Search

Feedback system design is often achieved by neglecting the unmodeled dynamics, such as the actuator and sensor, to reduce design complexity. It is based on an assumption that the unmodeled dynamics are fast enough to be negligible. However, it may cause severe problems for the stability or performance of the overall system, especially, when the controller contains the fast dynamics or uses the high-gain feedback term. A disturbance observer has been widely employed in many industrial applications due to its simple structure and powerful ability to reject disturbances and compensate plant uncertainties. However, since the disturbance observer contains fast dynamics in its structure, the analysis of the effect of the unmodeled dynamics on the disturbance observer-based control is mandatory. This paper reveals the robustness and disturbance rejection performance of the disturbance observer based on the singular perturbation theory and proposes its design guideline for robust stability in the presence of unmodeled dynamics. In addition, this paper presents that the disturbance observer recovers a nominal performance designed for a nominal model of the plant. [ABSTRACT FROM AUTHOR]

The article explores the fixed-time tracking control (FTTC) problem of strict-feedback nonlinear systems with unmodeled dynamics and dynamic disturbances. For the first time, a novel fixed-time dynamic signal is presented to address unmodeled dynamics. Associating adaptive backstepping control technology and fixed-time Lyapunov stability theory, an adaptive FTTC strategy is constructed, which guarantees that all signals of the closed-loop system are bounded and the tracking error can converge to a small region of zero within a fixed-time range. Finally, two simulation results illustrate the feasibility and validity of the suggested strategy. [ABSTRACT FROM AUTHOR]

We delved into a category of output feedback nonlinear systems that are distinguished by unmodeled dynamics, quantized input delays, and dynamic uncertainties. We introduce a novel finite-time adaptive dynamic surface control scheme developed through the construction of a first-order nonlinear filter. This approach integrates Young's inequality with neural network technologies. Then, to address unmodeled dynamics, the scheme incorporates a dynamic signal and utilizes Radial Basis Function (RBF) neural networks to approximate unknown smooth functions. Furthermore, an auxiliary function is devised to mitigate the impact of input quantization delays on the system's performance. The new controller design is both simple and effective, addressing the "hasingularity" problems typically associated with traditional finite-time controls. Theoretical analyses and simulation outcomes confirm the effectiveness of this approach, guaranteeing that all signals in the system are confined within a finite period.

In this paper, the control problem of prescribed-time adaptive neural stabilization for a class of non-strict feedback stochastic high-order nonlinear systems with dynamic uncertainty and unknown time-varying powers is discussed. The parameter separation technique, dynamic surface control technique, and dynamic signals were used to eradicate the influences of unknown time-varying powers together with state and input unmodeled dynamics, and to mitigate the computational intricacy of the backstepping. In a non-strict feedback framework, the radial basis function neural networks (RBFNNs) and Young's inequality were deployed to reconstruct the continuous unknown nonlinear functions. Finally, by establishing a new criterion of stochastic prescribed-time stability and introducing a proper bounded control gain function, an adaptive neural prescribed-time state-feedback controller was designed, ensuring that all signals of the closed-loop system were semi-global practical prescribed-time stable in probability. A numerical example and a practical example successfully validated the productivity and superiority of the control scheme.

ABSTRACT This article focuses on adaptive control of a class of non‐strict feedback nonlinear fractional order systems with input delay and unmodeled dynamics under prescribed performance constraints. Command filtering backstepping design method is employed to avoid explosion of computation. Additionally, an error compensation mechanism is established to mitigate any errors introduced by the command filter. Radial basis function neural network is utilized to approximate the nonlinear function. Auxiliary signal processing variables are introduced to handle unmodeled dynamics. To address the input delay problem, a Pade approximation technique is employed. The stability analysis of the controller is conducted using Lyapunov stability theory, ensuring that the tracking error converges within a narrow predefined performance range. Finally, simulation results are presented to demonstrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

