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This article focuses on the adaptive output feedback control problem for a class of uncertain nonlinear systems under unknown actuator and sensor faults. In contrast to existing works, we consider unknown time‐varying discontinues faults and loosen their conservative conditions on nonlinear functions. First, since there is just inaccurate output information is available, an observer based on dynamic gain is conceived to reconstruct state variables that cannot be measured. Second, the dynamic gain is introduced into the design of virtual controller by coordinate transformation, and is employed to construct Lyapunov function to handle unknown parameters. On the other hand, a new global output feedback control algorithm is proposed by utilizing dynamic gain which improves the flexibility of parameters selection on the premise of ensuring stability. Based on the Lyapunov stability theory, the resulting closed‐loop system is proved strictly all signals are global bounded. Finally, a simulation example is presented to illustrate the efficiency of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

ABSTRACT This article proposes a second‐order discrete‐time sliding mode controller (DSMC) by only using sampled‐data information of the system output. On one hand, different from the continuous‐time sliding mode control, the finite‐time stability of the closed‐loop system under DSMC is usually not kept due to the limited sampling frequency. On the other hand, in the output feedback framework, the second‐order sliding mode observer cannot independently estimate the internal state of the system if there exist unknown mismatched term and unknown time‐varying coefficient of the control input. To solve the above problems, a joint design strategy of second‐order discrete‐time sliding mode observer and sliding mode controller based on the “non‐separation principle” is proposed. The stability and robustness of the closed‐loop system is analyzed based on the Lyapunov design method. Finally, simulation example verifies the correctness of the theory. [ABSTRACT FROM AUTHOR]

This study focuses on designing robust H ∞ controllers to mitigate actuator saturation-related issues of active suspension systems. Two design methods are presented: one utilizing full-state feedback and the other relying on measurable output feedback. The H ∞ state-feedback control incorporates actuator saturation directly into the design, representing the control input with the saturation function in the state-space model. The resulting optimization-based design problem, derived from theoretical stability analyses, poses a non-convex NP-hard problem under bilinear matrix inequality (BMI) constraints. To address this challenge, the study proposes a meta-heuristic optimization-based technique, providing a straightforward yet effective method to manage the BMI problem. The scope extends to practical implementation considerations, where suspension systems may only utilize measurable output variables. The H ∞ static output-feedback control case is explored by extending the design of a full-order state-feedback controller. Despite the BMI-related challenges entailed in this scenario, the proposed meta-heuristic optimization-based design technique demonstrates flexibility in managing BMI conditions, thus eliminating the need to modify the controller design methodology. A noteworthy contribution of this study is its consistent application of the same meta-heuristic optimization-based technique for designing both H ∞ state-feedback and static output-feedback controllers. Experimental results validate the effectiveness of the proposed robust H ∞ control strategies. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive output feedback prescribed performance controller based on the extended state observer (ESO) for hydraulic systems with unmodeled dynamics. Considering the constraint on tracking errors, we have defined an improved convergence time-controllable prescribed performance function. It achieves the predetermined performance of the error signal for the original system by stabilizing the transformed system. In other words, the tracking error will remain within the specified constraint bounds over time. Unlike most existing prescribed performance control methods, this approach combines the advantages of finite-time performance functions and is derived from a modified transformation function related to tracking error and an asymmetric barrier function. It not only removes the constraints associated with initial error conditions, guaranteeing globally superior control performance but also offers flexibility in handling general cases of symmetric or asymmetric output constraints. Furthermore, an ESO is designed to estimate the unmeasured states and unknown dynamics through the output position signal, which avoids contamination of the velocity and acceleration signals by measurement noise and effectively mitigates the influence of most unfavorable factors on control performance. Building upon this foundation, we have developed a low-complexity control approach that integrates performance constraints with the ESO using filter-based backstepping techniques. Ultimately, the stability of the closed-loop system is substantiated using the Lyapunov method, and the effectiveness of the developed approach is validated through simulation. [ABSTRACT FROM AUTHOR]

