Command Filter Research Articles

Improved Adaptive Fault-Tolerant Control of a Variable Structure Fighter with Multiple Faults Based on an Extended Observer

In this paper, an improved adaptive fault-tolerant control (FTC) scheme is presented based on an extended state observer (ESO) for a highly maneuverable variable structure fighter (VSF) with simultaneous faults of the rudder surface and sensor. The feedback linearization model of the attitude system with faults and external disturbances is introduced. In order to realize the real-time observation of the rudder and sensor faults, a neural network estimator is used to approximate sensor faults, and the ESO is designed to estimate the rudder surface fault and attitude variables. On this basis, the estimated value is used to replace the measured value for state variables to design the adaptive fault-tolerant controller for the inner and outer loop, combined with the second-order command filter to offset the phenomenon of backstepping differential amplification system noise. Finally, the proposed FTC scheme realizes the variable structure self-repairing of multiple positions, multiple types of faults in VSF using fault estimation. The Lyapunov theory and Barbalat’s lemma prove the stability of the designed observer and controller. The effectiveness of the FTC scheme is verified by a simulation experiment.

Backstepping Design Embedded With Time-Varying Command Filters

If embedded with command filter properly, the implementation of backstepping design could be dramatically simplified. In this brief, we introduce a command filter with time-varying gain and integrate it with backstepping design, resulting in a new set of backstepping control algorithms with low complexity even for high-order strict-feedback systems. Furthermore, with the aid of “softening” sign function based compensator, zero-error output tracking is ensured while at the same time maintaining prescribed transient performance. Numerical simulation is carried out to verify the effectiveness and benefits of the proposed method.

Error-Based Gain-Varying Finite-Time Command Filtered Backstepping Control for Nonlinear Systems With Disturbances

In this brief, a novel error-based gain-varying backstepping control strategy is proposed for nonlinear systems with disturbances. The error-based gain-varying functions are constructed to substitute the static gains to enhance the anti-disturbance capability and improve the transient performance. The command filter is applied to avoid the differential explosion problem and the filtering errors are reduced by using gain-varying error compensation mechanism. The error-based gain-varying backstepping strategy ensures that the tracking error converges into a small neighborhood of origin in finite time and all signals in the closed-loop system are bounded in finite time. An example of single-link manipulator is employed to illustrate the effectiveness of the proposed strategy.

Two-layer virtual leader-following: An adaptive cooperative path following control for crowded UUV swarm subjected to constraints

This paper investigates an adaptive cooperative path following of a crowded unmanned underwater vehicle (UUV) swarm in the presence of constraints, including uncertain dynamics, external disturbance and input saturation. To satisfy a realistic crowded UUV swarm, a two-layer virtual leader-following (TLVL) is developed to keep the swarm along a commonly desired path while avoiding collision and preserving connectivity, where the desired path is the first-layer virtual leader and the center of mass (COM) for entire swarm is the second-layer virtual leader, and all UUV are followers whose interactions are only affected by partial information from their neighbors. For keeping UUVs connected in a group, the COM is estimated by using a consensus algorithm. Unlike the conventional line-of-sight (LOS) without considering unknown and time-varying sideslip angle, a COM-LOS guidance with finite-time sideslip-angle observer is designed to improve the convergence of cross-track error between the first-layer and second-layer virtual leaders, while three bounded potential fields are incorporated to guarantee without any collisions and loss communicating-links. To deal with uncertain dynamics and external disturbance, a robust adaptive tracking controller is proposed incorporating the disturbance observer, neural network, auxiliary scheme and command filter into TLVL pattern designed to track an integrated desired surge velocity and heading, where an auxiliary scheme is introduced into a switching manner to compensate effects of input saturation. The results about system stability are given by Lyapunov analysis. It has been demonstrated that the proposed scheme can drive the UUV swarm to follow a desired path and all error signals in the closed-loop system can converge to a small neighborhood around origin. The numerical simulation studies are finally given to illustrate the effectiveness of the proposed approach.

Decentralized Event-Triggered Adaptive Fuzzy Control for Nonlinear Switched Large-Scale Systems With Input Delay Via Command-Filtered Backstepping

This article presents a decentralized fuzzy adaptive event-triggered command-filtered control scheme for switched large-scale nonlinear systems with input delay. Fuzzy logic systems are employed to approximate uncertain nonlinearities and a novel observer is constructed to estimate unmeasured states. The “explosion of complexity” defect inherent in the backstepping approach is overcome by employing command filter technology. An auxiliary system is designed to compensate for the effect of input delay, and a novel event-triggered decentralized controller is derived based on the common Lyapunov function method. This article shows that the presented strategy guarantees that all closed-loop variables are semiglobally uniformly ultimately bounded. Finally, simulation results are shown to further confirm the presented strategy’s validity.