We delved into a category of output feedback nonlinear systems that are distinguished by unmodeled dynamics, quantized input delays, and dynamic uncertainties. We introduce a novel finite-time adaptive dynamic surface control scheme developed through the construction of a first-order nonlinear filter. This approach integrates Young's inequality with neural network technologies. Then, to address unmodeled dynamics, the scheme incorporates a dynamic signal and utilizes Radial Basis Function (RBF) neural networks to approximate unknown smooth functions. Furthermore, an auxiliary function is devised to mitigate the impact of input quantization delays on the system's performance. The new controller design is both simple and effective, addressing the "hasingularity" problems typically associated with traditional finite-time controls. Theoretical analyses and simulation outcomes confirm the effectiveness of this approach, guaranteeing that all signals in the system are confined within a finite period. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a command filter‐based decentralized adaptive backstepping practical prescribed‐time (PPT) tracking control scheme for a class of non‐strict feedback interconnected systems with time varying parameters, unknown control coefficients, unmodeled dynamics, input deadzone and saturation. By the aid of the characteristics of Gaussian functions, the obstacles arising from the non‐strict feedback terms are successfully solved. By constructing a novel time‐varying scaling function and utilizing nonlinear mapping, the PPT tracking control is developed. The estimations of dynamical uncertainties resulting from unmodeled dynamics are accomplished by employing auxiliary signals, while the unknown continuous terms are characterized by the aid of radial basis function neural networks (RBFNNs). A superposition of two hyperbolic tangent functions is utilized to approximate input nonlinearity. Utilizing the compact set defined in the command filtered backstepping technique, the problem of unknown control direction is solved without using the Nussbaum gain technique. All the signals involved are proved to be semi‐global uniform ultimate bounded, and the tracking error can enter the pre‐specified convergence region within a pre‐specified time. Simulation results are used to demonstrate the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

Aiming at alleviating the adverse influence of coupling unmodeled dynamics, actuator faults and external disturbances in the attitude tracking control system of tilt tri-rotor unmanned aerial vehicle (UAVs), a neural network (NN)-based robust adaptive super-twisting sliding mode fault-tolerant control scheme is designed in this paper. Firstly, in order to suppress the unmodeled dynamics coupled with the system states, a dynamic auxiliary signal, exponentially input-to-state practically stability and some special mathematical tools are used. Secondly, benefiting from adaptive control and super-twisting sliding mode control (STSMC), the influence of the unexpected chattering phenomenon of sliding mode control (SMC) and the unknown system parameters can be handled well. Moreover, NNs are employed to estimate and compensate some unknown nonlinear terms decomposed from the system model. Based on a decomposed quadratic Lyapunov function, both the bounded convergence of all signals of the closed-loop system and the stability of the system are proved. Numerical simulations are conducted to demonstrate the effectiveness of the proposed control method for the tilt tri-rotor UAVs. [ABSTRACT FROM AUTHOR]

In this paper, the control problem of prescribed-time adaptive neural stabilization for a class of non-strict feedback stochastic high-order nonlinear systems with dynamic uncertainty and unknown time-varying powers is discussed. The parameter separation technique, dynamic surface control technique, and dynamic signals were used to eradicate the influences of unknown time-varying powers together with state and input unmodeled dynamics, and to mitigate the computational intricacy of the backstepping. In a non-strict feedback framework, the radial basis function neural networks (RBFNNs) and Young's inequality were deployed to reconstruct the continuous unknown nonlinear functions. Finally, by establishing a new criterion of stochastic prescribed-time stability and introducing a proper bounded control gain function, an adaptive neural prescribed-time state-feedback controller was designed, ensuring that all signals of the closed-loop system were semi-global practical prescribed-time stable in probability. A numerical example and a practical example successfully validated the productivity and superiority of the control scheme. [ABSTRACT FROM AUTHOR]