In this paper, a new observer-based single-phase robustness sliding mode controller (SPRSMC) is proposed for the pitch control of a variable speed wind turbine (VSWT) systems. The finding of this research includes two tasks: i) to ensure a global stability of the VSWT plant, the reaching phase in traditional sliding mode control (TSMC) technique is eliminated and ii) to guess the immeasurable variables of VSWT plants, a novel pitch angle output feedback controller is constructed based on the estimator tool and output information only. Firstly, a single-phase switching function is determined to eject the reaching phase in TSMC. Moreover, an immeasurable variable of the VSWT system is estimated by employing the suggested estimator tool. Next, a SPRSMC for VSWT plant is built based on the support of the estimator instrument and output data only. Furthermore, an appropriate requirement is founded by utilizing the linear matrix inequality (LMI) method for ensuring the robust stability of motion dynamics in sliding mode. Finally, the solution of the suggested control is confirmed the three-blade wind turbine with a 5- MW utilizing the wind turbine simulator fatigue, aerodynamics, structures, and turbulence (FAST) code and the National Renewable Energy Laboratory (NREL). [ABSTRACT FROM AUTHOR]

Quantized signal control has been widely recognized as an effective means for conserving communication resources. However, ensuring stability of quantization‐driven nonlinear systems becomes particularly challenging in the presence of intermittent denial‐of‐service (DoS) attacks and sensor failures. This article explores the stable control problem of quantized nonlinear systems subject to DoS attacks. When an attack exists, all signals are unmeasurable. During the dormant phase of a DoS attack, only the quantized output signal is measurable, while there is a deviation between the received signal and the ideal signal due to sensor failure. To address these challenges, a switch‐type quantized observer is designed in this article to estimate unmeasurable state signals. In addition, we integrate the first‐order dynamic filter into the back‐stepping design process, which not only avoid the obstacles arisen from non‐differentiable output signals, but also simplifies the design process. Furthermore, this article adopts a more flexible sector quantizer and derives a set of adaptive laws to compensate for unknown quantization parameters. It is shown that, by utilizing the estimated signal, the proposed quantized adaptive control is able to effectively fight against DoS attacks and also is fault‐tolerant to sensor failures, guaranteeing semi‐globally uniformly ultimately bounded for all closed‐loop signals. Finally, simulation results validate the effectiveness of the proposed theory. [ABSTRACT FROM AUTHOR]

In networked control systems, the communication load is a main concern for implementing model predictive control (MPC). This paper introduces a self-triggered MPC algorithm under network environments to reduce communication load, for discrete-time linear systems with bounded state and output disturbances, when only the system output can be measured at triggering instants. The proposed algorithm mainly based on a state estimator whose estimation error is bounded by an invariant set, and on the self-triggered model predictive control of a nominal system. Moreover, the new cost function explicitly adopts communication load is in consideration. The proposed algorithm is proved to drive the system to an invariant set with respect to the bounded disturbances. Finally, simulation results is provided to verify the effectiveness of the proposed robust output feedback MPC algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an output feedback controller using iterative learning algorithm is proposed for stabilising a class of uncertain nonlinear single-input single-output (SISO) systems with unknown bounded disturbances. By dividing the time interval into equal iteration periods, iterative learning algorithm is integrated into output feedback control and is carried out under alignment condition. Using the output signal, control input and estimates of uncertain parameters, adaptive state observers are established to generate state estimates which will be employed to design the control law. Based on the backstepping technique, the output feedback controller is developed consisting of the ILC part and robust control part. Different from the traditional ILC update laws of uncertain parameters with constant gains, a novel type of parameter update law is developed where the ILC gain is variable and determined by the real-time state estimates such that system stability can be guaranteed. By virtue of the composite energy function, it is proved that all the signals of the closed-loop system are bounded and the output will uniformly converge to zero along the iteration axis, which indicates the stabilisation of the output signal over the time domain. Simulation verification is conducted to apply the proposed controller to a one-degree-of-freedom suspension system to validate the effectiveness of the control scheme. [ABSTRACT FROM AUTHOR]