Distributed Fuzzy Adaptive Output-Feedback Control of Unknown Nonlinear Multiagent Systems in Strict-Feedback Form.

This article is concerned with the cooperative tracking control problem for heterogeneous multiagent systems in a leader-following form under a directed graph. The dynamics of each following agent is unknown, obeying a strict-feedback form. With the help of fuzzy-logic systems, input filters, and constraint-handling schemes, a fully distributed output-feedback control algorithm is proposed to achieve output synchronization with prescribed performance and guarantee boundedness of signals in the closed-loop systems. In addition, the algorithm exhibits a simplicity control attribute in the sense that: 1) the control design utilizes only relative output measurements, and no extra information needs to be transmitted via the network and 2) the issue of explosion of complexity is addressed, without employing command filters or dynamic surface control techniques. Finally, the simulation results clarify and verify the established theoretical findings.

Command Filter Adaptive Asymptotic Tracking of Uncertain Nonlinear Systems With Time-Varying Parameters and Disturbances

This article is devoted to the adaptive asymptotic tracking for a class of uncertain nonlinear systems. The presence of unknown time-varying parameters and uncertain disturbances makes the systems in question essentially different from those in the related works. By skillfully combining adaptive technique and command filter-based backstepping, a novel command filter adaptive tracking controller is successfully designed to achieve asymptotic tracking. The typical feature of the proposed controller lies in the introduction of a smooth function with positive integrable time-varying function, which makes the controller powerful enough to compensate the unknown time-varying parameters and uncertain disturbances. Remarkably, a novel Lyapunov function by incorporating the lower bounds of control gains is used to prove the stability of the closed-loop system. Compared with some existing command filter-based backstepping, the conditions on the virtual control coefficients and disturbances are relaxed. Finally, the effectiveness of the proposed method is shown by a simulation example.

Adaptive neural output feedback finite‐time command filtered backstepping control for nonlinear systems with full‐state constraints

AbstractIn this paper, an adaptive neural finite‐time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high‐order nonlinear systems with full‐state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite‐time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite‐time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.

Intelligent Command Filter Design for Strict Feedback Unmodeled Dynamic MIMO Systems With Applications to Energy Systems

This study presents a command filtered control scheme for multi-input multi-output (MIMO) strict feedback nonlinear unmodeled dynamical systems with its applications to power systems. To deal with dynamic uncertainties, a dynamic signal is introduced, together with radial basis function neural networks (RBFNNs) to overcome the influences of the dynamic uncertainties. Command filters (CFs) are used to prevent the explosion of complexity, where the compensating signals can eliminate the effect of filter errors. Compared with single-input single-output strict feedback nonlinear systems, the method proposed in this study has more suitability. In the end, the simulation experiments are carried out by applying the developed algorithm to power systems, where the simulation results verify the efficacy of the approach proposed.

Adaptive smooth disturbance observer-based fast finite-time attitude tracking control of a small unmanned helicopter

In this paper, a novel control scheme, which includes an improved fast finite-time adaptive backstepping controller based on a newly designed adaptive smooth disturbance observer, is presented for the attitude tracking of a small unmanned helicopter system subject to lumped disturbances. First, an adaptive smooth disturbance observer (ASDO) is proposed to approximate the lumped disturbances, which owns the characteristics of smooth output, fast finite-time convergence, and adaptability to disturbances. Then, a finite-time backstepping control protocol is constructed to achieve the attitude tracking objective. By virtue of our newly proposed inequality, a singularity-free virtual control law is designed. To tackle the “explosion of complexity” problem, a fast finite-time command filter (FFTCF) is utilized to estimate the virtual control signals and their derivatives. In addition, an auxiliary dynamic system is introduced to attenuate the impact of the errors caused by FFTCF estimation. Moreover, an adaptive law with σ-modification term is developed to compensate the ASDO approximation error. Theoretical analysis proves that all signals of the closed-loop system are fast finite-time bounded, while the attitude tracking errors fast converge to a small region of the origin in finite time. Finally, two contrastive numerical simulations are carried out to validate the effectiveness and superiority of the designed control scheme.

