Maximum Convergence Time Research Articles

Intelligent Control System for the Hard X-Ray Nanoprobe Beamline Beam Optimization Based on Automatic Evolution Algorithm and Expert System

A synchrotron radiation beamline automatic optimization system has been used in the Shanghai Synchrotron Radiation Facility, improving the optimization efficiency, but it does not store and use the beamline adjusting experience, and cannot quickly optimize and store the experienced improvement. The expert system combined with an automatic evolutionary algorithm is used for intelligent beamline optimization; the algorithm initialization is optimized by invoking database experience, the convergence is quickly completed near the optimal solution, and the system’s learning is improved by storing experience results. The software was designed on the EPICS (Version 3.15) platform, which was used to implement the algorithm in Python language, the expert database was developed with MongoDB tool (Version 4.0.27), and the upper application interface was designed with CSS software (Phoebus Version 4.7.2). The system was successfully tested on the BL13U hard X-ray nanoprobe beamline of Shanghai Synchrotron Radiation Facility. The results show that the maximum convergence time of a single objective with four-axis degrees of freedom is about 2 min, and the speed is increased by 15 times. The solution set obtained by using multi-objective two and four-axis degrees of freedom is better overall. The system can effectively improve the optimization efficiency and effect, and its universality can be extended to other synchrotron radiation devices and beamlines to promote the development of intelligent beamline modulation technology.

Design of fixed-time sliding mode control using variable exponents

This paper proposes the design of a robust fixed-time sliding mode controller for a second-order nonlinear system with matched and unmatched uncertainties. First, a fixed-time stable scalar dynamics with a variable exponent is introduced. We establish the global fixed-time stability results for the proposed dynamics using Lyapunov analysis. The maximum convergence time has been derived in terms of the design parameters, which is independent of the initial conditions. These results are then extended to solve the robust fixed-time stabilization for uncertain nonlinear second-order system. Specifically, we proceed with the sliding mode control design, where the sliding surface and the control law are motivated by the proposed scalar dynamics. Moreover, the proposed design is free from singularity and guarantees fixed-time robust stabilization. Simulation results with comparative analysis has been included. Further, experimental validation on a single link manipulator is provided to demonstrate the performance of the proposed approach.

Event-based appointed-time cooperative formation for linear multiagent systems with actuator saturation

This paper considers the time-varying formation (TVF) tracking issue of a class of multiagent systems (MASs) with input saturation. Meanwhile, the prescribed transient and steady-state performance of MASs is guaranteed. Compared to the existing prescribed performance control (PPC) approaches, the maximum convergence time of the designed algorithm is preassigned. To save network resources, an event-based protocol is introduced to achieve lower information communication frequency, where Zeno behavior is excluded. Furthermore, an improved control strategy is employed where an auxiliary system is designed to improve the performance under actuator saturation. According to Lyapunov stability theory, the expected practical TVF tracking control problem is solved for the MASs. Finally, the effectiveness of the designed schemes is demonstrated by the simulation examples.

Strong tracking adaptive window multi‐innovation cubature Kalman filter algorithm for lithium‐ion battery state of energy estimation

SummaryAccurate estimation of lithium‐ion battery state of energy (SOE) is an important prerequisite for prolonging battery life and ensuring battery safety. To achieve a high‐precision estimation of the SOE, while a ternary lithium‐ion battery being the specifically targeted in this study, a novel method for SOE estimation is proposed, which combines limited‐memory recursive least squares (LM‐RLS) with strong tracking adaptive window Multi‐innovation cubature Kalman filtering (STW‐MCKF). In the LM‐RLS algorithm, the model parameters at the current time are updated with a limited dataset to solve the data saturation problem and improve the recognition accuracy of the RLS algorithm. The CKF algorithm is optimized by the STW algorithm in the STW‐CKF algorithm to enhance its robustness under strong disturbances. Additionally, a self‐adaptive window multiple innovation strategy is proposed to improve the accuracy of SOE estimation and the stability of the CKF algorithm, while maintaining a balance between computational complexity and SOE estimation accuracy. To validate the effectiveness of the algorithm, experiments are conducted under DST and BBDST conditions. The results show that the STW‐MCKF algorithm has a maximum convergence time of 4 s and an SOE estimation error within 1.04% under DST conditions. Under BBDST conditions, the maximum convergence time is 3 s, and the SOE estimation error is within 2.34%. Furthermore, the STW‐MCKF algorithm demonstrates good stability under the two conditions, indicating the effectiveness of the proposed method for lithium‐ion battery SOE estimation.