As presented in this paper, we explore the control of a discrete-time two–dimensional (2-D) system using the Lyapunov approach. The Giovane–Roesser model (G–R) for 2-D systems was introduced, and we presented the asymptotic stability analysis for this class of systems while maintaining the strictly bounded real (SBR) property. In the next step, we solve the stability problem in the presence of uncertainties in the system while preserving the SBR condition. We design state feedback and output feedback controllers to control 2-D discrete-time systems with preceding uncertainties, introducing algorithms to design such controllers. In order to ensure the validity of our findings, we present the simulation results as numerical and practical examples. [ABSTRACT FROM AUTHOR]

In multi-area interconnected power systems (MAIPS), all the plant state's measurement is stiff due to the lack of a device or the cost of the sensor is expensive. To solve this restriction, a novel sliding mode control techniquebased load frequency controller (LFC) is investigated for MAIPS where the estimation states of the system is utilized fully in the switching surface and controller. Initially, a single-phase switching function is suggested to dismiss the reaching phase in traditional sliding mode control (TSMC) approach. Secondly, the MAIPS's unmeasurable variables is estimated by using the suggested observer tool. Next, a new single phase robustness load frequency sliding mode controller (SPRLFSMC) for the MAIPS is established based on the support of the observer instrument and output data only. The entire plant's stability is ensured through the Lyapunov theory. Even though the plant's variables are not measured, the obtained results in the simulation display that the frequency remains in the nominal domain under load instabilities on the MAIPS. The simulation results for a three-area interconnected electricity plant verify the preeminence of the anticipated SPRLFSMC over other current controllers with respect to settling time and overshoot. [ABSTRACT FROM AUTHOR]

In the paper, a novel approach to decentralized controller design for nonlinear systems is introduced. The proposed method is based on the relationship between the stability of complex nonlinear systems and the stability of their subsystems. The design procedure of the decentralized controller consists of three steps. In the first step, the stability of the complex nonlinear system is calculated. In the second step, stability conditions at the subsystem level are obtained such that guarantee the stability of the complex nonlinear system. Finally, in the third step, a controller design method is used to ensure that the subsystem stability conditions obtained in the second step are met. As an example, to better understanding the proposed method two simple nonlinear models are used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Linear–quadratic regulator (LQR) is a control technique successfully implemented for several complex systems. However, LQR technique in certain cases fails due to disturbances mainly caused by the improper choice of weighting matrices Q and R. Methods using particle swarm optimisation (PSO) are used to find the weighting matrices of LQR and have been proven to be superior. The paper proposes an output feedback LQR proportional–integral–derivative (PID) controller, which is also compared with LQR and PSO-based LQR. The output feedback LQR–PID provides a better response, and the effectiveness of the proposed method is demonstrated through simulations, and experimental validations are conducted on various cases. [ABSTRACT FROM AUTHOR]

This study focuses on designing robust H∞ controllers to mitigate actuator saturation-related issues of active suspension systems. Two design methods are presented: one utilizing full-state feedback and the other relying on measurable output feedback. The H∞ state-feedback control incorporates actuator saturation directly into the design, representing the control input with the saturation function in the state-space model. The resulting optimization-based design problem, derived from theoretical stability analyses, poses a non-convex NP-hard problem under bilinear matrix inequality (BMI) constraints. To address this challenge, the study proposes a meta-heuristic optimization-based technique, providing a straightforward yet effective method to manage the BMI problem. The scope extends to practical implementation considerations, where suspension systems may only utilize measurable output variables. The H∞ static output-feedback control case is explored by extending the design of a full-order state-feedback controller. Despite the BMI-related challenges entailed in this scenario, the proposed meta-heuristic optimization-based design technique demonstrates flexibility in managing BMI conditions, thus eliminating the need to modify the controller design methodology. A noteworthy contribution of this study is its consistent application of the same meta-heuristic optimization-based technique for designing both H∞ state-feedback and static output-feedback controllers. Experimental results validate the effectiveness of the proposed robust H∞ control strategies.