Summary: The problem of adaptive fuzzy fixed‐time tracking control based on a category of nonlinear systems with unmodeled dynamics and dynamic disturbances is investigated in this article. By introducing the novel fixed‐time dynamic signal, the unmodeled dynamics can be disposed of. On the basis of the fuzzy logic systems (FLSs) and adaptive backstepping technology, the disposing difficulty of the unknown nonlinear portions is reduced. Then, all signals in the closed‐loop system are ensured to be bounded under the fixed‐time Lyapunov stability theory. Simultaneously, the tracking error converges to a small neighborhood of the origin. Eventually, simulation consequences reveal the validity of the presented control method. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the problem of adaptive tracking control for multi‐input and multi‐output (MIMO) non‐strict feedback systems with unmodeled dynamics and full state time‐varying constraints. To tackle the interference of unmodeled dynamics, the dynamic signal generated by the auxiliary system is used. Hyperbolic tangent function is used as a nonlinear mapping tool to transform the constrained system into an unconstrained one. Hysteresis quantizer is introduced to mitigate the chattering phenomenon and quantization error in the quantization signal. The derivative of virtual signal can be approximated more efficiently by command filter. Furthermore, an error compensation mechanism is established to mitigate the error introduced by the command filter. Unknown nonlinear functions are approximated by radial basis function neural networks (RBFNNs). Stability analysis of the proposed controller is performed through the Lyapunov stability theory and the output tracking error can be constrained within a specified range. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the problem of decentralized adaptive proportional‐integral (PI) tracking control is investigated for a class of strong interconnected nonlinear systems in presence of unmodeled dynamics and uncertain input delays. A novel compensation mechanism is proposed to deal with the considered strong interconnection functions by applying a smooth switching function to the design algorithm, and the difficulty generated by unmodeled dynamics is dominated by using a dynamic auxiliary signal. By utilizing the suitable error transformations and selecting the appropriate Lyapunov‐Krasovskii function, the effects of uncertain input delays could be surmounted. The novelty of this study is that, for the first time, a new decentralized adaptive PI tracking control scheme including a switching compensation mechanism is developed for the considered strong interconnected nonlinear systems. It is proved that all the signals in the closed‐loop systems are uniformly ultimately bounded (UUB) and the tracking errors converge to a bounded compact set. Finally, a simulation example is carried out to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive fault-tolerant control method for nonstrict-feedback nonlinear systems with unmodeled dynamics and output dead-zone in the presence of actuator faults. A dynamic signal is used to handle the unmodeled dynamics and a multi-dimensional Taylor network (MTN) to approximate unknown functions. The presented adaptive fault-tolerant control method ensures that all signals in closed-loop systems are semi-globally uniformly ultimately bounded (SGUUB) by applying the Lyapunov stability theory. It also guarantees that the tracking error will eventually converge to a bounded region around the origin. Finally, a numerical example and a real-world application of a one-link manipulator system are used to illustrate the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

This article realizes an adaptive finite‐time sampled‐data output‐feedback stabilization for a class of fractional‐order nonlinear systems with unmodeled dynamics and unavailable states. K‐filters are constructed to estimate unavailable states, a dynamic signal is introduced to handle unmodeled dynamics and neural networks were used to approximate uncertain nonlinearities existed in stabilizer construction. With the help of backstepping technique, an adaptive sampled‐data output‐feedback stabilizer is exported, and such stabilizer with allowable design parameters and sampling period can render the corresponding closed‐loop system reaches practically finite‐time stable, which can be demonstrated by means of selected Lyapunov function candidates. In the end, two simulations with a numerical and an engineering examples are presented to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

A new finite-time adaptive tracking control scheme based on prescribed performance was developed to solve the control problem of non-strict feedback systems with input delays and dynamic uncertainties. The time-delay systems were transformed into delay-free systems by Pade approximation and auxiliary intermediate variable, and the unmodeled dynamics was handled by the dynamic signal generated by the first-order auxiliary system. The prescribed performance adaptive tracking control was implemented by the hyperbolic tangent function, and the stability analysis was presented based on dynamic surface control method. Taking the second-order nonlinear system with unmodeled dynamics and input delay as example, the numerical simulation of the proposed control strategy was conducted in MATLAB environment. The results show that the proposed control scheme can avoid the singularity in the derivation of virtual control, and all signals in the closed-loop system are bounded in finite time. The tracking error can converge to prescribed time-varying region, and the control algorithm is effective. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive bearing rigid formation control strategy for a class of nonlinear system with unknown dead-zone inputs and external disturbance is proposed. Firstly, the I-Type fuzzy system is used to approximate the unknown nonlinear dynamics of the formation model, and the approximation errors and unknown external disturbance are eliminated by the parameter adaptive estimation. Furthermore, the adaptive dynamic estimation algorithm is utilized to estimate and compensate the unknown dead-zone parameters, effectively suppressing the impact of dead-zone on formation system performance. Finally, the stability of the formation system is proved based on LaSalle's invariance principle, and the effectiveness of the algorithm is verified by simulation results. [ABSTRACT FROM AUTHOR]

