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This paper investigates the trajectory tracking control problem of a four-wheel independently driven skid-steering mobile robot (FWID-SSMR) while considering friction resistance, parameter variation and external disturbances. Unlike previous studies that only achieved stable tracking control of FWID-SSMR, this paper accomplishes prescribed steady-state and transient performance. Based on the dynamic model of FWID-SSMR, an integer-order prescribed-time controller (IOPTC) is developed first, which can make the tracking errors converge to a predetermined residual set with a preset convergence rate in a prescribed time. Motivated by it, a fractional-order prescribed-time controller (FOPTC) is developed by exploiting the genetic attenuation properties of fractional calculus (FC) for improving the control performance. The feasibility and effectiveness of the developed controller are verified by Lyapunov theoretical analysis and numerical simulation studies. The simulation results show that both the IOPTC and FOPTC outperform the feedback controller (FBC). Moreover, the influence of the performance function on control performance is also tested, which can serve as a reference for selecting the appropriate performance function to use in future applications.

In this paper, an extended state observer-based adaptive prescribed performance control technique is proposed for a class of nonlinear systems with full-state constraints and uncertainties. An extraordinary feature is that not only the control problem of prescribed performance tracking and full-state constraints are solved simultaneously, but also the parametric uncertainties and disturbances are considered, which will make it difficult to design a stable controller. For this purpose, the extended state observer and adaptive technique are integrated to obtain estimations of disturbances and parameters. Then, based on the combination of prescribed performance and barrier Lyapunov function, a novel backstepping control scheme is developed with feedforward compensation of parameters and disturbances to ensure that the tracking error is kept within a specified prescribed performance bound without violation of full states at all times. Moreover, the boundedness of all signals in the closed-loop system is proved and asymptotic tracking can be realized if the disturbances are time-invariant. Finally, two simulation examples are performed to highlight the efficiency of the proposed approach.

This paper investigates the high-performance attitude control and active vibration suppression problem for flexible spacecraft in the presence of external disturbances. The active vibration control usually depends on additional sensors and actuators, which will highly increase the difficulty of practical application. In order to reduce the implementation complexity, the piezoelectric sensors are not adopted, but instead a modal observer is introduced to estimate the modal information. Based on the observed modal information and the prescribed performance design process, an adaptive attitude controller is developed, which has the capabilities of rejecting disturbances as well as possessing predetermined transient and steady-state control performance. Similarly, an active controller is constructed to deal with the vibrations induced by attitude motions. It can be proved that by constraining the estimations of the modal variables, the actual modal coordinate will also be constrained with expected attenuation characteristics. The stability of the entire closed-loop system is analyzed by the Lyapunov theory. Simulation results in different cases show the effectiveness and performance of the proposed algorithms.

This paper proposes a prescribed performance and tunable prescribed time-based approach for the horizontal path following control of an underactuated autonomous underwater vehicle with the influence of unknown disturbances. Firstly, an improved piecewise prescribed performance function and its corresponding error transformation function considering the sign of initial errors are introduced into line-of-sight guidance to confine the kinematics tracking errors to the prescribed performance within a tunable prescribed time. Subsequently, an adaptive dynamics controller is designed by combining the asymmetric time-varying barrier Lyapunov function and the tunable prescribed-time stability theory. Furthermore, the entire path following controller is demonstrated to be tunably prescribed-time stable and both position tracking errors and heading tracking error are consistently kept within the predefined constraints. Finally, three groups of comparative numerical simulations are presented to demonstrate the validity and outperformance of the designed control scheme and approach.

This paper is devoted to the adaptive finite-time prescribed performance control (FTPPC) for stochastic nonlinear systems with unknown virtual control coefficients (UVCCs), which are functions of system states. To eliminate the condition that the initial value of the performance function (PF) is bigger than the initial tracking error, a novel smooth shifting function, for the first time, is defined and embedded in FTPPC for the tracking error. New control laws are firstly proposed and employed to deal with UVCCs in the controller design, which are different from the Nussbaum gain technology in the existing papers. An adaptive FTPPC strategy is designed so that all of the signals in the closed-loop system are bounded in probability and the tracking error is restrained in a fixed bound after a preset finite time,even that the PF is smaller than the tracking error at the initial time instant.

