Adaptive Finite-time Control Research Articles

Finite-time adaptive dynamic surface synchronization control for dual-motor servo systems with backlash and time-varying uncertainties

The dual-motor driving servo system is continuously developed to satisfy strict safety and reliability requirements. However, several factors may degrade the system’s performance, such as transmission backlash, parameter drift, and motor dynamic characteristic differences. To overcome these factors, this study proposes a finite-time tracking and synchronization control method for dual-motor servo systems that suffer from backlash and time-varying uncertainties. Our solution utilizes an adaptive dynamic surface and cross-coupling control scheme to deal with tracking and synchronization control issues and compensate for the unknown time-varying uncertainties. Through synchronizing the speed and acceleration states, the proposed controller guarantees high control performance and eliminates the force fighting caused by the motor’s dynamic characteristic differences. In addition, finite-time control ensures the tracking error converges to an arbitrarily small neighborhood of zero in finite time. Moreover, the singularity problem in the derivative of the virtual control signal is avoided by introducing a new compensation term. Several simulations prove the proposed controller’s stability and effectiveness.

Robust adaptive finite-time tracking control for Intervention-AUV with input saturation and output constraints using high-order control barrier function

The Intervention-Autonomous Vehicle (I-AUV) as the effective operation equipment in the deep sea is suppressed to the external and physical constraints, which makes it challenging to achieve underwater missions. Aiming at the trajectory tracking control of I-AUV with input saturation and output constraints, a higher-order control barrier function-quadratic program (HoCBF-QP) based control scheme is proposed in this paper considering the uncertainties, disturbance and dynamic interaction. Firstly, a robust adaptive control is presented to track the desired trajectory in a finite time, where a feedback control term, based on the continuous terminal sliding mode (TSM) technique, is designed to improve the tracking performance under the uncertainties, disturbance and dynamic interaction. Secondly, a time-varying HoCBF is further developed to handle the high relative degree time-varying output constraints, then the multiple high-order control barrier functions can bring a family of safety-critical control input to preserve the reachability of AUV posture and joints motion. Thirdly, a convex quadratic program (QP)-based separation control frame is designed to conduct the tracking problem and output satisfaction separately. Due to its globally optimal advantage, the proposed control design provides a flexible frame to track objectives for the constrained I-AUV systems. Comparable simulation results demonstrate the fast convergence, robustness and high tracking accuracy of the proposed HoCBF-QP control for I-AUV systems with constraints.

Robust finite-time adaptive control for high performance voice coil motor-actuated fast steering mirror.

Fast steering mirror (FSM) is an efficient and reliable mechanical device in aerial optical image systems for controlling the beam direction with high precision. With the advantages of compact size, high speed, simple structure, and long linear stroke, voice coil motors are ideal actuators for FSM systems. However, model uncertainty can lead to poor performance or even system divergence, especially in environments with temperature variations, electromagnetic environment changes, etc. This paper proposes a novel finite-time adaptive control (FAC) algorithm for an FSM system to obtain high performance, i.e., positioning accuracy, dynamic performance, and robustness. In addition, the finite-time convergence of the controller is analyzed. In the experiments, the controller is implemented in a DSP-based microprocessor. The step response results show that the proposed algorithm has a shorter setting time, smaller overshoot, and smaller steady-state error compared to classical sliding mode control (SMC). The sinusoidal signal tracking accuracy of FAC + SMC has been improved by 19.8%. In addition, as the model uncertainty increases 10%, the root mean square errors (RMSEs) are 1.73″ and 1.18″ for SMC and FAC + SMC, respectively. With 20% model uncertainty, the RMSEs increase to 2.56″ and 1.85″, respectively. Extensive experiments demonstrate the general effectiveness of the proposed algorithm.

Finite-Time Consensus of Nonlinear Multiagent Systems With Nonidentical Input Delays via Event-Triggered Observer

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

Adaptive finite-time fan-coil outlet wind temperature control for the ASHPAC system

In this paper, an adaptive finite-time fan-coil outlet wind temperature control scheme is proposed for the air-source heat pump air-conditioning system. First, a correction module is introduced to compensate the first-order damp elements in the temperature sensor to capture the temperature in real-time. Then, a simple neural network is employed to approximate the unknown and nonlinear functions of the system. On this basis, an adaptive finite-time neural controller is developed, and the finite-time convergence of temperature regulation error is ensured. The stability of the studied fan-coil control is guaranteed by rigorous Lyapunov proof. Finally, two simulation examples are carried out to verify the effectiveness of the proposed control scheme.

