetic algorithm (GA) based integrated optimization strategy is also developed to determine the optimal sensor location for online estimation. Finally, the whole temperature distribution is estimated in real time using the observer, the measured voltage, current and the limited available temperature data. Simulation results on a battery thermal system verify the effectiveness of this developed model.

This article aims at proposing an adaptive neural control strategy for a class of nonlinear time-delay systems with input delays and unknown control directions. Different from previous researches that investigated delays and constraints separately, the novelty of this article lies in that it simultaneously considers delays (state and input delays) and input constraints (magnitude and rate constraints) for a class of uncertain nonlinear systems. In this article, the uncertain states and input delays are handled by integrating a constructed auxiliary system that functions as an observer with neural networks (NNs), with which the adverse effects caused by the uncertain states and input delays can be approximated and compensated. By involving smooth hyperbolic tangent functions in the designed auxiliary system, the problem of magnitude and rate constraints of the control input is fully addressed. Then, the backstepping technique runs through the entire control designing process, which allows the designed adaptive neural control strategy to handle the input constraints and delays at the same time. Furthermore, Nussbaum functions are employed to resolve the problem of unknown control directions. Due to the introduction of an input-driven filter, only the output of the system is required to be measured as the control feedback, which promotes the applicability of the designed controller. Under the proposed control scheme, semiglobal, uniform, and ultimate boundedness of all signals of the closed-loop system is realized with uncertain control directions, input and state delays, and guaranteed magnitude and rate constraints of control inputs. Finally, simulation results are illustrated to verify the effectiveness of the presented control method.

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position harmonic error (ERPHE) caused by inverter nonlinearities, flux spatial harmonics, and the current measurement error in high-frequency signal injection based schemes, an adaptive iterative learning control based online suppression method is proposed in this paper. This method constructs a P-type iterative learning rotor position observer with the forgetting factor to obtain the tracking error and generate the compensation value in real time. The stability, the convergence, and the parameter sensitivity of this observer are analyzed in detail. To improve the suppression effect of the ERPHE for different loads and speeds, an iterative learning gain self-tuning strategy is investigated. The proposed method does not require the offline detection and the discrimination of harmonic frequencies, which has strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW interior PMSM drive platform.

This paper develops a distributed solution to the fully-heterogeneous containment control problem (CCP), for which not only the followers' dynamics but also the leaders' dynamics are non-identical. A novel formulation of the fully-heterogeneous CCP is first presented in which each follower constructs its virtual exo-system. To build these virtual exo-systems by followers, a novel distributed algorithm is developed to calculate the so-called normalized level of influences (NLIs) of all leaders on each follower, and a novel adaptive distributed observer is designed to estimate the dynamics and states of all leaders that have an influence on each follower. Then, a distributed control protocol is proposed based on the cooperative output regulation framework, utilizing this virtual exo-system. Based on estimations of leaders' dynamics and states and NLIs of leaders on each follower, the solutions of the so-called linear regulator equations are calculated in a distributed manner, and consequently, a distributed control protocol is designed for solving the output containment problem. Finally, theoretical results are verified by performing numerical simulations.

A simple necessary and sufficient condition ensuring that a real matrix of dimension 3 is similar to a Metzler matrix is exhibited. When this condition is satisfied, a construction of the transfer matrix is given. This construction is used to design an interval observer for a family of continuous-time systems. An example is provided with interval observer design for the so-called love dynamics in the case of limit cycles.

This article studies the multiagent bipartite consensus in networks with deterministic disturbances and antagonistic interactions. An observer-based output-feedback controller design is provided to guarantee the bipartite consensus with deterministic disturbances that satisfy the matching condition. Then, by considering that the bandwidths of communication channels are limited in practical systems, the event-triggered scenario of the proposed output controller for the bipartite consensus is further studied; the node-based broadcast updating fashion is utilized and the Zeno behavior is ruled out. Simulations are also offered to support the theoretical results of protocol designs.