Flexible-joint manipulators have a lightweight nature, compact structure, and high flexibility, making them widely applicable in industrial manufacturing, biomedical instruments, and aerospace fields. However, the inherent flexibility of single-link flexible-joint manipulators (SLFJMs) poses substantial control challenges. Compared to traditional control algorithms, prescribed performance control (PPC) algorithms provide superior transient response and steady-state performance by defining a prescribed performance function. However, existing PPC algorithms are limited to a specific range of system initial states, which reduces the joint manipulator’s operational workspace and weakens the robustness of the control algorithm. To address this issue, this study proposes a prescribed performance bounded-H∞ fault-tolerant controller for SLFJMs. By designing an improved tangent-type barrier Lyapunov function (BLF), a prescribed performance controller that is independent of the initial state of the SLFJM is developed. An input control function (ICF) is employed to mitigate the impulse response of the control input, ensuring a smooth transition from zero. Furthermore, the improved tangent-type BLF enables the tracking error to rapidly converge to a small neighborhood of zero. Finally, a stabilization control simulation experiment is conducted; the results validate the effectiveness of the proposed prescribed performance bounded-H∞ controller.

In this work, the problems of active vibration suppression and high accuracy attitude control for a flexible satellite with piezoelectric actuators are studied. Firstly, the attitude error dynamic equation is developed. By utilizing a novel hyperbolic cosecant function and the error transformation equation, the attitude errors can be transferred into new prescribed performance state variables. In order to further ensure better convergence of the new variables, a fixed-time sliding mode control is proposed. Subsequently, a novel fully adaptive component synthesis vibration suppression method is presented to realize vibration suppression during the attitude maneuver by utilizing piezoelectric actuators. Stability analysis of the proposed prescribed performance control is given. Finally, abundant numerical simulation results demonstrate the excellent performances of the proposed control scheme.

This paper investigates an adaptive fuzzy controller prescribed performance for uncertain robot systems with actuator saturation. To mitigate the impact of model uncertainty and unknown disturbance, an adaptive fuzzy neural network (AFNN) is designed to approximate model uncertainty, and an adaptive disturbance observer (ADO) based on the AFNN is constructed to approximate the disturbance. For multi-degree-of-freedom robotic systems, an auxiliary system is constructed to alleviate the problem of actuator saturation. Combined with the barrier Lyapunov function, an adaptive prescribed performance controller is designed to realize finite-time tracking control for robotic systems with model uncertainty, external disturbance, and actuator saturation. The superiority and practicability of the designed control method are verified by simulations and experiments.

This paper investigates an approximation-free output feedback prescribed performance control for a half-vehicle active suspension systems to improve driving comfort. Different from prior results that ignore actuator dynamics, this paper factored hydraulic actuators into the controller design. To solve the nonlinearities of the hydraulic active suspension system, an approximation-free, backstepping-free control scheme is developed, where function approximators (e.g. neural networks and fuzzy systems) and the explosion of complexity in backstepping design can be avoided. In this sense, the heavy computational burden can be removed. Moreover, by using a high-gain observer (HGO) and a prescribed performance function, the proposed controller simply requires the system outputs to be available and can achieve prescribed transient and steady-state performance of system outputs. To stop the propagation of peak phenomena caused by the HGO into the suspension system, the proposed controller is designed to saturate properly without affecting system performance attributes. The stability of the suspension system and the performance requirements of the system output are strictly proven. Finally, the comparative simulations are conducted to validate the effectiveness of the proposed method for improving suspension performance.

In this work, we pay attention to investigating fixed-time trajectory tracking with prescribed performance for a multi-degree-of-freedom manipulator system subjected to unknown dynamics and input saturation. The radial basis function neural network (RBFNN) is applied to online compensate for the unknown dynamics of the system. In order to guarantee the transient and steady-state performance of the trajectory tracking control, a prescribed performance function (PPF) is used to transform the tracking error. Based on the transformed error, a fixed-time auxiliary system is proposed to compensate for the input saturation impact. Using the compensation error, a non-singular terminal sliding surface is designed, and the corresponding fixed-time control scheme is also proposed. By Lyapunov theorem, it is proved that the reaching phase of the sliding manifold can be completed in finite time, and the stability of the closed-loop system is analyzed. Experimental results verify the effectiveness of the proposed method.