The paper proposes a command filtering adaptive output feedback control scheme with preset performance for a Robotic Arm Model (RAM) designed specifically for steel structures. Initially, a neural network observer is employed to approximate the nonlinear functions within the model and to estimate the unmeasured states of the system. Subsequently, within the backstepping framework, the integration of preset performance theory and command filtering technology addresses the differential complexity challenges commonly encountered in traditional backstepping methods. This approach ensures rapid convergence of the system’s tracking error within predetermined boundaries. The efficacy of this strategy is demonstrated through simulation instances.

To address the issue of inadequate control effect resulting from the challenge of measuring state feedback information comprehensively, this paper proposes an iterative output feedback energy-to-peak control method that solely depends on partial external observation data. This paper presents the computational steps for achieving a suboptimal solution to the output feedback bilinear matrix inequality constrained problem using an iterative linear matrix inequality approach. The proposed method overcomes the challenge of numerically solving the optimization problem. The unknown variables in the output feedback linear matrix inequality optimization problem are transformed equivalently to obtain a new linear matrix inequality optimization problem. The numerical results of the two optimization problems are then used to iterate with each other and obtain a suboptimal solution. A comprehensive analysis was conducted on an 8-story building structure, utilizing state feedback, output feedback, and iterative output feedback energy-to-peak control techniques. The results of the study indicate that the proposed iterative output feedback energy-to-peak control approach efficiently reduces the displacement and acceleration response of the structure, exhibiting superior control effectiveness than both the state feedback and output feedback energy-to-peak control methods. The findings demonstrate the practicality and versatility of the suggested method.

Summary This work presents an approach to control a type of stochastic cascade nonlinear systems, which involve external disturbances and nonlinear terms. The external disturbances under the consideration are not measurable and regarded as extended states. The proposed method addresses the problem of unmeasured states and external disturbances by introducing an extended state observer (ESO). Based on the above work, a new stochastic nonlinear system including ESO is established. To ensure the stability and boundedness of the system, an adaptive output feedback controller is designed by using stochastic integrated input‐to‐state stability and backstepping design technique. This controller guarantees that all the states remain almost surely bounded and globally asymptotically stable in probability. Simulation experiments are conducted to validate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the distributed output feedback consensus control for a class of uncertain nonlinear multi-agent systems (MASs). The dynamics of subsystems are heterogeneous and contain unknown control gains. A novel distributed fuzzy consensus control protocol is constructed by employing variable separation technology and adaptive backstepping technique. The main advantage of the protocol is that it is distributed only depending on the relative output information. Moreover, it does not require the observer information exchange, which can avoid network attacks. A numerical example is given to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper studies both synchronous and asynchronous consensus problems for linear multi‐agent systems under switching topology. A novel distributed continuous‐discrete observer is established by solely using the relative sampled output messages, which needs neither to store messages from neighboring agents nor estimate the information of itself and all neighbors. Based on the estimation provided by the distributed observer, a distributed output feedback control with digital form is synthesized, which allows each agent to update the value of its controller at the synchronous/asynchronous sampling instants. It is of interest to see that, for the class of chain‐integrator multi‐agent systems, the proposed control approach allows the sampling periods to be chosen freely and thus would be more practical in some situations from the perspective of energy saving. The theoretical results are illustrated by formation flying of spacecraft and synchronization of multiple double‐integrators. [ABSTRACT FROM AUTHOR]

