Practical Finite-time Stability Research Articles

Distributed finite-time adaptive consensus output-feedback control of multi-agent systems under switching topologies

The paper investigates the finite-time adaptive consensus control of nonlinear multi-agent systems (MASs) by output-feedback. During the process of control design, the fuzzy logic system (FLS) is employed to approximate nonlinear function, as well as constructing a fuzzy state observer to estimate unmeasured states. Combining the dynamic surface control (DSC) with adaptive backstepping method conquers the complexity explosion issue. Then, semi-global practical finite-time stability (SGPFS) for closed-loop system is obtained under arbitrary switching communication topologies. All the signals of the overall system are uniformly ultimately bounded (UUB). Finally, the availability of developed control approach will be demonstrated through a simulation.

Practical Finite-Time Command-Filtered Adaptive Backstepping With Its Applications to Quadrotor Hovers.

In this article, a practical finite-time command-filtered adaptive backstepping (PFTCFAB) control method is presented for a class of uncertain nonlinear systems with nonparametric unknown nonlinearities and external disturbances. Unlike PFTCFAB control techniques that use neural networks (NNs) or fuzzy-logic systems (FLSs) to deal with system uncertainties, the proposed method is capable of handling such uncertainties without the need for NNs or FLSs, thus reducing complexity and increasing reliability. In the proposed approach, novel function adaptive laws are designed to directly estimate unknown nonparametric nonlinearities and external disturbances by means of command filter techniques, and a type of practical finite-time command filters is proposed to obtain such laws. Moreover, the PFTCFAB controllers and finite-time command filters are designed with practical finite-time Lyapunov stability, which ensures finite-time stability of system tracking and filter estimation errors. Experimental results with a quadrotor hover system are presented and discussed to demonstrate the advantages and effectiveness of the proposed control strategy.

Fast finite-time control for a class of stochastic low-order nonlinear system with uncertainties

This work explores the challenge of adaptive fast finite-time control for stochastic low-order nonlinear systems with uncertainties. It introduces an improved practical finite-time criterion that aims to achieve accelerated convergence. The criterion aims to extend to a larger range of stochastic nonlinear systems, accommodating state uncertainties and integrating Lyapunov functions with varying powers to ensure rapid convergence in the presence of uncertainties. The entire control design approach is based on the backstepping method. Specifically, fuzzy logic systems are employed in the recursive design of controllers and adaptive laws to address complex uncertain terms. Additionally, the desired magnitude target powers of the Lyapunov function in the criterion are effectively obtained by a clever overlapping selection of parameters. Finally, this study demonstrates the practical finite-time stability of stochastic low-order uncertain nonlinear systems and validates the effectiveness of the proposed approach through simulation example, highlighting its ability to achieve fast convergence.

Finite-time state-constrained adaptive fault-tolerant control for heavy launch vehicles with input quantization

This paper proposes the finite-time state-constrained adaptive fault-tolerant control method for a class of heavy launch vehicles subject to quantized input signals and actuator faults. Firstly, the attitude dynamic model of the heavy launch vehicles suffering from the actuator faults and disturbances is formulated. Secondly, to deal with the completely unknown nonlinear functions, radial basis function neural networks (RBFNNs) are introduced for approximation and compensation. Meanwhile, the barrier Lyapunov function (BLF) is introduced to ensure that all the states in the closed-loop system are bounded and the constraints of the system states are satisfied. As a result, the finite-time state-constrained adaptive fault-tolerant control structure is constructed for the heavy launch vehicles and the semi-globally practical finite-time stability (SGPFS) of the closed-loop control system is proved. Finally, numerical simulation results show the effectiveness and the satisfactory performance of the proposed control algorithm.

Practical Event-Triggered Finite-Time Second-Order Sliding Mode Controller Design.

This article proposes a novel event-triggered second-order sliding mode (SOSM) control algorithm using the small-gain theorems. The developed algorithm has global event property in aspects of the triggering time intervals. First, an SOSM controller is designed related to the sampling error of states, and it is proved that the closed-loop system is finite-time input-to-state stable (FTISS) with the sampling error via utilizing the small-gain theorems. Second, combined with the constructed SOSM controller, a new triggering mechanism is proposed depending on the sampling error by designing the appropriate FTISS gain condition. Third, the practical finite-time stability of the closed-loop system is verified. It is shown that the minimum triggering time interval is always a positive value in the whole state space. Finally, the simulation results demonstrate the effectiveness of the developed control method.