This article concentrates on the problem of fixed-time tracking control for a certain class of nonlinear systems with unmodeled dynamics. Unmodeled dynamics are prevalent in practical engineering systems, such as axially symmetric systems like robotic arms, spacecraft, and missiles. In this paper, the fuzzy-logic systems (FLSs) are implemented to address the challenge of accurately approximating the unknown nonlinear terms that arise during the derived control algorithm process. By employing fixed-time command filters (FTCF), the "explosion of complexity" issues encountered in traditional backstepping methods will be effectively resolved. Moreover, error compensation mechanisms are derived to effectively mitigate the filtering errors that may arise from the FTCFs. The computational burden associated with FLSs is reduced through the utilization of the weight vector estimation method based on the maximal norm and an adaptive approach. A fixed-time adaptive fuzzy tracking controller is developed within the backstepping control framework to ensure the boundedness of all signals and achieve fixed-time convergence of the tracking error for the controlled system. Illustrative examples are conducted to illustrate the viability of the derived controller. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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 finite-time prescribed performance outputfeedback adaptive control method based on event triggering is proposed for uncertain nonlinear systems with unmodeled dynamics. Firstly, dynamic signals are introduced to handle uncertain dynamic disturbances in the system, and a novel finite-time performance function is used to constrain tracking errors. In order to estimate unmeasurable states, a state observer is designed. In addition, fuzzy logic systems are introduced to approach unknown nonlinear functions in the system, greatly reducing computational complexity. Then, the event-triggered scheme is improved, which can switch between fixed threshold strategy and relative threshold strategy. On this basis, a fuzzy adaptive event-triggered controller is designed, which can guarantee that all signals of the control system are semi-globally consistent and ultimately bounded, without Zeno behavior occurring. Finally, the effectiveness of the proposed method was proven and validated. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an adaptive output feedback dynamic surface control (DSC) strategy is proposed for strict‐feedback stochastic nonlinear systems with input quantization, prescribed performance and dynamic uncertainties. A new quantizer is used to process the input signal, which can avoid the chattering of the quantization signal and keep the upper bound of the quantization error constant. Radial basis functions are used to approximate unknown smooth functions, unmodeled dynamics are processed by dynamic signals, and unmeasurable states are estimated by high gain observer. Hyperbolic tangent functions are employed to handle prescribed performance. The second order command filter is used to replace the first order filter used in general DSC, and the compensation term is added in each step of DSC. By the Lyapunov stability analysis, all signals in the controlled system are semi‐globally uniformly ultimately bounded (SGUUB) in probability. Two examples further prove that the control scheme designed in this paper is reasonable and effective. [ABSTRACT FROM AUTHOR]