In this article, a neural-network-based constrained output-feedback control is considered for microelectromechanical system (MEMS) gyroscopes subject to scarce transmission bandwidth and lumped disturbances resulting from model uncertainties, dynamic coupling, and environmental disturbances. First, a hybrid quantizer capable of achieving an adjustable communication rate and quantization density is proposed to convert continuous control signals into discrete values, allowing for reduced chattering behavior even when control actions vary within large regions and enhanced tracking accuracy can be ensured. Subsequently, by applying two types of nonlinear mapping, all state variables of MEMS gyroscopes are restrained within the predefined time-varying asymmetric functions without imposing stringent feasibility conditions on virtual control laws. Furthermore, an echo-state network-based minimal learning parameter neural observer is developed to simultaneously recover the unmeasurable velocity-state variables, matched as well as unmatched disturbances in constrained MEMS gyroscopes dynamics, enabling an output-feedback control solution with a decreased online learning complexity. It is shown via the Lyapunov stability and nonsmooth analysis that all signals in the closed-loop system remain ultimately uniformly bounded even with discontinuous control actions. Comparison simulations are produced to certify the effectiveness of the presented controller.

In this article, an adaptive sensor fault accommodation scheme is proposed for uncertain vehicle active suspensions via output-feedback control where vehicle body displacement is the only measurable output signal corrupted by sensor bias. An adaptive observer with variable gains is constructed to obtain state estimates whose design procedure involves parameter adaption of the uncertain system parameters and sensor bias, and an output-feedback controller is designed to attenuate the vehicle body displacement based on the partial measurement information, estimates of the states, and unknown parameters. Compensation for measurement error is made both in the design process of the adaptive observer and output-feedback controller in order to weaken the influence brought about by sensor bias fault. In order to guarantee system stability, the variable observer gains are determined in real time using a switching strategy where their values can be modified in finite times by monitoring the state estimates generated by the observer itself. It is proved that the vehicle body displacement will converge to a small neighborhood around zero, and all the signals of the closed-loop system are ensured to be bounded through selecting suitable control parameters. Simulation is carried out to show the effectiveness of the proposed method and results indicate that better stabilization of the suspension vertical motion can be achieved through adaptive compensation for sensor bias.

Human-on-the-loop (HOTL) system is regarded as a promising technology to allow autonomous underwater vehicle (AUV) to track the most adequate target point as soon as possible. However, the unique characteristics of the underwater environment make it challenging to perform the tracking task. This article is concerned with a finite-time tracking control issue for AUV, subjected to unavailable velocity signals in the measurement side and uncertain model parameters in physical side. A HOTL system, including operator, buoys, AUV and sensors, is first provided to construct a cooperative tracking network. For such system, operator in surface control center decides the tracking mission based on all available data. Then, a buoy-assisted localization estimator is utilized by AUV to acquire its position, through which a fast terminal sliding mode observer is developed to estimate the velocity of AUV in finite time. With the estimated velocity information, an adaptive-nonsingular fast terminal sliding mode tracking controller is designed to drive AUV to the target point in finite time. For the proposed velocity observer and tracking controller, the signum and differential functions are employed together to improve the convergence speed and reduce the chattering. Besides that, the proposed solution can not only guarantee finite-time velocity observation, but also achieve finite-time tracking control. Finally, simulation and experimental results are both presented to verify the effectiveness.

High-gain nature of extended state observer (ESO), which forms an integral part of active disturbance rejection control (ADRC) technique, results in the following limitations: (1) Sensitivity to high-frequency measurement noise which limits closed-loop performance in practical applications. (2) Escalation of observer gains up-to a power $\bm{n+1}$ of observer bandwidth, which complicates numerical implementation when system order $(\bm{n})$ or observer bandwidth is large. To overcome these limitations, a low-power higher-order ESO (LHESO) is proposed in the present work for practical application of ADRC scheme in noisy environment. Moreover, a recently proposed cascade ESO (CESO), designed for noise suppression, is analyzed in the frequency-domain to reveal an underlying similarity with higher-order ESO, which is not reported in literature. Presented analysis justifies the performance improvement obtained over conventional ESO and provides a guideline for selecting the number of cascade levels based on the expected nature of disturbance. Case study performed on a DC-DC boost converter illustrates the practical advantages of proposed scheme over CESO in terms of improved immunity to high-frequency measurement noise, precise regulation in presence of time-varying disturbance, low observer gains that facilitate numerical implementation and ease of tuning due to a single observer parameter.