Summary: A partial model‐free, data‐driven adaptive optimal output feedback (OPFB) control scheme with low computational cost continuous‐time is proposed in this paper. The design objective is to obtain the optimal control law by using measurable input and output data, without some knowledge of system model information. Firstly, the system states are decoupled into measurable and unmeasurable parts, and a new state‐space equation is built to estimate the unmeasurable states by using a reduced‐order observer. Based on this, a parametrization method is utilized to reconstruct the system states. Subsequently, by using the reconstructed states, the adaptive dynamic programming (ADP) Bellman equations based on policy‐iteration (PI) and value‐iteration (VI) are presented to solve the control problems with initially stable and unstable conditions, respectively. Then, the convergence of the system is proved. Compared with the early proposed OPFB algorithms, only the unknown internal state needs to be reconstructed. Therefore, the computation cost and design complexity are reduced for the proposed scheme. The effectiveness of the proposed scheme is verified through two numerical simulations. In addition, a practical inverted pendulum experiment is carried out to demonstrate the performance of the proposed scheme. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A new output feedback control approach is proposed to address the global practical tracking issue for a class of nonlinear input-quantization systems with polynomial growth condition, which introduces a non-identification adaptive gain in the backstepping design instead of constant control gain. This approach is applicable to any quantizer with sector-bounded characteristic (e.g., logarithmic quantizer, hysteresis quantizer). Except for using the quantized signal in the observer, the controller design in this article is completely independent of the input quantizer due to the introduction of adaptive gain. Different from the existing results, a suitable quantizer can be selected as needed and it is unnecessary to redesign the observer and controller. By combining the dynamic-gain scaling technique with the backstepping method, the design problem of control gains is transformed into choose the dynamic gain indexes, which significantly reduces the computational complexity of designing controller. [ABSTRACT FROM AUTHOR]

This paper proposes an output feedback-based control for uncertain electro-hydrostatic systems (EHSs) to satisfy high output tracking precision under the influences of unknown mismatched and matched uncertainties and unstructured dynamical behavior. In this configuration, an extended state observer (ESO) is first employed to obtain unmeasured states and suppress the adverse effect of matched uncertainty. Meanwhile, the influence of unstructured dynamical behavior is approximated by employing a radial basis function neural network (RBFNN)-based technique. With the unmeasured states observed, matched uncertainty, and system dynamics compensated, the robust backstepping sliding mode control is accordingly established and the lumped mismatched uncertainty is then suppressed through disturbance observer-based adaptive law. Interestingly, the proposed control methodology requires only output feedback but can address the whole system dynamics. The stability of the closed-loop system is theoretically proven through a Lyapunov theorem and the effectiveness of the proposed methodology is demonstrated through comparative simulations. [ABSTRACT FROM AUTHOR]

In this article, we investigate the event‐triggered cooperative robust output regulation problem of a class of minimum‐phase linear uncertain multi‐agent systems. In particular, we first propose a dynamic event‐triggered mechanism and an event‐triggered distributed output feedback control law. Then, by the internal model, the event‐triggered cooperative robust output regulation problem is converted into the event‐triggered cooperative robust stabilization problem of an augmented system composed of the distributed internal model and the original multi‐agent system. Next, we show that the event‐triggered cooperative robust stabilization problem can be solved by the proposed event‐triggered distributed output feedback control law together with a dynamic event‐triggered mechanism, and thus the event‐triggered cooperative robust output regulation problem is solved exactly in the sense that the tracking error of each subsystem converges to zero asymptotically while preventing the Zeno behavior from happening. Finally, we provide an example to illustrate the proposed control approach. [ABSTRACT FROM AUTHOR]