A Finite-Time Trajectory-Tracking Method for State-Constrained Flexible Manipulators Based on Improved Back-Stepping Control

In order to solve the trajectory-tracking-control problem of the state-constrained flexible manipulator systems, a finite-time back-stepping control method based on command filtering is presented in this paper. Considering that the virtual signal requires integration in each step, which will lead to high computational complexity in the traditional back-stepping, the finite-time command filter is used to filter the virtual signal and to obtain the intermediate signal in finite time, to thus reduce the computational complexity. The compensation mechanism is used to eliminate the error generated by the command filter. Furthermore, the adaptive estimation method is introduced to approach the uncertainty of the state-constrained flexible manipulator system. Then, the Lyapunov function is used to prove that the tracking error of the system can be stabilized in a sufficiently small origin neighborhood within a finite time. The simulation of a single rod flexible manipulator system demonstrates the effect of the proposed approach.

Adaptive fault-tolerant attitude control for hypersonic reentry vehicle subject to complex uncertainties

In this paper, a novel fast attitude adaptive fault-tolerant control (FTC) scheme based on adaptive neural network and command filter is presented for the hypersonic reentry vehicles (HRV) with complex uncertainties which contain parameter uncertainties, un-modeled dynamics, actuator faults, and external disturbances. To improve the performance of closed-loop FTC, command filter and neural network are introduced to reconstruct system nonlinearities that are related to complex uncertainties. Compared with the FTC scheme with only neural network, the FTC scheme with command filter and neural network has fewer controller design parameters so that the computational complexity is decreased and the control efficiency is improved, which is of great significance for HRV. Then, the adaptive backstepping fault-tolerant controller based on command filter and neural network is designed, which can solve the complexity explosion problem in the standard backstepping control and the small uncertainty problem in the backstepping control only containing command filter. Moreover, to improve the approximation accuracy of the neural network-based universal approximator, an adaptive update law of neural network weights is designed by using the convex optimization technique. It is proved that the presented FTC scheme can ensure that the closed-loop control system is stable and the tracking errors are convergent. Finally, simulation results are carried out to verify the superiority and effectiveness of the presented FTC scheme.

Adaptive command-filtered finite-time control of non-strict feedback stochastic non-linear systems

In this paper, an adaptive neural network (NN) finite-time command filter controller for a class of non-strict feedback stochastic non-linear systems has been investigated. Using error compensation signals and NNs, a command filter controller is presented which guarantees that all the signals of the closed-loop system are practical finite-time stable and the output signal tracks the given reference signal under the bounded error. In the design procedure, NNs are employed to approximate unknown non-linear functions, which contain all the state variables of the whole system, in order to avoid excessive and burdensome computations and ensure that the backstepping method works normally for non-strict feedback systems. Meanwhile, the “explosion of complexity” problem caused by the backstepping method is avoided using the command filter approach. The control scheme not only resolves the “explosion of complexity” problem but also eliminates the filtering error in finite-time. Finally, the simulation results are given to prove the effectiveness of the proposed control method.

Decentralized adaptive output feedback dynamic surface control for stochastic nonstrict‐feedback interconnected nonlinear systems with actuator failures and input quantization via command filter

AbstractIn this article, a new decentralized adaptive neural output feedback control scheme based on first‐order command filter is proposed for stochastic nonstrict‐feedback interconnected systems with prescribed performance, input quantization, actuator failures and unmodeled dynamics. The unknown smooth functions are eliminated by using the radial basis function neural networks. The immeasurable states in the system are estimated using the decentralized K‐filters, unmodeled dynamics is processed using dynamic signal, and the hyperbolic tangent function is applied to the construction of the prescribed performance function. The hysteretic quantizer and actuator failure are denoted in the form of linearization, and a smoothing function is introduced to compensate for the effects of quantization and bounded stuck faults. Based on the dynamic surface control (DSC) method and using the properties of Gaussian function to deal with stochastic nonstrict‐feedback interconnected systems, the first‐order command filter is used to replace the first‐order filter in the traditional DSC to eliminate the influence of filtering error on the systems, and an error compensation signal is introduced at the recursive each step of the design to construct a new error dynamic surface, which simplifies the derivation process and the design of the controller. Finally, the Lyapunov method is used to prove that all the signals in the whole controlled system are semiglobally uniformly ultimately bounded in probability and the tracking error is within the time‐varying constraint. The effectiveness of the proposed decentralized adaptive control method is verified by a numerical simulation and an example simulation.

Observer-Based Adaptive Fuzzy Output Feedback Control of Fractional-Order Chaotic Systems With Input Quantization

This study investigates the adaptive fuzzy output feedback control of strict-feedback fractional-order chaotic systems with unmeasurable states and quantized input. First, the functional uncertainties are approximated by fuzzy logic systems (FLSs). Second, combining the FLS and the system output signal, an observer is constructed to estimate the unmeasurable states. A command filter is defined to cope with the “explosion of complexity” problem resulting from the repeated derivatives of virtual control inputs in each backstepping step. To compensate for quantization errors, a hyperbolic tangent function is introduced to transform the control signal, which can not only guarantee that the tracking error converges to an arbitrarily small region near the origin but also reduce the chattering phenomenon of the control input. In addition, the stability analysis is carried out relying on the fractional Lyapunov stability criterion such that all the signals keep bounded. Finally, a numerical simulation example is put forward to verify the effectiveness of our method.