Composite adaptive attitude control for combined spacecraft with inertia uncertainties

This paper examines the attitude control problem of the combined spacecraft subject to inertia uncertainties. A composite adaptive finite-time control scheme with guaranteed parameter convergence is proposed to address this challenging problem, in the absence of persistent excitation. As a stepping stone, the attitude dynamics of the combined spacecraft is first established with explicit consideration of the center-of-mass variation and thruster reconfiguration. Then, a composite adaptive finite-time controller is developed based on a constructive time-varying sliding manifold, and in particular, the concurrent learning technique is used in conjunction with the dynamic regressor extension and mixing procedure to achieve parameter convergence under finite excitation that is strictly weaker than the persistent excitation. Lyapunov stability analysis shows that the derived adaptive controller can simultaneously guarantee finite-time convergence of the attitude tracking and parameter estimation errors; moreover, its robustness against inertia variations and external disturbances is also analyzed. Finally, a set of numerical simulations under ideal and practical scenarios are performed to validate the effectiveness and outperformance of the proposed method.

Event-based decentralized adaptive finite-time tracking control of interconnected nonlinear time-varying systems

Event-based decentralized adaptive finite-time tracking control of interconnected nonlinear time-varying systems

Adaptive finite-time control for switched nonlinear systems subject to multiple objective constraints via multi-dimensional Taylor network approach

The finite-time control of switched nonlinear systems subject to multiple objective constraints is investigated in this article. Firstly, with the aim of dealing with the major challenge brought by multiple objective constraints, the time-varying and asymmetric barrier function is designed, which transforms multiple objective constrained systems into unconstrained systems. Secondly, the dynamic surface control technique is introduced into the backstepping design process, and the error generated in the filtering process is reduced by constructing the error compensation systems. Then, an adaptive finite-time controller based on multi-dimensional Taylor network (MTN) is proposed. The controller proposed in this article can avoid the “singularity” problem and ensure that the objective functions never violate constraints. Finally, the effectiveness of the finite-time control strategy proposed in this article is verified by the aircraft system simulation.

Event-Triggered Adaptive Finite-Time Control for Active Suspension Systems With Prescribed Performance

In this article, we address the event-triggered based adaptive finite-time control problem for active suspension systems (ASSs) over the resource-constrained controller area network (CAN), which is the most extensively employed in-vehicle communication network in automotive systems. The control aim is to develop an event-triggered algorithm to reduce the communication burden from the CAN and meanwhile improve the suspension performances. Specifically, a novel finite-time performance function is presented to guarantee that the tracking error is retained in a small region at any prespecified time. It is shown that under the proposed control framework, ride comfort, suspension space limitation, and handling stability are all ensured. Then, the established event-triggered mechanism based on the relative threshold method can avoid Zeno behavior. Finally, all the signals are bounded for the closed-loop ASSs. The usefulness of the presented adaptive control strategy is demonstrated through the simulation results.

Dynamic Event-Based Adaptive Finite-Time Tracking Control for Nonlinear Stochastic Systems Under State Constraints

This article focuses on the problem of adaptive finite-time tracking control for nonlinear stochastic systems under asymmetric constraints based on dynamic event-triggering control. Different from the existing works, a novel adaptive tracking control algorithm is proposed with asymmetric time-varying constraints and dynamic event-triggering mechanism. First, to constrain the state variable within given time-varying boundaries, a novel predefined-time performance function is constructed. Second, a novel barrier function related to state variable is constructed, by means of which the state variable is directly constrained within the asymmetric time-varying boundaries without the virtual controller. In addition, by establishing a novel dynamic function, we propose a dynamic event-triggering mechanism, and then design controller accordingly, which can reduce computation burdens and save the network resources. By the aid of the Lyapunov stability theory, it is proved that the system tracking error converges to an adjustable bounded set in probability in a finite time and all state variables are successfully constrained into the asymmetric time-varying boundaries. Finally, the effectiveness of the proposed control algorithm is verified by a simulation example.