The back-electromotive force (BEMF) model-based observer is an attractive sensorless control strategy for permanent magnet synchronous motor (PMSM) drives. However, the accuracy of BEMF observer is deteriorated due to the dc offsets caused by current measurement and inverter nonlinearity. To address this problem, the paper proposed a two-degree-of-freedom structure-based backstepping observer (TSBO) for sensorless PMSM drives. First, a two-degree-of-freedom structure is presented to improve the frequency-domain response of the BEMF observer. Second, a second-order complex vector transfer function is inversely introduced into the studied structure to achieve bandpass characteristics. Compared with the conventional BEMF observer, the studied TSBO can achieve dc error suppression without introducing the extra bandpass filter. The studied algorithm is verified by the experimental results on a 4.4kW PMSM platform.

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.

This article investigates the fault-tolerant control problem for stochastic switched interval type-2 (IT2) fuzzy time-delayed uncertain systems based on unknown input observer synthesis, which can avoid uneasy measurement on the time derivative of output, and estimate unavailable or partially measurable states, including sensor and actuator faults accurately. First, a desired fuzzy observer is designed to ensure the observer-based dynamic error system mean-square exponentially stable with sufficient condition of a strict (l,ℏ,℘)-℧-dissipative performance, which is a unified framework of passivity, and H∞ provides results with less conservativeness. Then, we concentrate on stability analyses on dissipativity-based switched IT2 fuzzy systems with stochastic perturbation through linear matrix inequalities, Lyapunov function, free-weighting matrices, and average dwell time, discussing it according to different values of disturbance. Finally, simulation examples are listed to account for availability and effectiveness of the research methodology.

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.

Finite-dimensional observer-based controller design for PDEs is a challenging problem. Recently, such controllers were introduced for the 1D heat equation, under the assumption that one of the observation or control operators is bounded. This paper suggests a constructive method for such controllers for 1D parabolic PDEs with both (observation and control) operators being unbounded. We consider the Kuramoto-Sivashinsky equation (KSE) under either boundary or in-domain point measurement and boundary actuation. We employ a modal decomposition approach via dynamic extension, using eigenfunctions of a Sturm-Liouville operator. The controller dimension is defined by the number of unstable modes, whereas the observer dimension $N$ may be larger than this number. We suggest a direct Lyapunov approach to the full-order closed-loop system, which results in an LMI whose elements and dimension depend on $N$. The value of $N$ and the decay rate are obtained from the LMI. We extend our approach to internal stabilization with guaranteed $L^2$-gain and input-to-state stabilization. We prove two crucial properties of the derived LMIs. First, We prove that the LMIs are always feasible provided $N$ and the $L^2$ or ISS gains are large enough, thereby obtaining guarantees for our approach. Moreover, for the case of stabilization, we show that feasibility of the LMI for some $N$ implies its feasibility for $N+1$ (i.e., enlarging $N$ in the LMI cannot deteriorate the resulting decay rate of the closed-loop system). Numerical examples demonstrate the efficiency of the method.

The problem of having an identical speed when dealing with multiple motors always exists in the industry. There are several methods to address the problem, but all the methodologies have two common drawbacks. Firstly, the control design requires continuous information of the desired speed and the actual speed of motors; secondly, it is sometimes difficult to directly measure the speed variables. In the proposed study, both of these drawbacks are addressed by designing an observer-based event-triggered networked multi-agent system. The proposed method uses the leader following consensus approach with a centralized event triggering control design, so that whenever a follower's speed diverges from that of the leader, an event is triggered, which communicates and resets all the agents to the leader's speed. Moreover, an observer is designed such that the ith agent uses its jth neighbor agent and observer speed information to estimate the leader's speed. The stability of the proposed design is formulated by Lyapunov stability, while the simulation results endorse the design concepts and energy saving.

This article develops an adaptive observation-based efficient reinforcement learning (RL) approach for systems with uncertain drift dynamics. A novel concurrent learning adaptive extended observer (CL-AEO) is first designed to jointly estimate the system state and parameter. This observer has a two-time-scale structure and does not require any additional numerical techniques to calculate the state derivative information. The idea of concurrent learning (CL) is leveraged to use the recorded data, which leads to a relaxed verifiable excitation condition for the convergence of parameter estimation. Based on the estimated state and parameter provided by the CL-AEO, a simulation of experience-based RL scheme is developed to online approximate the optimal control policy. Rigorous theoretical analysis is given to show that the practical convergence of the system state to the origin and the developed policy to the ideal optimal policy can be achieved without the persistence of excitation (PE) condition. Finally, the effectiveness and superiority of the developed methodology are demonstrated via comparative simulations.

