Adaptive Prescribed Performance Control Research Articles

Ultra-local model based prescribed performance adaptive control of overhead cranes under uncertainties

In this paper, a model-free adaptive control scheme is proposed to address the problem of system performance variations due to inaccurate models of overhead cranes, and external disturbances. First, an adaptive fast integral terminal sliding mode controller with a prespecified performance setting is proposed to accelerate the convergence of the sliding mode surface. In addition, an improved form of performance function is introduced to ensure the high accuracy convergence of state errors, while effectively enhance response performance and state tracking accuracy. Second, a prespecified performance nonlinear disturbance observer is introduced to estimate the lumped dynamics part of the ultra-local model, which can guarantee effective model-free properties. Finally, the stability of the closed-loop control scheme is analyzed by means of Lyapunov stability theory, and the effectiveness and superiority of the proposed method is proved by means of numerical simulation results.

Command Filter‐Based Prescribed Performance Adaptive Control for Fractional Order Non‐Strict System With Unmodeled Dynamics and Input Delay

ABSTRACTThis 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.

Finite-time prescribed performance adaptive control of MEMS micromirror stochastic system with event-triggered mechanism

Finite-time prescribed performance adaptive control of MEMS micromirror stochastic system with event-triggered mechanism

Adaptive prescribed performance control for spacecraft attitude synchronization subject to external disturbances with guaranteed steady‐state performance

AbstractThis paper proposes an adaptive prescribed performance control approach for multiple spacecraft attitude synchronization with the undirected topology. A novel distributed observer is proposed to estimate the states of the leader spacecraft while the estimation value of attitude is still a unit quaternion. Then we design the adaptive prescribed performance control algorithm restrain the external disturbances and parameter uncertainty. By the unit quaternion estimation value of attitude, we make the extension from single spacecraft to multiple spacecraft. Compared to some previous results, the bounds of attitude tracking error and angular velocity error in the steady state can be adjusted by users.

Robust adaptive prescribed performance control for nonlinear systems with arbitrary initial states

The initial state of nonlinear MIMO strict feedback systems is often unpredictable and random, and existing prescribed performance control (PPC) techniques can only guarantee system output constraints within a fixed initial value performance boundary. In this paper, an adaptive PPC method tailored for nonlinear systems was introduced, enabling the system stable tracking regardless of the arbitrary initial states. To guarantee that the tracking error consistently meets the prescribed performance boundary(PPB) envelope, we introduce a nonlinear mapping between the initial value of the prescribed performance functions(PPFs) and the system tracking errors, resulting in an asymmetric time-varying performance boundary. Building upon this foundation, an adaptive PPC scheme is proposed under the backstepping framework, integrated with a nonlinear disturbance observer (NDO). This method ensures that the tracking error remains within the desired PPB, regardless of external disturbances or system initial states. To prevent ‘complexity explosion’, a dynamic surface control (DSC) technology is employed to filter the virtual control signals of each subsystem. Furthermore, the ultimate uniform boundedness of the closed-loop signal has been demonstrated, and a practical flight vehicles roll control case was introduced to validate the efficacy of the proposed method.

Adaptive fuzzy performance control based on a triggering mechanism with decreasing function and internal dynamic variable

The problem of fuzzy adaptive prescribed performance control with dynamic event-triggered input for a class of uncertain nonlinear systems is investigated in this article. A dynamic event-triggered scheme is proposed that integrates an internal dynamic variable and an exponentially decreasing function, effectively reducing unnecessary trigger events and minimizing the system of communication resources. Subsequently, a fuzzy estimator is designed to estimate the unmeasurable states of the system, while fuzzy logic systems are employed to approximate the uncertain nonlinear terms. The proposed scheme ensures semi-globally uniformly ultimately bounded (SGUUB) of the closed-loop system and converges the tracking error to a prescribed performance range based on a barrier Lyapunov function. Finally, a simulation example is given to justify the rationality of the proposed strategy.