This paper presents a new prescribed performance-based finite-time adaptive tracking control scheme for a class of pure-feedback nonlinear systems with input quantization and dynamical uncertainties. To process the input signal, a new quantizer combining the advantages of a hysteresis quantizer and uniform quantizer has been used. Radial basis function neural networks have been utilized to approximate unknown nonlinear smooth functions. An auxiliary system has been employed to estimate unmodeled dynamics by producing a dynamic signal. By introducing a hyperbolic tangent function and performance function, the tracking error was made to fall within the prescribed time-varying constraints. Using modified dynamic surface control (DSC) technology and a finite-time control method, a novel finite-time controller has been designed, and the singularity problem of differentiating each virtual control scheme in the existing finite-time control scheme has been removed. Theoretical analysis shows that all signals in the closed-loop system are semi-globally practically finite-time stable, and that the tracking error converges to a prescribed time-varying region. Simulation results for two numerical examples have been provided to illustrate the validity of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper presents a new prescribed performance-based finite-time adaptive tracking control scheme for a class of pure-feedback nonlinear systems with input quantization and dynamical uncertainties. To process the input signal, a new quantizer combining the advantages of a hysteresis quantizer and uniform quantizer has been used. Radial basis function neural networks have been utilized to approximate unknown nonlinear smooth functions. An auxiliary system has been employed to estimate unmodeled dynamics by producing a dynamic signal. By introducing a hyperbolic tangent function and performance function, the tracking error was made to fall within the prescribed time-varying constraints. Using modified dynamic surface control (DSC) technology and a finite-time control method, a novel finite-time controller has been designed, and the singularity problem of differentiating each virtual control scheme in the existing finite-time control scheme has been removed. Theoretical analysis shows that all signals in the closed-loop system are semi-globally practically finite-time stable, and that the tracking error converges to a prescribed time-varying region. Simulation results for two numerical examples have been provided to illustrate the validity of the proposed control method.

This study focuses on the adaptive prescribed-time neural control for a class of high-order switched systems with nonlinear parameterization in presence of unmodeled dynamics and quantized input. Different from the existing results on finite-time control on basis of adding a power integrator technique, the controller construction and stability analysis are simplified, and the tracking error remains within a set range over any prescribed time. Under the frame of backstepping design, a state feedback controller is designed. During the controller design procedure, Radial basis function (RBF) neural networks with minimal learning parameters are employed to identify the unknown compounded nonlinear functions, and the control input is quantized. Based on Lyapunov stability theory, the closed-loop system’s signals are all assured to be semi-globally uniformly bounded (SGUB), and the tracking error is kept inside a prescribed zone at a finite time. Finally, a numerical simulation is provided to demonstrate the viability and efficacy of the control strategy.

A prescribed performance neural network adaptive control scheme based on event-triggered mechanism is presented for a class of strict-feedback nonlinear systems with unmodeled dynamics. First, in order to improve the performance of system, finite-time performance function is introduced. The unknown nonlinear functions are approximated by radial basis function (RBF) neural networks. Then, an adaptive eventtriggered controller based on back-stepping is designed, which guarantees that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Meanwhile, the tracking error can converge to a prescribed range, and the Zeno-behavior can be avoided. Finally, simulation verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The actor‐critic neural optimal control is investigated for the state‐constrained nonlinear systems in the nonstrict feedback form with unmodeled dynamics in this paper. The filtering errors in the traditional dynamic surface control (DSC) are countervailed by the introduced compensation signals. Two design phases together determine the input: the feedforward input design and the near optimal input design. In the feedforward input design, a mapping rule is established to keep all the states in the finite range, and a first‐order adjunctive signal is designed to treat the unmodeled dynamics. In the near optimal input design, the cost function relying on the reconstructed error system is minimized by the near optimal input via adaptive dynamic programming (ADP). In the whole design processing, the unknown nonlinear uncertain parts are fitted by the radial basis function neural networks (RBFNNs). The stability analysis illustrates all the signals are bounded in the controlled system. Two simulation examples are employed to verify the theoretical findings. [ABSTRACT FROM AUTHOR]

DC–DC buck converters have become prominent components for energy optimization in power systems, and how to improve control performances is a challenging issue to be addressed. In this paper, we aim to investigate the harmonic problem of sliding mode (SM) controlled buck converters imposed by the often-ignored unmodeled dynamics of the Hall sensor. The unified mathematical model of the whole system is established by combining the SM controller, the buck converter, and the Hall sensor, where the signal loss in the transmission process of the whole closed-loop control system is considered. Based on the Lyapunov stability theorem, the SM controller is designed to guarantee system stability, as well as to deduce the stable working areas and the tuned controller parameters. Furthermore, we introduce the descriptive function (DF) approach to investigate the influence of the unmodeled dynamics of the Hall sensor on the system harmonics in the frequency domain, which can deduce the relationship between the amplitude-frequency characteristics of the output signal and the Hall sensor. Simulations and experiments validate this paper. [ABSTRACT FROM AUTHOR]