This article demonstrates the realization of angle tracking and deformation suppression by developing two boundary controllers for a flexible variable-length rotary crane arm with extraneous disturbances and asymmetric input-output constraints. The dynamic model description of this kind of crane arm system is several partial differential equations integrated into few ordinary differential equations. The S-curve acceleration and deceleration scheme is utilized to adjust the elongation rate of the arm. A kind of novel observer is put forward to tackle unknown extraneous disturbances. Auxiliary systems and barrier Lyapunov functions are introduced to meet the asymmetric input-output constraints. With the help of Lyapunov's theory, the global exponential stability and uniform boundedness are analyzed. The numerical simulations are finally provided to illuminate its availability of the designed control schemes.

A robust finite-time control (FTC) framework using continuous terminal sliding-mode control (SMC) and high-order sliding-mode observer (HOSMO) is discussed to realize the trajectory tracking of flexible-joint robots in this article. Control performances of the robots always suffer from unknown matched and mismatched time-varying disturbances. Traditional SMC exists with a chattering phenomenon and cannot cope with mismatched time-varying disturbances due to its inherent structure property. For this reason, two HOSMOs are devised to estimate the time-varying disturbances on the link and motor side, respectively. Then, by fusing the states and disturbance estimations into a novel terminal sliding-mode surface, a continuous robust FTC scheme is developed. The proposed control strategy can not only handle both matched and mismatched time-varying disturbances but also obtain a finite-time convergence performance. The rigorous finite-time stability analysis of the closed-loop system under the proposed control method is guaranteed. The results are illustrated to verify the effectiveness and robustness of the proposed design approach.

With the aid of neural networks, this article develops two data-driven designs of fault detection (FD) for dynamic systems. The first neural network is constructed for generating residual signals in the so-called finite impulse response (FIR) filter-based form, and the second one is designed for recursively generating residual signals. By theoretical analysis, we show that two proposed neural networks via self-organizing learning can find their optimal architectures, respectively, corresponding to FIR filter and recursive observer for FD purposes. Additional contributions of this study lie in that we establish bridges that link model- and neural-network-based methods for detecting faults in dynamic systems. An experiment on a three-tank system is adopted to illustrate the effectiveness of two proposed neural network-aided FD algorithms.

This article is concerned with the secure state estimation problem for cyber-physical systems under intermittent denial-of-service (DoS) attacks. Based on a switching scheme and the cascade observer technique, a novel resilient state observer with a switched compensation mechanism is designed. Moreover, a quantitative relationship between the resilience against DoS attacks and the design parameters is revealed. Compared with the existing results, where only the boundedness of the estimation error is guaranteed under DoS attacks, the exponential convergence of the estimation error is achieved by employing the proposed observer scheme, such that the estimation performance is improved. More specifically, in the disturbance-free case, it is proven that the state estimation error converges exponentially to 0 despite the existence of DoS attacks. Finally, simulation results are provided to illustrate the effectiveness and merits of the proposed methods.

This study focuses on dissipativity-based fault detection for multiple delayed uncertain switched Takagi-Sugeno fuzzy stochastic systems with intermittent faults and unmeasurable premise variables. Nonlinear dynamics, exogenous disturbances, and measurement noise are also considered. In contrast to the existing study works, there is a wider range of applications. An observer is explored to detect faults. A controller is studied to stabilize the considered system. A piecewise fuzzy Lyapunov function is collected to obtain delay-dependent sufficient conditions by means of linear matrix inequalities. The designed observer has less conservatism. In addition, the strict (Q, S,R)-e-dissipativity performance is achieved in the residual dynamic. Besides, the elaborate H∞ performance and the elaborate H performance are also acquired. Finally, the availability of the method in this study is verified through two simulation examples.

This paper considers a new bi-directional cascaded system of a fractional ordinary differential equation (FODE) and a fractional partial differential equation (FPDE) which interacts at an intermediate point. The space-dependent coefficients, interaction between the FODE and FPDE at an intermediate point and the presence of fractional calculus makes the FODE-FPDE cascaded system, representative. In this note, we first apply an invertible integral transformation to convert the system into a FODE-FPDE coupled system, as the target system, which is Mittag–Leffler stable. Using the backstepping method and under some assumptions of the space-dependent coefficients, we work out the kernel functions in the transformation and we design a boundary controller. Also, by the invertibility of the transformation, we show the Mittag–Leffler stability of the closed-loop system via the Lyapunov approach. Second, we propose an observer for which we prove that it can well estimate the original cascaded system. Then, we design an output feedback boundary control law and show that the closed-loop system is Mittag–Leffler stable under the designed output feedback control law. Finally, we present some numerical illustrations to show the correctness of the theoretical results.