Adaptive command filtered fixed-time control of nonlinear systems with input quantization

In this paper, the fixed-time adaptive tracking control issues of the nonlinear systems with input quantization are considered. By designing a common adaptive parameter, the over-parameterization problem of the most existing results is resolved. Based on command filter and backstepping technique, the fixed-time adaptive tracking control can be first realized. In addition, we adopt the Nussbaum-type function to deal with the quantized input. The fuzzy logic systems are utilized to approximate the unknown nonlinearlities. Moreover, it can be concluded that the proposed method can guarantee the stability of the controlled system and the boundedness of the whole signals. At last, simulation examples are employed to verify the availability of the presented scheme.

Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

AbstractIn this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to estimate unknown nonlinear functions. Then, an event‐triggering mechanism based on the tracking error is introduced to avoid the over‐consumption of communication resources. Furthermore, command filters are employed to solve the problem of complexity explosion that exists in conventional backstepping control design, and the error compensation signals are designed to reduce the errors caused by the filters. Considering that the unknown control gain and input saturation exist in many practical applications, a Nussbaum‐type function is thus introduced into the controller design to address these challenging issues. Finally, stability of the closed‐loop system is strictly proven under a standard Lyapunov stability analysis framework. The effectiveness of the proposed control scheme is illustrated by a simulation example.

Disturbance-observer-based adaptive command filtered control for uncertain nonlinear systems

This article proposes an asymptotic adaptive command filtered control approach for uncertain nonlinear systems with parametric uncertainties, mismatched and matched disturbances. To accomplish the task, a disturbance observer (DO) only with one tuning parameter is firstly used to attain the disturbance compensation. The parameter uncertainties can be addressed via composite updated laws. Then, by judiciously combining DO, adaptive control and command filter technique, a novel command filtered controller with adaptive-gain auxiliary systems is developed to attain asymptotic tracking and shun “explosion of complexity”. The system stability is proved by utilizing the Lyapunov function. Extensive experimental results uncover the preponderance of the exhibited strategy.

Adaptive Finite-Time Neural Constrained Control for Nonlinear Active Suspension Systems Based on the Command Filter

This article focuses on the adaptive finite-time command filter control problem for nonlinear quarter active suspension systems with actuator failure. In the previous designs of active suspension systems, a second-order system for the vehicle body movement is employed to achieve the controller design and a zero-dynamic analysis is needed, thus the designed controller only contains a part of system variables and there is a difficulty in selecting a proper Lyapunov function in the zero-dynamic analysis. In order to overcome the aforementioned problems, a novel active suspension system is shown based on the Butterworth low-pass filter. The neural networks (NNs) are used to identify the unknown functions of active suspension systems. Meanwhile, the command filter is proposed to handle the “explosion of complexity” problem caused by the adaptive backstepping technique. Then, the adaptive practical finite-time control scheme combined with the command filter and neural identify technique is constructed in a unified framework, which can handle the “singularity” problem. Through the practical finite-time stability analysis, it is easily to obtain that all signals of closed-loop systems are bounded in a finite time. Finally, simulation results for the nonlinear quarter active suspension systems are provided to verify the effectiveness of the NN control scheme.

Adaptive fuzzy finite-time backstepping control of fractional-order nonlinear systems with actuator faults via command-filtering and sliding mode technique

In the paper, a class of unknown fractional-order nonlinear systems suffering from actuator faults are investigated. Meanwhile, an adaptive finite-time sliding mode control (SMC) approach based on approximation principle of fuzzy logic system (FLS) and backstepping layout is proposed. It is well known that the standard backstepping control has inherent computational complexity. Therefore, a type of fractional-order command filter (CF) is introduced to overcome such a shortcoming, that is, by means of fractional-order CF, the virtual input signal and its fractional derivative can be estimated properly as anticipated. Fractional-order sliding mode surfaces are constructed to diminish the filtering errors such that more better performance is guaranteed. Besides, compared to the conventional backstepping control, the CF-based fuzzy backstepping SMC approach presented in the paper not only shows the superior robustness, but also facilitates to accomplish the desired tracking control objective in finite time. The finite-time stability analysis is established on the basis of fractional-order Lyapunov method. Finally, the effectiveness of the proposed methodology is identified by numerical simulations.