This work investigates the problem of Multi-dimensional Taylor Network (MTN)-based fault-tolerant control (FTC) for single-input and single-output nonlinear systems in non-strict feedback form. A MTN-based FTC method is presented for nonlinear systems with actuator faults and unmodeled dynamics. The actuator faults are contains both the loss of effectiveness factor of the actuator and a time-varying bias signal. MTN is used to approximate the unknown nonlinear functions, while unmodeled dynamics and dynamical disturbances are handled with the help of dynamical signal functions. A systemically backstepping-based fault-tolerant control scheme is proposed based on Lyapunov stability theory and MTN approximation ability. The suggested technique ensures that all closed-loop system signals are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small region around the origin. To demonstrate the effectiveness of the proposed controller design, three examples, including a single-link robot manipulator, are presented. [ABSTRACT FROM AUTHOR]

This paper presents a neuroadaptive tracking control method for a class of pure‐feedback nonlinear systems in the presence of dynamic constraints and unmodeled dynamics simultaneously. By introducing a nonlinear mapping (NM), the tracking control problem for constrained pure‐feedback system is recast into a regulation problem of the converted system without constraints. Such transformation allows the states to be confined within given regions directly, this is in contrast to the commonly used Barrier Lyapunov Function method that relies on the upper bound of the virtual control errors. To handle the unmodeled dynamics in the system, a dynamic compensation signal is introduced. It is shown that in the proposed scheme the neural networks (NN) not only act as a universal approximator to deal with unknown nonlinearity, but also function as a decoupler to cope with the coupling effects between state and the new variable arising from the introduction of the NM and the backstepping design. Simulation results also confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, the issue of neural adaptive decentralized finite-time prescribed performance (FTPP) control is investigated for interconnected nonlinear time-delay systems. First, to bypass the potential singularity difficulties, the hyperbolic tangent function and the radial basis function neural networks are integrated to handle the unknown nonlinear items. Then, an adaptive FTPP control strategy is developed, where an improved fractional-order filter is applied to tackle the tremendous "amount of calculation" and eliminate the filter error simultaneously. Furthermore, by considering the impact of bandwidth limitation, an adaptive self-triggered control law is designed, in which the next trigger instant is determined through the current information. Ultimately, it can be demonstrated that the proposed control scheme not only guarantees that all states of the closed-loop system are semi-globally uniformly ultimately bounded, but also that the system output is confined to a small area in finite time. Two simulation examples are carried out to verify the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel controller design method for stochastic nonlinear systems with unmodeled dynamics, uncertain parameters, and unknown covariance noise. In order to deal with these uncertainties, a new event‐based small gain controller is designed. The event‐triggered scheme can reduce the computational burden caused by disturbance and noise. By combining the technique of changing supply rate with small gain condition, a stochastic input‐to‐state practically stability Lyapunov function is obtained for the subsystem. Simultaneously, the changing supply function is used to treat with unmodeled dynamics. Based on backstepping process and adaptive control approach, the influence of unknown covariance noise is overcome. It is proved that the designed state‐feedback controller can ensure the overall system is stochastic input‐to‐state practically stability. [ABSTRACT FROM AUTHOR]

The finite‐time command filter tracking control for a class of nonstrictly feedback nonlinear systems with unmodeled dynamics and full‐state constraints is investigated in this paper. The hyperbolic tangent function is used as a nonlinear mapping technique to solve the obstacle of the full‐state constraints. A new adaptive finite time control method is proposed through command filtering reverse engineering, and the shortcomings of the dynamic surface control (DSC) method are overcome by the error compensation mechanism. Dynamic signal is designed to handle dynamical uncertain terms. Normalization signal is designed to handle input unmodeled dynamics. Unknown nonlinear functions are approximated by radial basis function neural networks. Based on the Lyapunov stability theory, it is proved that all signals in the closed‐loop system are semi‐globally consistent and finally bounded and the output tracking error converges in finite time. Two numerical examples are utilized to verify the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