Adaptive prescribed performance control of output constrained hybrid-powered barrel elevator with dual-model uncertainty compensation

Adaptive prescribed performance control of output constrained hybrid-powered barrel elevator with dual-model uncertainty compensation

Safe Dual-Layer Nested Adaptive Prescribed Performance Control of Nonlinear Systems With Discontinuous Reference

Safe Dual-Layer Nested Adaptive Prescribed Performance Control of Nonlinear Systems With Discontinuous Reference

Disturbance-Observer-Based Adaptive Prescribed Performance Formation Tracking Control for Multiple Underactuated Surface Vehicles

This study proposes a new disturbance-observer-based adaptive distributed formation control scheme for multiple underactuated surface vehicles (USVs) subject to unknown synthesized disturbances under prescribed performance constraints. A modified sliding mode differentiator (MSMD) is applied as a nonlinear disturbance observer to estimate unknown synthesized disturbances, which contain unknown environmental disturbances and system modelling uncertainties, thus enhancing the robustness of the system. Based on this, we impose the time-varying performance constraints on the position tracking error between the neighboring USVs. A novel differentiable error transformation equation is embedded in the prescribed performance control, and an adaptive prescribed performance controller is constructed by employing the backstepping method to ensure that the position tracking error remains within the prescribed transient and steady performance, and each USV realizes collision-free formation motion. Furthermore, a novel second-order nonlinear differentiator is introduced to extract the derivative information of the virtual control law. Finally, the numerical simulation results verify the effectiveness of the proposed control scheme.

Adaptive Prescribed Performance Control for PEM Fuel Cell Air Supply Systems With Unknown Air Compressor Faults

Adaptive Prescribed Performance Control for PEM Fuel Cell Air Supply Systems With Unknown Air Compressor Faults

Adaptive Multi-Switching-Based Global Tracking Control for Switched Nonlinear Systems With Prescribed Performance

This paper is concerned with the tracking control for a class of switched nonlinear systems subject to prescribed performance. Firstly, a barrier function and a normalized function are introduced to achieve prescribed performance control, which ensures that the tracking error evolves within prescribed boundary. Then, multi-dimensional Taylor network provides a new approach for how to estimate the nonlinearity arising from the backstepping control process. Significantly, the proposed multi-switching-based adaptive controller realizes that all signals in the closed-loop system are globally uniformly ultimately bounded while ensuring asymptotic tracking. Different from most existing network-approximation-based control strategies, the developed method in this paper is not only independent of the initial state, but also can achieve global stability. Finally, it is easy to verify the effectiveness of the proposed control method through two simulations. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This research is motivated by the fact that the control ideas of many practical engineering systems can be provided by making a profound study on switched nonlinear systems. However, most of the existing results focused on semi-global stability of systems. Therefore, this study devotes to develop a novel adaptive prescribed performance control strategy, which can not only ensure global stability of closed-loop system but also realize the asymptotic tracking of the switched nonlinear systems. It is worth noting that the proposed control approach has great significance for many practical systems, such as circuit systems and single-link inverted pendulum systems.

Switching periodic event‐triggered global prescribed performance control of uncertain strict‐feedback systems with sensor faults

AbstractThis study investigates the global adaptive prescribed performance control (PPC) of a class of uncertain strict‐feedback nonlinear systems with sensor faults based on the switching periodic event‐triggering mechanism (SPETM). Due to the existence of sensor faults, the controlled system is first remodeled by utilizing the available variables. To reduce the communication frequency, a novel SPETM is proposed by combining the advantages of the static and the dynamic event‐triggering mechanisms. This mechanism can not only avoid continuously monitoring the event‐triggering condition and avoid the Zeno phenomenon in mechanism, but also reduce the trigger frequency while ensuring the system performance by adjusting the event‐triggering threshold dynamically. Meanwhile, a time‐varying scaling function, whose reciprocal is considered as a prescribed performance function, is designed to achieve the global PPC by combining the nonlinear transformation technique. The adaptive control algorithm design is completed by employing the backstepping methodology, which can guarantee that all closed‐loop signals are bounded and the actual system output signal evolves within the prescribed performance boundary for arbitrary initial values. The effectiveness and the advantages of the proposed control algorithm are illustrated through an application example of the network‐based robotic manipulator system.