An Adaptive Control Methodology for Spacecraft Attitude Tracking Under Model and Environmental Uncertainties

This study investigates the attitude tracking problem for rigid spacecraft under model and environmental uncertainties. Two different control laws are separately developed and combined. First, based on a nominal attitude dynamic model assuming no uncertainty, the first controller is developed that exactly tracks a prespecified attitude reference trajectory with minimized control cost. The designer can generate this reference to prescribe desired performance specifications such as the maximum convergence time and overshoot. Next, an uncertain attitude dynamic system is considered, and the second controller is designed and added so that the controlled system can successfully track the predesigned reference trajectory with a user-specified tolerance even subject to uncertainty whose bounds are unknown. Compared to existing adaptive control schemes, the proposed approach possesses a very simple structure with a small number of control parameters and is not computationally intensive, making it more attractive for practical implementation. The proposed control laws generate smooth control signals and any information about the uncertainty bound is not needed in its design. Simulation results are provided to demonstrate the practical feasibility of the proposed approach, where reorientation/slew maneuvers of a large spacecraft are considered. The effects of limitations on the control torques are also investigated to show the effectiveness of the control methodology developed herein.

Neural Network-Based Tracking Control of Uncertain Robotic Systems: Predefined-Time Nonsingular Terminal Sliding-Mode Approach

This article investigates the predefined time trajectory tracking control of uncertain nonlinear robotic systems. A radial basis function neural network (RBFNN) is used to estimate uncertainties in the robotic system dynamics. To avoid the singularity of terminal sliding-mode control (TSMC), a modified sliding variable is adopted. In order to realize that the tracking errors can converge to a small neighborhood of the origin in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">predefined time</i> , within which the maximum convergence time can be adjusted by explicit parameters in advance, a nonsingular TSMC based on the RBFNN is proposed. Experiments on a ROKAE platform demonstrate the effectiveness and advantage of the proposed control method.

Finite-time control for an Unmanned Surface Vehicle based on adaptive sliding mode strategy

The proper control of the maximum convergence time toward a desired motion or trajectory is highly recommended for Unmanned Surface Vehicles (USV) in the case of autonomous trajectory tracking navigation and obstacle avoidance scenarios. This manuscript proposes to control the convergence time of an adaptive sliding mode controller applied to an autonomous vessel prototype subject to bounded perturbations. In the proposed approach, the maximum settling time is defined as a user input conditioning the controller design and tuning. To guarantee such convergence time to a small neighborhood of the sliding surface, the adaptive law is obtained through a Lyapunov function. The proposed motion and trajectory controller is then implemented on a USV prototype subject to parametric uncertainties and external perturbations. The experiments demonstrate the ability of the controller to constrain the sliding variable to the desired closed-loop dynamics and converge within the desired settling time.

Sliding Mode Control of Underactuated Nonlinear Systems Based on Piecewise Double Power Reaching Law

A sliding mode control (SMC) strategy using novel piecewise double power reaching law (PDPRL) for a class of fourth-order nonlinear underactuated systems (USs) is proposed to realize simultaneous control of the unactuated and the actuated part. A novel PDPRL is designed in the form of a piecewise function to reduce the chattering produced by SMC. The fixed-time convergence characteristic and the existence of maximum convergence time independent of the initial value of the sliding mode surface for the PDPRL are analyzed. Considering the uncertainties of model parameters and external disturbance in the USs, a supertwisting disturbance observer (STDO) is provided to accurately estimate the disturbance in real time, and the estimated value is compensated to the controller. The underactuated inverted pendulum system is taken to verify the effectiveness of the devised strategy, and the simulation results show that the proposed strategy can reduce chattering, suppress the disturbance, and enhance the robustness of the system.