Summary: In this article, a finite‐time adaptive quantized dynamic surface control problem is solved for stochastic partially nonaffine nonstrict‐feedback constrained nonlinear systems with multiple unmodeled dynamics. Firstly, a variable energy function is built to handle the stochastic state unmodeled dynamics under nonstrict‐feedback structure, and a normalized signal is employed to process the input unmodeled dynamics. Furthermore, a specific transformation technique is introduced to keep all states in a predefined asymmetric dynamic constrained region. Compared with the existing results, it not only avoids the circular argument, and also relaxes the condition of the constraint function. Then, the nonlinear function is approximated with the linearly parameterized neural networks. Subsequently, in order to make the tracking error reach a steady state in the finite time, with the help of the properties of the Gaussian function and dynamic surface control technique, a novel finite‐time adaptive quantized controller is designed to ensure that all error signals are semi‐globally practical finite‐time stable and all states obey stochastic probabilistic constraints. Numerical simulation examples are provided to verify the theoretical results obtained. [ABSTRACT FROM AUTHOR]

In this study, a double-loop tracking control strategy is investigated to realize trajectory tracking control for a wheeled mobile robot (WMR) with unmodeled dynamics. More specifically, two nonlinear ESOs are designed to estimate disturbances from external disturbances and unmodeled dynamics. Combining with integral sliding mode control and backstepping control, a double-loop tracking controller is designed to enhance tracking accuracy for the WMR along the right angle roads. Based on Lyapunov methods, convergence analysis is given for both the nonlinear ESOs and the double-loop tracking controller. Validity of the double-loop tracking control strategy is demonstrated by experimental results on the WMR along a right angle road. • A double-loop tracking controller is proposed to improve tracking precision on trajectory tracking of the right angle roads for the WMR. • Sufficient conditions of adjustable parameters are given for nonlinear ESOs to estimate internal disturbances from unmodeled dynamics. • A sliding control law with nonlinear functions instead of sign functions is designed to weaken the chattering of sliding mode control system. [ABSTRACT FROM AUTHOR]

This paper focuses on the control problem of time‐delay nonlinear high‐order fully actuated (HOFA) systems with unmodeled dynamics and unknown dead‐zone input. The primary objective of this paper is to design an adaptive controller by using the HOFA systems approach. To do so, we need to tackle some technical obstacles. Firstly, in order to deal with unmodeled dynamics in the HOFA systems, the technique of changing the supply rate is combined with the HOFA systems approach. Secondly, an adaptive dead‐zone inverse is constructed to compensate for the influence of the unknown asymmetrical dead‐zone input nonlinearity. Then, the controller is designed by the HOFA systems approach, and it is proved that all states of the systems converge to a bounded region based on the Lyapunov‐Krasovskii functions. Finally, the simulation results demonstrate the effectiveness of the designed adaptive controller. [ABSTRACT FROM AUTHOR]

This article concentrates on the event‐triggered adaptive tracking containment control problem for a class of nonlinear multi‐agent systems (MASs) with unmodeled dynamics and prescribed performance. In order to deal with the design difficulties which are aroused by unknown nonlinearities and unmodeled dynamics, the properties of Gaussian function and some novel dynamics signals are used in this paper. Meanwhile, a relative threshold‐based event‐triggered mechanism is adopted such that the system communication burden is reduced under the condition of limited communication resources. It's shown the proposed tracking containment control protocol can ensure that the outputs of the followers converge to the convex hull spanned by the multiple leaders's outputs, all signals of the closed‐loop systems are uniformly ultimately bounded and the Zeno behavior can be effectively avoided. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed containment control protocol. [ABSTRACT FROM AUTHOR]

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui 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.)