Complex dynamical systems rely on the correct deployment and operation of numerous components, with state-of-the-art methods relying on learning-enabled components in various stages of modeling, sensing, and control at both offline and online levels. This article addresses the runtime safety monitoring problem of dynamical systems embedded with neural-network components. A runtime safety state estimator in the form of an interval observer is developed to construct the lower bound and upper bound of system state trajectories in runtime. The developed runtime safety state estimator consists of two auxiliary neural networks derived from the neural network embedded in dynamical systems, and observer gains to ensure the positivity, namely, the ability of the estimator to bound the system state in runtime, and the convergence of the corresponding error dynamics. The design procedure is formulated in terms of a family of linear programming feasibility problems. The developed method is illustrated by a numerical example and is validated with evaluations on an adaptive cruise control system.

Harmonic current estimation is required in active power filters for compensation purposes. The most efficient way of calculating the total harmonic current up to the infinite order is to subtract the fundamental component from the distorted current. Direct determination of the fundamental component of a distorted current of mains frequency was realized recently with a third-order modulation integral observer. This article shows that using an exponential modulation function (Exp-MF) for the integral observer will: 1) significantly enhance the robustness of the observer against noise and 2) automatically remove the low-frequency envelope arising from the D/A and A/C sampling processes compared to previous polynomial modulation function. These new and advantageous features are supported with detailed analysis and experimental verification. The robust observer can be implemented in grid-connected power electronics circuits that require the instantaneous information of the fundamental and/or harmonic currents. Practical comparative tests with the adaptive notch filter and recursive discrete Fourier transform (DFT) methods in an active power filter have confirmed the good performance under both the steady and dynamic states of the proposed Exp-MF integral observer.

In this short paper, relaxed observer-based state estimation of discrete-time Takagi-Sugeno fuzzy systems is proposed via developing a new augmented matrix approach. In contrast with the recent method reported in the literature, a more advanced introduction mode of additional matrices is developed without any redundant constraint under the framework of homogeneous polynomials. Then, the proposed augmented matrices can be built in order to depict the characteristic information of each possible working mode in a superior way, and thus our obtained result is less conservative than the recent one. Moreover, it is worth noting that that the recent method belongs to the special case of ours. Finally, two numerical simulations are given to test and validate the superiority and generality of the developed method.

This article studies the state estimation for probabilistic Boolean networks via observing output sequences. Detectability describes the ability of an observer to uniquely estimate system states. By defining the probability of an observed output sequence, a new concept called detectability measure is proposed. The detectability measure is defined as the limit of the sum of probabilities of all detectable output sequences when the length of output sequences goes to infinity, and it can be regarded as a quantitative assessment of state estimation. A stochastic state estimator is designed by defining a corresponding nondeterministic stochastic finite automaton, which combines the information of state estimation and probability of output sequences. The proposed concept of detectability measure further performs the quantitative analysis on detectability. Furthermore, by defining a Markov chain, the calculation of detectability measure is converted to the calculation of the sum of probabilities of certain specific states in Markov chain. Finally, numerical examples are given to illustrate the obtained theoretical results.

In this article, a distributed adaptive model-free control algorithm is proposed for consensus and formation-tracking problems in a network of agents with completely unknown nonlinear dynamic systems. The specification of the communication graph in the network is incorporated in the adaptive laws for estimation of the unknown linear and nonlinear terms, and in the online updating of the elements in the main controller gain matrix. The decentralized control signal at each agent in the network requires information about the states of the leader agent, as well as the desired formation variables of the agents in a local coordinate frame. These two sets of variables are provided at each agent by utilizing two recently proposed distributed observers. It is shown that only a spanning-tree rooted at the leader agent is enough for the convergence and stability of the proposed cooperative control and observer algorithms. Two simulation studies are provided to evaluate the performance of the proposed algorithm in comparison with two state-of-the-art distributed model-free control algorithms. With lower control effort as well as fewer offline gain tuning, the same level of consensus errors is achieved. Finally, the application of the proposed solution is studied in the formation-tracking control of a team of autonomous aerial mobile robots via simulation results.