Adaptive Finite-Time Control of Stochastic Genetic Regulatory Networks with Time-Varying Delays

This article discusses the finite-time stability problem for stochastic genetic regulatory networks (SGRNs) with time-varying delays. By designing suitable adaptive controllers and skillfully choosing appropriate Lyapunov and multi-Lyapunov functions, respectively, the above non-switched and switched SGRNs can achieve finite time stability in probability. Superior to some existing controllers for GRNs, the above adaptive design procedures can reduce the dependence of the system parameters. Finally, two numerical simulation examples illustrate the effectiveness of the theoretical results.

Trajectory tracking of Intervention-Autonomous Underwater Vehicle via a robust adaptive finite-time control

The trajectory tracking of Intervention-Autonomous Underwater Vehicle (I-AUV) under hydrodynamic, uncertainties, disturbances and dynamic interaction is studied in this paper. A robust adaptive finite-time control is proposed to attain the high tracking accuracy and strong robustness. Firstly, to enhance the dynamical performance, a continuous terminal sliding mode surface is designed to guarantee that surface and errors are stable finite-time convergent to origin. Second, a feedback control term is developed to depict the hydrodynamic, uncertainties and disturbance estimation error. Then, an adaptive nested estimation technology is developed to approach the disturbance, in which a virtual torque is tuned by the low-pass filtering, and dual nested adaptive laws are provided to approximate this virtual torque and its time derivative, which can constrain the residual error to a small elliptic domain. The validity of our proposed control is verified by Lyapunov stability theory, and its reliability is proved in the simulation cases.

Smooth, Singularity-Free, Finite-Time Tracking Control for Euler–Lagrange Systems

This paper investigates the problem of constrained finite-time tracking control of Euler–Lagrange systems subject to system uncertainties and external disturbances. Firstly, we introduce a nonsingular, fast, constrained terminal sliding manifold (NFCTSM) that contains a time-varying gain to deal with the output tracking error constraint. Therefore, the desired performance in steady-state and transience such as ultimate-tracking-error bound, maximum overshoot, and convergence speed are provided. Then, based on the proposed NFCTSM, a smooth adaptive finite-time control is designed such that the tracking errors converge to an arbitrary small region around the origin during a finite period of time. Moreover, the square of the upper bound of the lumped uncertainty is estimated by the adaptive law in order not to use the discontinuous signum function. The efficacy and usefulness of the proposed control methodology are demonstrated via simulation results and comparison with relevant works.

Funnel-based adaptive fuzzy finite-time control for non-affine nonlinear systems preceded by unknown actuators

In this paper, an adaptive finite-time funnel control for non-affine strict-feedback nonlinear systems preceded by unknown non-smooth input nonlinearities is proposed. The input nonlinearities include backlash-like hysteresis and dead-zone. Unknown nonlinear functions are handled using fuzzy logic systems (FLS), based on the universal approximation theorem. An improved funnel error surface is utilized to guarantee the steady-state and transient predetermined performances while the differentiability problem in the controller design is averted. Using the Lyapunov approach, all the adaptive laws are extracted. In addition, an adaptive continuous robust term is added to the control input to relax the assumption of knowing the bounds of uncertainties. All the signals in the closed-loop system are shown to be semi-globally practically finite-time bounded with predetermined performance for output tracking error. Finally, comparative numerical and practical examples are provided to authenticate the efficacy and applicability of the proposed scheme.

IBLF-based adaptive finite-time control and modeling for continuous stirred tank reactor with output constraint

This paper studies a finite-time adaptive fuzzy control approach for a continuous stirred tank reactor (CSTR) with percent conversion constraint and uncertainties. This system is seen as a class of non-affine systems, and the system is resolved by the mean value theorem. Integral barrier Lyapunov functions (iBLFs) are used to handle output constraint in the design process of the finite-time adaptive controller. In order to calculate the time derivative of the virtual controller, a finite-time convergent differentiator (FTCD) is proposed, which can avert the issue of “explosion of complexity” in the backstepping design. Based on the finite time stability theory, the proposed approach not only ensures the closed-loop stability, but also guarantees tracking performance in a finite time. Finally, the simulation results on CSTR are showed to reveal the availability of the developed control scheme.