Command filter-based adaptive neural tracking control of nonlinear systems with multiple actuator constraints and disturbances

In this paper, the adaptive practical finite-time tracking control problem for a class of strictly feedback nonlinear systems with multiple actuator constraints is investigated using backstepping techniques and practical finite-time stability theory. The effects of deadband and saturated nonlinear constraints on the controller design of nonlinear systems are addressed by the equivalent transformation method. The problem of complexity explosion due to the derivatives of virtual control signals is solved by using the virtual control signals as inputs to the command filters and using the outputs of the command filters to perform the corresponding control tasks. An adaptive neural network tracking backstepping control strategy based on the command filter technique and the backstepping design algorithm is proposed by approximating an unknown nonlinear function using a neural network. The control strategy ensures the boundedness of all variables in the closed-loop system, and the output tracking error fluctuates in a small region near the origin. Finally, simulations verify the effectiveness of the control strategy designed in this paper.

Event-triggered adaptive control for nonlinear systems using time-receding horizons without initial dependence

This paper investigates the full-state constraint event-triggered adaptive control for a class of uncertain strict-feedback systems. The lack of information on the coupling dynamics of virtual variables in backstepping increases the complexity of feedback design. Given this, the requirements of shaping system performance constraints, eliminating initial dependence, and reducing data transfer costs together give rise to an interesting and challenging problem. Constructing the time-receding horizon (TRH) and stitching it with the quadratic Lyapunov function (QLF) is the key to constrained tracking. Specifying TRHs as a set of smooth bounds with fixed-time convergence and forcing the system to stabilize within the constrained region before the prescribed settling time provide a sufficient condition for practical finite-time stability (PFS). For relaxing the initial dependence, a tuning function is designed to match the performance constraints under arbitrary system initial conditions. A dual-channel event-triggered mechanism (ETM) is developed to automatically adjust the controller and estimator data flow updates with less transmission burden. By combining a specific inequality with backstepping, uncertainties are overcome without the “complexity explosion” in recursion steps. Finally, simulations demonstrate the effectiveness of the proposed method.

Practical finite-time coordinated path following in uncertain time-varying flow field

In this paper, the problem of practical finite-time coordinated path following control of a multi-agent system in an uncertain Euler flow field is studied. The coordinated path following control system is decoupled into a path following sub-system and a coordinated formation sub-system. The uncertain Euler flow field is estimated by constructing an extended state observer. By embedding the extended state observer into the design of the finite-time controllers for both sub-systems, practical finite-time stability of the coordinated path following control system is proved. Simulation results are provided for validating the feasibility and effectiveness of the control method.

Finite-Time Model Reference Adaptive Grasping Control With Fuzzy State Observer for Maglev Grasping Robot System

The Maglev grasping robot system (MGRS) with single axial grasping coils is proposed to achieve the reliable transporting, but the grasped body is always subjected to multiple disturbances derived from the robot motion. Hence, the finite-time model reference adaptive control is presented to guarantee the satisfied transient and robustness performance. First, two degree-of-freedom dynamic model is built with consideration of uneven magnetic flux. Second, the prescribed performance reference model (PPRM) is designed by finite-time control (FTC), and its convergence performance is analyzed by FTC Lyapunov function. Then, the adaptive tracking controller is deduced from the model matching and FTC stability, which makes the MGRS to approximate the PPRM in finite time, and fuzzy approximation state observer is used to online get external disturbances and unknown state. It is proved that the proposed strategy can guarantee the closed system semiglobally practical finite-time stability, and the model approximation error and observe error can converge to the desired neighborhood in finite time. Finally, experimental results show that the MGRS with the proposed strategy has faster transient performance, and stronger robustness than the other three strategies.

Adaptive finite-time output tracker design of uncertain nonlinear systems with sandwich structure and actuator saturation constraint

This paper studies the finite-time output tracking problem for a class of nonlinear sandwich systems with sandwiched dead-zone function and input actuator saturation constraint. To achieve the goal, a systematic adaptive backstepping controller is proposed based on the practical finite-time stability criterion which guarantees the output tracking even if the dead zone and saturation constraints occur, simultaneously. In the control approach to confine the system output to a predefined set, the barrier Lyapunov function is utilised. To cope with the problem of ‘explosion of complexity’, a finite-time command filter is also employed. Furthermore, the values of the unknown parameters are estimated by appropriate adaptive laws. The finite-time stability of the closed-loop system is proved based on the Lyapunov approach which ensures the boundedness of all signals. Finally, the efficiency and applicability of the proposed method are confirmed by providing the simulation results for the hydraulic servo press system.