This article investigates the global stabilisation problem via adaptive output feedback for a class of uncertain nonlinear systems. It is worth emphasising that the system under investigation possesses function control coefficients, input matching uncertainty and the polynomial-of-output growth rate, which is essentially different from the existing related literature, and hence brings some technical difficulties to the control design. To solve this problem, an adaptive output-feedback controller with dynamic high-gain and extended state observer is proposed. Remarkably, only one dynamic gain is introduced to overcome the function control coefficients, the serious uncertainty and the polynomial-of-output in the system growth rate. Moreover, under the designed controller, the states of the original system globally converge to zero. Two simulation examples are given to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed-time output feedback stabilization problem for a class of nonlinear time-delay systems. First, a novel dual-gain function is put forward by exploiting the dynamic gain and the time-varying gain function to design the reduced-order observer for reconstructing unavailable states. Then, by utilizing the Lyapunov--Krasovskii functional and state variables of the reduced-order observer, a new prescribed-time controller is presented based on the nonscaling design framework. Since no state scaling is required in controller design process under this framework, our control strategy is simpler and can greatly reduce the computational burden. Further, compared with the previous prescribed-time stabilization results, our designed controller acts on the entire time domain, not just a limited time interval. Based on our proposed stability criterion, it is proved that the controller can render that all system state variables converge to the origin within the prescribed time. Finally, a numerical example is provided to illustrate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a fixed-time fuzzy adaptive output feedback control scheme for the robotic arm model (RAM) of steel structures. Firstly, the process of transforming RAM into a nonlinear system is elaborated. Secondly, a fuzzy observer is designed to approximate the nonlinear function and estimate the observed state of the system. Subsequently, a fixed-time adaptive controller is constructed, ensuring the system's stability within a fixed time, with the convergence time unaffected by the initial state. Finally, the effectiveness of the strategy is verified through simulation examples. [ABSTRACT FROM AUTHOR]

Failure of any of the drive systems in a Four-Wheel Independent Drive (4WID) electric vehicle may affect the control performance and driving safety of the whole vehicle. Therefore, in this paper, a fault-tolerant controller (FTC) for 4WID electric vehicles considering drive actuator failures is proposed. First, a comprehensive characterization of multiple fault types is achieved by establishing a generalized fault model and designing a comprehensive fault factor. Second, based on the comprehensive fault factor, an LPV model with faults is constructed. Further, a fault-tolerant controller based on LPV/H∞ output feedback is designed by combining the weighting function. Finally, the effectiveness of the FTC in this paper is verified by simulation and hardware-in-the-loop (HIL) experiments. The experimental results show that the FTC designed in this paper can improve the stability of the vehicle traveling while ensuring tracking accuracy when the drive system fails. [ABSTRACT FROM AUTHOR]

This paper presents an observer-based dynamic output-feedback controller design procedure using linear matrix inequality (LMI) optimization for second-order systems with uncertainty and persistent perturbation in the states. Using linear-quadratic criteria, cost functions are minimized in a two-stage procedure to compute optimal state-feedback gains, and observer gains are coupled into a dynamic output-feedback optimal controller. The LMI set used in the two stages is matrix inversion free, a key issue for polytope formulation when uncertainty is present. The approach is tested in a mobile inverted pendulum robotic platform, and the effectiveness is verified in this underactuated and undesensed case. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an adaptive observer based output feedback prescribed‐time control is designed for a class of nonlinear systems with unknown parameters. According to the characteristics of the nonlinear systems, a novel adaptive observer is first designed. Then by imposing parametric Lyapunov equations, a time‐varying high‐gain observer based output feedback adaptive controller is constructed, by which, it is proven that the states of both the observer and the original system can be driven to zero within any prescribed time. Moreover, the control signal is proven uniformly bounded by a scalar. Two numerical examples are illustrated to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an observer‐based event‐triggered distributed model predictive control method is proposed for a class of nonlinear interconnected systems with bounded disturbances, considering unmeasurable states. First of all, the state observer is constructed. It is proved that the observation error is bounded. Second, distributed model predictive controller is designed by using observed value. Meanwhile, the event‐triggered mechanism is set by using the error between the actual output and the predicted output. The setting of event‐triggered mechanism not only ensures the error between the actual output and the predicted output within a certain range, but also reduces the calculation amounts of solving the optimization problem. The states of each subsystem enter the terminal invariant set by distributed model predictive control, and then are stabilized in the invariant set under the action of output feedback control law. In addition, sufficient conditions are given to ensure the feasibility of the algorithm and the stability of the closed‐loop system. Finally, the numerical example is given, and the simulation results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel reinforcement learning (RL)-based event-triggered (ET) output feedback control algorithm is proposed for a class of uncertain strict-feedback nonlinear discrete-time systems. In contrast to traditional RL-based control methods, we proposed an ET output feedback controller based on the backstepping technique, where the transmission cost can be efficiently conserved. Then, in light of the radial basis function (RBF) neural network (NN), various critic NNs are constructed to approximate the critic functions in each step. Furthermore, with the backing of the proposed ET mechanism, a sampled output feedback controller is addressed to guarantee that the tracking errors and all signals of the closed-loop system are semi-global uniformly ultimately bounded (SGUUB). Finally, a simulation example is presented to demonstrate the effectiveness of the control strategy. [ABSTRACT FROM AUTHOR]