An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation

In this paper, we present a novel adaptation rule to optimize the exoskeleton assistance in rehabilitation tasks. The proposed method adapts the exoskeleton contribution to user impairment severity without any prior knowledge about the user motor capacity. The proposed controller is a combination of an adaptive feedforward controller and a low gain adaptive PD controller. The PD controller guarantees the stability of the human-exoskeleton system during feedforward torque adaptation by utilizing only the human-exoskeleton joint positions as the sensory feedback for assistive torque optimization. In addition to providing a convergence proof, in order to study the performance of our method we applied it to a simplified 2-DOF model of human-arm and a generic 9-DOF model of lower limb to perform walking. In each simulated task, we implemented the impaired human torque to be insufficient for the task completion. Moreover, the scenarios that violate our convergence proof assumptions are considered. The simulation results show a converging behavior for the proposed controller; the maximum convergence time of 20 s is observed. In addition, a stable control performance that optimally supplements the remaining user motor contribution is observed; the joint angle tracking error in steady condition and its improvement compared to the start of adaptation are as follows: shoulder 0.96±2.53° (76%); elbow −0.35±0.81° (33%); hip 0.10±0.86° (38%); knee −0.19±0.67° (25%); and ankle −0.05±0.20° (60%). The presented simulation results verify the robustness of proposed adaptive method in cases that differ from our mathematical assumptions and indicate its potentials to be used in practice.

Finite-Time Intra-Layer and Inter-Layer Quasi-Synchronization of Two-Layer Multi-Weighted Networks

The article pays attention to a two-layer multi-weighted network, and studies finite-time (FT) intra-/inter-layer quasi-synchronization of two-layer multi-weighted networks. Firstly, FT stability and quasi-stability theorems of dynamical system are discussed, and the results show that the maximum convergence time of FT quasi-stability is smaller than that of FT stability for the dynamic system. Secondly, novel sufficient criteria are gained for FT intra-/inter-layer quasi-synchronization of two-layer multi-weighted networks. Thirdly, the relationship among multiple weights number, the topological structure, inner coupling modes, coupling strengthes and across layers are established. In particular, the results show that the smaller multiple weights number do not necessarily lead to faster quasi-synchronization, and the multiple weights number may have a positive feedback effect on FT intra-/inter-layer quasi-synchronization. When the multiple weights number is greater than a certain value, FT intra-layer quasi-synchronization and FT inter-layer quasi-synchronization of networks can be realized simultaneously, and the conditions of taking the minimum convergence time are also given. Finally, numerical examples verify the effectiveness of the proposed method.

Performance analysis of nonlinear activated zeroing neural networks for time-varying matrix pseudoinversion with application

By exploiting two simplified nonlinear activation functions, two zeroing neural network (ZNN) models are designed and studied to efficiently tackle the time-varying matrix pseudoinversion problem. Compared with ZNN activated by previously presented activation functions, these two simplified finite-time ZNN (SFTZNN) models (called SFTZNN1 and SFTZNN2) not only achieve faster finite-time convergence, but also possess better robustness. In addition, the SFTZNN1 and SFTZNN2 models have simpler structure compared with the widely used sign-bi-power activated ZNN model. Theoretical analysis is presented to obtain the maximum convergence time for the SFTZNN models in ideal conditions. Besides, when external perturbations are injected into the proposed SFTZNN models, upper bounds of the steady-state residual error are theoretically calculated. Comparative simulations and one engineering application case validate the feasibility and superiority of the two new SFTZNN models when solving time-varying matrix pseudoinversion.