Identification and Control of Flexible Joint Robots Based on a Composite-Learning Optimal Bounded Ellipsoid Algorithm and Prescribe Performance Control Technique

This paper presents an indirect adaptive neural network (NN) control algorithm tailored for flexible joint robots (FJRs), aimed at achieving desired transient and steady-state performance. To simplify the controller design process, the original higher-order system is decomposed into two lower-order subsystems using the singular perturbation technique (SPT). NNs are then employed to reconstruct the aggregated uncertainties. An adaptive prescribed performance control (PPC) strategy and a continuous terminal sliding mode control strategy are introduced for the reduced slow subsystem and fast subsystem, respectively, to guarantee a specified convergence speed and steady-state accuracy for the closed-loop system. Additionally, a composite-learning optimal bounded ellipsoid algorithm (OBE)-based identification scheme is proposed to update the NN weights, where the tracking errors of the reduced slow and fast subsystems are integrated into the learning algorithm to enhance the identification and tracking performance. The stability of the closed-loop system is rigorously established using the Lyapunov approach. Simulations demonstrate the effectiveness of the proposed identification and control schemes.

Segmented hybrid event‐triggered control for underactuated autonomous underwater vehicles with an asymmetrical prescribed performance constraint

AbstractIn this technical article, the trajectory‐tracking problem of event‐based adaptive prescribed performance control for underactuated autonomous underwater vehicles (AUVs) is considered. The primary innovation of this article is the proposal of a segmented event‐triggered mechanism (SETM) that incorporates preselected convergence time. This mechanism allows for the regulation of communication frequency in the control channel (controller‐to‐actuator) according to the dynamic characteristics of the AUV system at different stages. In addition to presenting new design scheme based on SETM, an enhanced vision known as segmented hybrid event‐triggered mechanism (SHETM) is introduced. Notably, the minimum and maximum triggering intervals (MITI and MATI) for these two ETMs can be calculated from the corresponding resettable dynamic variables presented in the trigger conditions. Subsequently, by utilizing the proposed asymmetrical prescribed performance control (APPC) strategy, the system's output tracking error behaviors in both transient and steady‐state stages can be qualitatively predetermined through design parameters within the boundary function. Moreover, the minimum learning parameter (MLP) based RBFNN adaptive control law is developed to counteract the effects of model uncertainties and ocean current perturbations. Finally, rigorous theoretical analysis and simulation results confirm the viability of the proposed scheme.

Approximate Optimal Adaptive Prescribed Performance Control for Uncertain Nonlinear Systems With Feature Information

Approximate Optimal Adaptive Prescribed Performance Control for Uncertain Nonlinear Systems With Feature Information

Adaptive prescribed performance based on recursive nonsingular terminal sliding mode control for quad-rotor systems under uncertainty and disturbance: Real-time validation

In this paper, at the aim of the fast trajectory-following control of the Unmanned Aerial Vehicle (UAV) systems subject to uncertainty and disturbance, the adaptive prescribed performance control based recursive nonsingular terminal sliding mode control is suggested. Afterward, for the fast trajectory-following control of the uncertain and perturbed quad-rotor system, a recursive nonsingular terminal sliding mode strategy is recommended. Since the better transient and steady-state response of the sliding mode surface is of the utmost importance in the design of the controller, the prescribed performance control scheme is proposed in which the transformed prescribed form of the previously proposed recursive nonsingular terminal sliding surface is obtained to prove that the recursive nonsingular terminal sliding surface can converge to a preset region. Whereas unknown bounded uncertainty and disturbance always exist in the UAV system's dynamical model which decreases its performance and efficiency, the adaptive mechanism is applied to approximate uncertain bounds of uncertainty and disturbance to advance the implementation of the UAV system. Later, quick reachability of the prescribed form switching surface is demonstrated using Lyapunov concept. Ultimately, simulation results using MATLAB/Simulink over a quad-rotor system and real-time simulation using the Speedgoat Real-Time Target Machine platform are conducted to validate the feasibility and performance of the proposed method.