In this paper, based on the estimation of optimal slip ratio, a model predictive control approach is proposed for the anti-lock braking system with motor of battery electric vehicle. A wheel dynamics model with a virtual control quantity is established. A method for estimating the optimal slip ratio of different roads is introduced, which consists of a sliding mode observer for longitudinal braking force and a recognizer of slip ratio. A ground slip map that describes the relationship between slip ratio and ground adhesion coefficient is proposed, which is utilized to identify the optimal slip ratio. Based on the model predictive control algorithm, a braking torque controller is designed to track the optimal slip ratio for good performance of braking stability and security. The vehicle control unit embedded with the estimator and model predictive control law is tested in battery electric vehicle on the dry and wet roads. The experimental results indicate that the proposed approach has a good performance for the estimation and control of slip ratio during the anti-lock braking with electric motor, especially suitable for the low adhesion road conditions.

Affine nonlinear systems are special kinds of non-linear systems, and they can represent many practical systems. Dealing with affine nonlinear system based on T-S fuzzy model has drawn much attention in literature recently. In this paper, a robust control method is proposed for affine nonlinear system with disturbance based on T-S fuzzy model. First, an optimization T-S fuzzy model is constructed focusing on optimizing the parameters of the weight functions by using differential evolution algorithm (DEA). Second, in order to reconstruct the disturbance, an interval observer is designed for the affine nonlinear system, and then a novelty disturbance reconstruction method based on the interval observer is developed. The disturbance reconstruction value can asymptotically approach the actual disturbance value, and furthermore, it decouples the control input. Third, by taking the optimization T-S fuzzy model as design model and by introducing the reconstruction of disturbance into controller, a feedback robust controller with disturbance compensation is designed to stabilize the affine nonlinear system. Finally, a simulation example of the crane model is given and some comparisons are provided to verify the effectiveness and show the advantages of the proposed method.

A novel adaptive sliding mode observer-based control strategy is put forward for a type of uncertain singular systems subject to unknown state delay, input nonlinearity and uncertain perturbation in this article. Firstly, the unmeasured state is reconstructed by a particular observer without any inputs, from which a novel linear switching surface is provided. Subsequently, performance analysis of the resultant system on the switching surface is ensured under a new admissibility criterion. An associated adaptive control signal is synthesized to ensure that the established switching surface can be attained in finite moment. Finally, three numerical simulations are conducted to confirm the validity and superiority of the specified method.

It is important to accurately obtain the motion information of the ego vehicle and surrounding vehicles for autonomous vehicles to plan safe trajectories in complicated traffic environments. In this paper, a LiDAR-based estimation method is developed to simultaneously identify the pose and the velocity information of an ego vehicle and its surrounding moving obstacles. Specifically, a pose estimation network is designed to estimate the poses of both the ego vehicle and the obstacles only with the continuous point clouds obtained by the LiDAR mounted on the ego vehicle. In the network, PointNet++ is utilized as the backbone to extract point-wise features and divide the points into the static part and the moving part. The former is used to estimate the ego vehicle's pose, while the latter is applied for the obstacle pose identification. Then, a reduced-order observer is designed to estimate the velocities, whose convergence is proved with the Lyapunov theory. Finally, both simulation and experiment results are provided to show the effectiveness of the proposed method.

Weight-on-Bit is of vital importance to the drilling trajectory orientation in directional drilling. This paper concerns robust control of drilling trajectory with weight-on-bit uncertainty for the directional drilling process. The objective is to develop an equivalent-input-disturbance-based trajectory control scheme such that the drilling trajectory is precisely controlled by suppressing the fluctuations of weight-on-bit. The motion orientation of both the drill bit and a series of stabilizers is used to describe the evolution process of the drilling trajectory. A state-space model with weight-on-bit uncertainty is derived from the evolution equation through a variable transformation. An equivalent-input-disturbance-based trajectory control system is designed, and two control loops are to track and control the trajectory inclination and azimuth, respectively. Two internal models track the trajectory inclination and azimuth respectively to elevate the control accuracy in the trajectory system. Two observer models combined with two low-pass filters estimate the trajectory inclination and azimuth by measuring the bottom hole assembly’s inclination and azimuth. Some sufficient conditions are derived using linear-matrix-inequalities to obtain the control parameters by considering a reasonable fluctuation range of weight-on-bit. Finally, the control effects in the build-up and horizontal section of the drilling trajectory illustrate the proposed approach’s validity.