Chattering-Free Trajectory Tracking Robust Predefined-Time Sliding Mode Control for a Remotely Operated Vehicle

This study investigates a new chattering-free robust predefined-time sliding mode control (CFRPSMC) scheme for the trajectory tracking control problem of a three-degree-of-freedom (3-DOF) remotely operated vehicle (ROV) in the presence of matched uncertainties. The advanced notion of predefined-time stability is used to provide a maximum convergence time as desired that can be set during the control design and independently of the initial conditions. Based on defining a new form of sliding surfaces, a new control law is designed to ease the undesirable chattering phenomenon without damaging the robustness properties and tracking precision. The proposed control scheme can not only solve the predefined-time tracking controller design problem, but also provide the robustness to various uncertainties. The Lyapunov stability theory is used to establish the stability analysis of the closed-loop system in both the reaching phase and the sliding phase. The performance of the proposed CFRPSMC scheme is evaluated for the 3-DOF ROV through two comparative simulation cases using Simulink/MATLAB. The comparative simulation results and analytical comparisons demonstrate the efficacy and superiority of the proposed method compared with other relevant conventional methods.

Observer-based prescribed performance attitude control for flexible spacecraft with actuator saturation

This paper investigates the prescribed performance attitude control problem for flexible spacecraft subject to external disturbances and actuator constraints. By using a new performance function and an error transformation, the attitude control system is transformed into an error system which will be kept bounded to ensure expected dynamic and steady-state responses. Compared with the commonly used performance function, the modified one has an explicit prespecified terminal time which determines the maximum convergence time of the attitude control system. A modal observer and a disturbance observer are designed to deal with the flexible vibration and disturbances, respectively. Furthermore, when considering actuator saturation, an improved control strategy is developed with an auxiliary system utilized to compensate the saturation. The stability of the closed-loop system is analyzed by Lyapunov theory. Simulation results show the effectiveness and performance of the proposed methods.

Disturbance-Observer-Based Prescribed Performance Fault-Tolerant Trajectory Tracking Control for Ocean Bottom Flying Node

The development of marine petroleum exploitation becomes an important part of marine economy development gradually. Ocean bottom flying node (OBFN) is a kind of autonomous underwater vehicle which can explore marine petroleum and natural gas in large-scale deployment. This paper investigates the prescribed performance fault-tolerant trajectory tracking control method for an OBFN in presence of modeling uncertainties, ocean current disturbances, and thruster faults. By using a new performance function and an error transformation scheme, the proposed controller is able to achieve the desired tracking performance and the maximum convergence time is known as a priori. A disturbance observer is designed to deal with the influence of the modeling uncertainties, ocean disturbances, and thruster faults. The stability of the closed-loop OBFN system is proved by using Lyapunov theory. Finally, simulation results under different types of thruster faults illustrate the effectiveness of the proposed disturbance-observer-based prescribed performance fault-tolerant trajectory tracking controller.

Fault-Tolerant Prescribed Performance Control Algorithm for Underwater Acoustic Sensor Network Nodes With Thruster Saturation

This paper designs a prescribed performance fault-tolerant control strategy for the underwater acoustic sensor network nodes (UASNN) trajectory tracking control in the presence of ocean current disturbances, modeling uncertainties, and thruster faults. By using a general uncertainties observer, the influence of disturbances and uncertainties are estimated. Additionally, a novel performance function which determines explicitly the maximum convergence time is utilized. Based on the new performance function and corresponding error transformation, the 6-DOF tracking errors are restricted to prescribed bounds to ensure the desired transient and steady response. Furthermore, when considering thruster saturation, we introduce an auxiliary system to compensate for the saturation. The closed-loop system stability is proved by Lyapunov theory. The numerical simulations for three thruster faults are carried out to demonstrate that the proposed strategy is effective.

Fixed-Time Feedback Control of the Hydraulic Turbine Governing System

Dealing with convergence time and dealing with steady state are two of the most challenging problems in the field of stability of the hydraulic turbine governing system. In this paper, we solve these two challenging problems by designing fixed-time feedback controllers. The design of the controllers is based on the fixed-time theory and backstepping method. Compared with the existing controllers for fixed-time, finite-time, and other techniques, the designed controllers make the maximum convergence time of the system unaffected by the initial state. The convergence time is also shorter. Meanwhile, they are continuous and do not include any sign function, and hence, the chattering phenomenon in most of the existing results is overcome via nonchattering control. In addition, they give the system better stability and robustness to disturbances. Finally, the numerical simulation results in this paper will contribute to a better understanding of the effectiveness and superiority of the proposed controllers.