It is well known that unknown modeling errors cannot be avoided in practice. Such unmeasured uncertainties are usually denoted as unknown nonlinear functions that exist in every channel of the system equation. This paper aims to develop an output feedback adaptive control scheme by constructing state estimation filters to address such unknown modeling errors. In the controller design, these uncertainties caused by modeling errors will be accumulated to the last step for compensation. Unknown parameters existing in the upper bound functions of unknown nonlinear functions and system parameters are estimated synchronously based on tuning function approaches. It is shown that the designed output feedback controller can ensure the stability and tracking performance of the closed‐loop system, and the transient performance in terms of a truncated norm is also established. [ABSTRACT FROM AUTHOR]

This paper addresses the output feedback adaptive resilient control problem for a class of stochastic nonlinear systems under sensor and actuator attacks. By applying a linear state transformation, the original system is transformed into a new one for which output feedback control becomes possible. The Nussbaum function is exploited to overcome the unknown control direction problem generated by uncertain control coefficients and adversary attacks. Based on the available compromised output signal and the states reconstructed by a new extended state observer, an output feedback adaptive resilient control scheme is proposed for such systems by combining the neural-networks-based approximation approach, backstepping technique and some special methods. The proposed controller can guarantee that all signals in the closed-loop system are semiglobally bounded in probability and the stabilisation error variables remain in an adjustable vicinity of origin. Finally, the effectiveness and applicability of the proposed control methodology is verified by two illustrative examples. [ABSTRACT FROM AUTHOR]

This study investigates an event-triggered disturbance estimation approach for the inner-formation system. An extended state observer is designed using an event-based sampling scheme, which offers advantages over traditional estimation methods by reducing information transmission and unnecessary output information exchange while ensuring accurate system estimation performance. Additionally, a method for designing output-feedback control is proposed. The separation of feedback control and event-based observation in the design of output feedback allows us to apply existing optimal control algorithms to the targeted plant without compromising our established event-triggered sampling methods. A numerical simulation is presented, and we demonstrate the effectiveness of our proposed approach for the inner-formation system. [ABSTRACT FROM AUTHOR]

At present, one typical control strategy for guaranteeing transient and steady-state performance is funnel control and prescribed performance control. The strategy features completely discarding the system nonlinearities, even if they are completely known and available. Such an intrinsic feature requires the controller to produce a larger control effect to eliminate the negative impact caused by the high nonlinearities, leading to a conservative controller. In this paper, we fully take advantage of known information on nonlinearities in control design, instead of completely discarding it as done in funnel control. Particularly, we leverage adaptive techniques (i.e., high-gain dynamic compensation) to deal with unknown system nonlinearities. Meanwhile, we integrate the tools of output feedback, tracking control, and the performance guarantee. As such, an adaptive output-feedback scheme is developed for global tracking with spatiotemporal performance specifications: arbitrarily given tracking accuracy and accuracy-ensured time. A simulation example supports the developed approach. [ABSTRACT FROM AUTHOR]

In this paper, a disturbance-observer–based control approach is developed for overhead crane systems. Different from existing control strategies, the issues consisting of the output feedback, input saturation, double-pendulum dynamics, and uncertain disturbances are taken into consideration here. In particular, a disturbance observer is designed first, which can exactly estimate uncertain disturbances. Next, to enhance the performance of the controller, a virtual position signal is constructed and a corresponding Lyapunov function is introduced. Then, based on the provided Lyapunov function and the designed disturbance observer, a composite control approach is developed for overhead crane systems with double-pendulum dynamics and the convergence of the system states is proved via rigorous theoretical analysis. Finally, the effectiveness and robustness of the proposed control approach are verified by simulation tests. [ABSTRACT FROM AUTHOR]