Practical finite-/fixed-time improved command-filtered backstepping control for nonlinear systems via immersion and invariance

This paper presents a practical finite-/fixed-time adaptive tracking control scheme based on improved command filtered backstepping (ICFBS) and immersion and invariance (I&I) for a class of single-input single-output (SISO) strict-feedback nonlinear systems with uncertainty parameterization. The conventional command-filtered backstepping method (CFBS) is improved by introducing any bounded function such that ICFBS relaxes the boundedness assumption of the control direction function and elevates the tunable space of the design scheme, with almost no increase in computational complexity. The higher-power composite term of a parameter estimation is designed in the parameter update law based on I&I and the inequality constructed by us proves that this form of the adaptive update law can be augmented to suit for not only uniform ultimate bounded (UUB) and practical finite-time stability but also practical fixed-time stability of the whole closed-loop system. The error compensation system is also extended to realize the practical finite-/fixed-time convergence of the error compensation signals. The proposed controller guarantees practical finite-/fixed-time stability of the closed-loop system. Therefore, our work solves two unsolved problems: both practical fixed-time and practical finite-/fixed-time ICFBS and I&I adaptive tracking control. Finally, the proposed control scheme is validated by a practical electromechanical system and a nonlinear system with unbounded control direction functions.

Robust optimal triple event-triggered intermittent control for uncertain input-constrained nonlinear systems

This paper investigates the concept of finite time (FT) optimal event-triggered intermittent (ETI) control for nonlinear systems with input constraints and dynamic uncertainty. Firstly, we address a key challenge faced by existing controllers, wherein the unbounded nature of the signal function renders it impractical to handle the input-constrained FT control problem. To overcome this limitation, we derive a novel lemma of semiglobal practical finite-time stability (SGPFTS) specifically for intermittent control with the additional constant terms. By leveraging the lemma and the Hamilton–Jacobi–Bellman (HJB) equation, we obtain the optimal FT guaranteed cost intermittent control law and establish FT stability criteria for uncertain nonlinear systems. Recognizing that the general HJB theory is ill-equipped to address input-constrained optimal control problems, We introduce the model predictive control (MPC) algorithm, which has been proven to be an effective online optimization method in handling robust optimal control and constrained problems. Moreover, conventional ETI optimal control methods struggle to address intermittent control for nonlinear systems with input constraints and dynamic uncertainty. To address this limitation, we propose a triple ETI Lyapunov-based MPC strategy, which not only enhances intermittent control performance but also resolves input constraints and dynamic uncertainty issues. Finally, we present two straightforward examples of uncertain nonlinear systems to demonstrate the effectiveness of the developed strategy in practice.

Practical Finite-Time Stability of Nonlinear Systems With Delayed Impulsive Control

Practical Finite-Time Stability of Nonlinear Systems With Delayed Impulsive Control

Finite-Time Command Filtered Adaptive Control for Robot Manipulators in Random Vibration Environment

This paper introduces a finite-time tracking control algorithm for robot manipulator systems in a random vibration environment, which addresses the challenges of parameter uncertainty and input saturation. The algorithm combines command filtered adaptive backstepping with neural networks to approximate unknown nonlinear dynamics and avoid the singularity problem of traditional finite-time backstepping methods. An error compensation mechanism based on the fractional power function is also introduced to improve trajectory tracking accuracy, and the algorithm is shown to ensure practical finite-time stability in mean square. Numerical simulations demonstrate that the effectiveness of proposed method.

Finite-Time Observer-Based Formation Tracking With Application to Omnidirectional Robots

The challenging problem of robust formation control pertaining to omni-directional robots with model uncertainty and actuator saturation is comprehensively investigated in this paper. Firstly, an observer-based formation controller is designed to ensure the semi-global uniform ultimate boundedness of the system's formation tracking error. Then both global stability and practical finite-time stability are achieved to ascertain the practicality of the observer design. Apart from restricting the control input amplitude, we also investigate the reverse effect caused by saturated and coupled control input. As such, an adaptive compensator is formulated to attenuate the state oscillation caused by the reverse effect. Both numerical simulations and hardware-in-the-loop experiments are carried out to illustrate and evaluate the effectiveness and potentials of the proposed new techniques for real-world applications.