Adaptive prescribed performance control for wave equations with dynamic boundary and multiple parametric uncertainties

&lt;abstract&gt;&lt;p&gt;In modern engineering, the dynamics of many practical problems can be described by hyperbolic distributed parameter systems. This paper is devoted to the adaptive prescribed performance control for a class of typical uncertain hyperbolic distributed parameter systems, since uncertainties are inevitable in practice. The systems in question simultaneously have unknown in-domain spatially varying damping coefficient and unknown boundary constant damping coefficient. Moreover, dynamic boundary condition is considered in the present paper. These characteristics make the control problem in the paper essentially different from those in the related works. To solve the problem, using adaptive technique based projection operator, backstepping method developed for ODEs and Lyapunov stability theories, a powerful adaptive prescribed performance control scheme is proposed to successfully guarantee that all states of the resulting closed-loop system are bounded, furthermore, the original system state converges to an arbitrary prescribed small neighborhood of the origin. Compared with the existing results, the developed control schemes can not only effectively handle the serious uncertainties, but also overcome the technical difficulties in the infinite-dimensional backstepping control design method caused by the dynamic boundary condition and guarantee prescribed performance.&lt;/p&gt;&lt;/abstract&gt;

Adaptive Fuzzy Prescribed Performance Output-Feedback Cooperative Control for Uncertain Nonlinear Multiagent Systems

The paper considers the prescribed performance output-feedback cooperative control of multi-agent systems with nonparametric uncertainties. Scale and performance functions are proposed to construct a barrier function, based on which the adaptive prescribed performance control scheme is devel- oped. Therein, the fuzzy observers are employed to estimate the unavailable states. The proposed scheme has the following characteristics: 1) the tracking errors are always limited to a specified boundary. 2) the tracking error converges to a specified accuracy within a given time, and the settling time and tracking accuracy are determined only by the user and no longer depend on the initial conditions. 3) the distributed fuzzy controller design uses only the output information from agent and its neighbor. The effectiveness and practicality of the proposed method are demonstrated by two simulation examples.

Adaptive prescribed performance control for state constrained stochastic nonlinear systems with unknown control direction: a novel network-based approach

Adaptive prescribed performance control for state constrained stochastic nonlinear systems with unknown control direction: a novel network-based approach

Event-triggered adaptive prescribed performance control of flexible-joint manipulators with output constraint

PurposeThis paper aims to design an event-triggered adaptive prescribed performance controller for flexible manipulators, with the primary objectives of achieving output performance constraints and addressing communication resource limitations.Design/methodology/approachThe authors propose a novel prescribed performance barrier Lyapunov function (PP-BLF) that considers both output and tracking performance constraints. The PP-BLF ensures that the system's output, transient behavior and steady-state performance, adhere to prescribed constraints. The boundary of the PP-BLF is established by an exponential function that decays over time. Notably, the PP-BLF can be applied seamlessly in unconstrained cases without necessitating controller redesign. Moreover, the controller design incorporates an event-triggered mechanism, effectively reducing the frequency of controller updates and optimizing the utilization of communication resources. Additionally, the authors employ adaptive techniques to estimate the system's unknown parameters and approximate unknown nonlinear functions using radial basis function neural networks (RBFNN). To address the challenge of “complexity explosion”, dynamic surface technology is employed.FindingsNumerical simulations are conducted under five different cases to verify the effectiveness of the proposed controller. The results demonstrate that the controller successfully constrains the output tracking error within the prescribed performance boundary. Moreover, compared with the traditional time-triggered mechanism, the event-triggered mechanism significantly reduces the controller's update frequency, resolving the problem of limited communication resources.Originality/valueThe paper reduces the update frequency of control signals and improves resource utilization through an event-triggered mechanism in the form of relative thresholds. The authors recognize that the event-triggered mechanism may impact the output performance of the system. To address this challenge, the authors propose a prescribed performance Barrier Lyapunov Function (PP-BLF). The PP-BLF is designed to effectively constrain the output performance of the system, ensuring satisfactory control even when the control signal updates are reduced.