The emerging trans-media vehicle is significant due to its amphibious ability. A finite-time output feedback trans-media tracking control scheme is proposed for a slender body trans-media vehicle with unknown time-varying hydrodynamics and external disturbances. First, a novel neural network extended state observer is developed to observe the vehicle's velocities and handle the time-varying hydrodynamics and total disturbances simultaneously. Then, combined with the proposed observer, the finite-time command filtered backstepping technique is carefully constructed to yield the finite-time output feedback tracking control. The strength of the proposed approach to the existing methods is that it ensures the trans-media tracking errors converge to the small region of origin within a finite time, even in the absence of velocity measurements. The simulations are given to illustrate the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this article, we consider the nonfragile containment control problem of nonlinear multi‐agent systems (MASs) with exogenous disturbance where the communication links among agents under consideration is directed. Firstly, based on relative output measurements between the agent and its neighbors, a disturbance observer‐based control protocol is proposed to solve the containment control problem of MASs with inherent nonlinear dynamics and exogenous disturbances. Secondly, because of the additional tuning of parameters in the real control systems, uncertainties in the designing of observer and controller gains always occur, and as a result, an output feedback controller with disturbance rejection is conceived and the containment control problem of nonlinear MASs with nonfragility is thoroughly investigated. Then, depending on matrix transformation and inequality technique, sufficient conditions of the designed controller gains exist, which is derived from the asymptotic stability analysis problem of some containment error dynamics of MASs. Finally, two simulation examples are exploited to illustrate the effectiveness of the proposed techniques. [ABSTRACT FROM AUTHOR]

Summary: We consider a global regulation problem for a chain of integrators with an unknown time‐varying input delay by event‐triggered output feedback control. We show that the considered system is globally regulated by the proposed event‐triggered output feedback controller with a dynamic gain‐scaling factor. Moreover, we show that interexecution times are lower bounded and these lower bounds can be increased with our control method. An example is given for clear illustration. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Donghua University (English Edition) is the property of Journal of Donghua University Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper considers the output tracking problem for uncertain nonaffine systems. A robust output tracking controller for a class of uncertain nonaffine strict-feedback systems with guaranteed tracking error bounds is proposed. First, a diffeomorphism is employed to convert the strict-feedback nonaffine system with mismatched uncertainties into a feedback linearization nonaffine system (FLNS). Second, for the transformed FLNS, an error transformation and sliding surface technique are combined to transform the constrained tracking problem of a high-order system into an unconstrained stabilization problem of a first-order system when visualizing the guaranteed tracking error bound as a tracking error constraint. Then, a saturated approximate dynamic inversion scheme and an extended state observer are combined to achieve the stabilization of the transformed first-order nonaffine system. The stability of the closed-loop system is analyzed rigorously and it is sufficient to guarantee that the tracking error constraint can be achieved and all system states are semi-globally uniformly bounded for the original system. Simulation results clarify and verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In tasks that require improved mechanical strength and load-bearing capacity, such as the handling of heavy or large-volume objects, multi-robot collaborative control is of utmost importance. In this paper, a novel control framework is introduced for multi-robot cooperation, aiming to address the challenges presented by dynamic coupling, anisotropy, the lack of velocity information, and the significant network transmission load within large-scale robot cooperation systems. This framework draws upon insights from both vibration theory and control theory. Firstly, a novel decoupling modal space is presented for multi-robots to complete a collaborative task, which means each control channel is not affected by the coupling of other control channels. Then, a distributed filter is designed for systems to avoid the use of velocity measurement information, which ensures that the output feedback control of multiple robots is realized and makes the estimation error converge to zero uniformly, exponentially and globally. Moreover, distributed adaptive event-triggered protocols are developed that are independent of network scale and do not rely on global information. In this study, the controller and communication do not need to be continuously updated. Finally, experimental demonstrations are provided to show the effectiveness of the proposed control algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)