In this paper, we consider observer-based adaptive fuzzy finite time control scheme for non-strict feedback uncertain nonlinear systems with unmodeled dynamics and input delay. A fuzzy state observer is employed to estimate the unmeasurable states and the unknown nonlinearities are identified by the fuzzy logic systems in each step. The design difficulty caused by the unmodeled dynamics and input delay is tackled by a dynamic signal and a compensation signal, respectively. Based on the proposed compensation signal, the considered input delay can be unknown and time varying. To decrease the computational burden, the dynamic surface control (DSC) scheme is adopted in the design process. In the framework of finite time Lyapunov theory, an effective adaptive fuzzy finite time controller has been obtained by combining the idea of backstepping technology with DSC scheme. The proposed method not only solves the algebraic loop problem, but also realizes the finite time stability performance constraint in the presence of input delay, unmodeled dynamics and unmeasurable states. Finally, the stability analysis shows that all signals of the closed-loop systems are bounded in finite time. Simulation results show the superiority of the devised scheme. [ABSTRACT FROM AUTHOR]

An adaptive fuzzy control strategy is proposed for a single-link flexible-joint robotic manipulator (SFRM) with prescribed performance, in which the unknown nonlinearity is identified by adopting the fuzzy-logic system. By designing a performance function, the transient performance of the control system is guaranteed. To stabilize the SFRM, a dynamic signal is applied to handle the unmodeled dynamics. To cut down the communication load of the channel, the event-triggered control law is developed based on the switching threshold strategy. The Lyapunov stability theory and backstepping technique are applied coordinately to design the control strategy. The semiglobally ultimately uniformly boundedness can be ensured for all signals in the closed-loop system. The designed control method can also guarantee that the tracking error can converge to a small neighborhood of zero within the prescribed performance boundaries. At the end of the article, two illustrative examples are shown to validate the designed event-triggered controller. [ABSTRACT FROM AUTHOR]

Summary: This paper aims to address the adaptive consensus tracking control problem for distributed nonlinear multi‐agent systems with unmodeled dynamics. It should be emphasized that each considered follower is modeled as a nonlinear non‐strict feedback system in which the control gains are unknown functions rather than constants. By applying an inherent property of radial basis function (RBF) neural networks (NNs) and the introduced dynamics signals, the design difficulties aroused from unknown nonlinearities and unmodeled dynamics are overcome such that the control purpose can be achieved. Then, based on adaptive backstepping methods, a new consensus tracking control protocol is proposed. It is shown that the closed‐loop systems are stable and all the outputs of followers ultimately track the reference signal, that is, the output of the leader, synchronously. Finally, the effectiveness of the proposed control protocol is illustrated through the simulation results. [ABSTRACT FROM AUTHOR]

This article presents a new stabilizing control scheme for a class of interconnected nonlinear systems subjected to unmodeled dynamics and immeasurable states. Fuzzy logic systems are applied to approximate the unknown functions, and a fuzzy-based state observer is constructed. The interconnection of the overall system is completely compensated via the cyclic-small-gain condition theorem, and the small-gain theorem is introduced to overcome the unmodeled dynamics in each subsystem. Furthermore, assumptions from prior literature are relaxed, and computing burden is reduced through the design of less adaptive laws. This article proves that under the designed control scheme, the closed-loop systems are controlled to be input-to-state practically stable and that all signals are guaranteed to be semiglobally uniformly ultimately bounded. Finally, this article’s simulation section illustrates the effectiveness of the proposed approach through an example derived from a practical system model. [ABSTRACT FROM AUTHOR]

The complex industrial processes exhibiting nonstationary and multivariable with time‐varying dynamics result in low accuracy. Also, stability compensation is difficult to be obtained by a conventional PID controller. Hence, a deep learning‐based data‐driven PID controller is designed for unmodeled dynamics compensation for complex industrial processes. In this research work, a nonlinear PID controller is designed with a deep neural network (DNN) model from unmodeled dynamics of the complex industrial processes. To validate the performance, results from stability compensation and convergence of the model parameters for closed‐loop systems were obtained. When tested on a real‐time twin tank system, it achieved an accurate output flowrate with 97.65% accuracy and 1.89% peak overshoot compared with conventional PID controller. Both simulated and experimental results validate that proposed controller has improved stability and uniform convergence of system variables. The proposed deep learning‐based PID controller was employed on a twin tank control system. This confirms the feasibility and practical application of a real‐time complex process. [ABSTRACT FROM AUTHOR]