Fast Finite-Time Neuroadaptive Consensus Control for Nonlinear Nontriangular Structured Multiagent Systems With Uncertainty

This literature is concerned with fast finite-time adaptive consensus control for nonlinear nontriangular structured (NTS) multiagent systems (MASs) with uncertainty. In contrast to the existing finite-time schemes, the consensus control for NTS MASs has two design difficulties, reflected in the nonaffine characteristics of the control input and the algebraic loop from the direct backstepping. Moreover, due to the repeated utilization of necessary inequalities, the traditional adaptive framework is incapable of achieving the desired finite-time asymptotic consensus and only rests on satisfying the practical finite-time stability. To tackle such obstacles, a novel adaptive neural algorithm is discussed via the modified tuning function as well as the projection operator in this work. Under the fast finite-time stabilizer constructed, the error of the NTS MASs converges asymptotically to a preset range within the finite time. The flexibility of the algorithm is demonstrated by the simulation example.

Practical finite-time command filtered backstepping control of MPCVD reactor systems with uncertainties

A practical finite-time command filtered backstepping control method is proposed in this paper for a microwave plasma chemical vapor deposition (MPCVD) reactor system. The MPCVD reactor system is modeled as a coupled nonlinear system with unknown control direction functions and unknown nonlinearities. To address the unknown nonlinearities, novel practical finite-time command filters are proposed to construct the estimations of such nonlinearities. On the other hand, an equivalent augmented system of the reactor system is proposed to address the design challenges that posed by the system unknown control direction functions. Additionally, it can be concluded that the proposed control method ensures practical finite-time stability of the reactor system tracking errors by using the practical finite-time Lyapunov stability criterion. Finally, the effectiveness of the approach is demonstrated through the simulation results.

Finite-Time Fuzzy Adaptive PPC for Nonstrict-Feedback Nonlinear MIMO Systems.

This article addresses the issue of the fuzzy adaptive prescribed performance control (PPC) design for nonstrict feedback multiple input multiple output (MIMO) nonlinear systems in finite time. Unknown nonlinear functions are handled via fuzzy-logic systems (FLSs). By combining the adaptive backstepping control algorithm and the nonlinear filters, a novel dynamic surface control (DSC) method is proposed, which can not only avoid the computational complexity issue but also improve the control performance in contrast to the traditional DSC control methods. Furthermore, to make the tracking errors have the prescribed performance in finite time, a new Lyapunov function is constructed by considering the transform error constraint. Based on the designed Lyapunov functions, it is proved that all the signals of the controlled systems are semiglobal practical finite-time stability (SGPFS). Finally, a simulation example is provided to illustrate the feasibility and validity of the put forward control scheme.

Event-triggered adaptive sliding mode control for steer-by-wire systems with actuator fault

The event-triggered tracking control is studied for the steer-by-wire (SbW) system with actuator fault, inaccurate dynamic model, and external disturbance. First, an adaptive sliding mode control based on event-triggering mechanism is proposed. The lumped nonlinearity, including friction torque and self-aligning torque of SbW systems, is approximated by an adaptive robust radial basis function neural network (RBFNN). Robust terms of sliding mode control attenuate the negative effects of the actuator fault, modeling error, external disturbance, and event-triggering error on control performance. More importantly, the sliding-mode high-frequency switching control appears in the high-order derivative of sliding variable without increasing the input–output relative degree, thereby eliminating chattering. Furthermore, the designed method can achieve the practical finite-time stability. The proposed event-triggering mechanism can strictly exclude Zeno behavior and economize the communication resources. Finally, simulations and experiments show the effectiveness of the proposed algorithm.

Adaptive Fuzzy Finite-Time Fault-Tolerant Consensus Tracking Control for High-Order Multiagent Systems With Directed Graphs.

This article investigates the distributed adaptive fuzzy finite-time fault-tolerant consensus tracking control for a class of unknown nonlinear high-order multiagent systems (MASs) with actuator faults and high powers (ratio of positive odd rational numbers). The fault models include both loss of effectiveness and bias fault. Compared with existing similar results, the MASs considered here are more general and complex, which include the special case when the powers are equal to 1. Besides, the functions in this article are completely unknown and do not need to satisfy any growth conditions. In the backstepping framework, an adaptive fuzzy fault-tolerant consensus tracking controller is designed via adding one power integrator technique and directed graph theory so that the controlled systems are semiglobal practical finite-time stability (SGPFTS). Finally, numerical simulation results further verify the effectiveness of the developed control scheme.