Finite-time command filtered event-triggered adaptive output feedback control for nonlinear systems with unknown dead-zone constraints

This paper addresses the design of neural network observer and adaptive finite-time tracking controller for uncertain nonlinear with event-triggered inputs and unknown dead-zone constraints. To shorten the convergence time and reduce the computational burden, the finite-time command filter backstepping (CFB) technique is modified by improving the error compensation term, and a novel adaptive output feedback event triggering mechanism is developed in this way. It is proven that the newly presented control strategy achieves the goal of finite-time convergence and effectively saves network bandwidth. Meanwhile, all of the closed-loop signals are bounded, and the tracking performance is guaranteed based on the finite-time CFB method avoiding the problem of complexity explosion. Finally, a practical example is included to verify the validity of the proposed theoretical results.

Finite-Time Adaptive Neural Control Scheme for Uncertain High-Order Systems with Input Nonlinearities and Unmodeled Dynamics

This paper proposes a novel finite-time adaptive neural control method for a class of high-order nonlinear systems with high powers in the presence of dead zone input nonlinearities and unmodeled dynamics. By utilizing prescribed performance functions and radial basis function neural networks, the tracking error and state errors are limited within the preassigned range in a finite time, which can be specified by the designer in advance according to the chosen the parameters of the novel prescribed performance functions. Nonlinear transformed error surfaces are designed to counteract the effects of dead zone input nonlinearities in nonlinear high-order systems with unknown system nonlinearities and unmodeled dynamics. Based on the Lyapunov theorem, the tracking errors are proven to converge into a preassigned set in a finite time previously specified by the novel prescribed performance function. Finally, simulation results demonstrate the effectiveness of the proposed method.

Adaptive Neural Network Finite-Time Control of Uncertain Fractional-Order Systems with Unknown Dead-Zone Fault via Command Filter

In this paper, the adaptive finite-time control problem for fractional-order systems with uncertainties and unknown dead-zone fault was studied by combining a fractional-order command filter, radial basis function neural network, and Nussbaum gain function technique. First, the fractional-order command filter-based backstepping control method is applied to avoid the computational complexity problem existing in the conventional recursive procedure, where the fractional-order command filter is introduced to obtain the filter signals and their fractional-order derivatives. Second, the radial basis function neural network is used to handle the uncertain nonlinear functions in the recursive design step. Third, the Nussbaum gain function technique is considered to handle the unknown control gain caused by the unknown dead-zone fault. Moreover, by introducing the compensating signal into the control law design, the virtual control law, adaptive laws, and the adaptive neural network finite-time control law are constructed to ensure that all signals associated with the closed-loop system are bounded in finite time and that the tracking error can converge to a small neighborhood of origin in finite time. Finally, the validity of the proposed control law is confirmed by providing simulation cases.

Finite-Time Adaptive Event-Triggered Control for Robot Manipulators With Output Constraints

This brief investigates the tracking control of robot manipulators with output constraints. A novel finite-time adaptive event-triggered command filtered backstepping scheme is given, in which the command filters are utilized to avoid the direct differentials of virtual control signal and the compensation mechanism is used to eliminate filtering errors. The barrier Lyapunov functions are employed to ensure the outputs are constrained in the predefined region, and the finite-time control with fraction-power signals can help the closed-loop system get better convergence rate and steady-state performance. Moreover, the relative threshold-event-triggered mechanism is used to reduce the resource waste and communication burden effectively. A simulation model of 2-DOF robot manipulator is employed to demonstrate the proposed algorithm.

Adaptive finite‐time event‐triggered control for uncertain nonlinearly parameterized systems with unknown control direction and actuator failures

In this article, the issue of adaptive finite-time event-triggered tracking control is investigated for nonlinearly parameterized systems with unknown faults and unknown control gain. By establishing the proper parameter adaptive laws with the intermediate variable, the problem of system instability caused by the uncertainty of the parametric system and the unknown faults of the actuator can be effectively handled. Simultaneously, the unknown nonlinear term denotes the bias fault is approximated by the fuzzy logic systems (FLSs). Accordingly, an improved event-triggered controller with a varying threshold strategy is developed to rule out the Zeno phenomenon and demonstrate that all the signals in the closed-loop system are semi-globally practically finite-time stable (SGPFS). Finally, a simulation result of the manipulator system is carried out to validate the proposed control scheme.