Presence Of External Disturbances Research Articles

Convolutional Long Short-Term Memory Predictor for Collaborative Remotely Operated Vehicle Trajectory Tracking in a Leader–Follower Formation Subject to Communication and Sensor Latency in the Presence of External Disturbances

Nowadays, collaborative operations between Remotely Operated Vehicles (ROVs) face considerable challenges, particularly in leader–follower schemes. The underwater environment imposes limitations on acoustic modems, leading to reduced transmission speeds and increased latency in ROV position and speed transmission. This complicates effective communication between the ROVs. Traditional methods, such as Recursive Least Squares (RLS) predictors and the Kalman filter, have been employed to address these issues. However, these approaches have limitations in handling non-linear patterns and disturbances in underwater environments. This paper introduces a Convolutional Long Short-Term Memory (ConvLSTM) predictor designed to enhance communication and trajectory tracking between ROVs in a leader–follower scheme. The proposed ConvLSTM aims to address the shortcomings of previous methods by adapting effectively to varying conditions, including ocean currents, communication delays, and signal interruptions. Simulations were conducted to evaluate ConvLSTM’s performance and compare it with other advanced predictors, such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), under different conditions. The results demonstrated that ConvLSTM achieved a 13.9% improvement in trajectory tracking, surpassing other predictors in scenarios that replicate real underwater conditions and multi-vehicle communication. These results highlight ConvLSTM’s potential to significantly enhance the performance and stability of collaborative ROV operations in dynamic underwater environments.

Robust Linear Discrete Control for a Hexacopter: Experimental Results

This paper presents a discrete-time robust linear control method for tracking a hexacopter's trajectory in the presence of external disturbances. The control of multi-rotor type unmanned aerial vehicles (UAVs) has gained considerable attention recently due to their various applications, such as crop spraying in precision agriculture. The control of UAVs requires robustness to reject disturbances and accommodate dynamic uncertainties. To achieve this goal, the robust discrete-time control action is designed in two stages. The first stage utilizes the solution of a difference Riccati-equation to guarantee system stability in an optimal sense. The second stage provides system robustness against external disturbances and uncertain dynamics. Furthermore, the Lyapunov stability theory for discrete linear systems is used to derive system asymptotic stability. Finally, experimental results of the hexacopter flight are provided to illustrate the effectiveness of the presented control law.

Quadrotor attitude control with a modified fixed‐time synergetic controller: A geometric analysis and experimental validation

AbstractThis article proposes a novel modified fixed‐time synergetic control (MFTSC) approach to address the quadrotor control problem in the presence of external disturbances. In contrast to previous fixed‐time synergetic control (FTSC) methods, the proposed approach employs an aggregate macrovariable expression with independent fractional powers. This expression expands the set of real numbers that the parameters of the fractional power can take, leading to a significantly higher rate of convergence toward optimal control parameters (more than 28‐fold), as demonstrated through geometric analysis. The stability of the proposed control system is established, and numerical simulations and experimental validation are conducted using a quadrotor test bench to corroborate the theoretical results.

Adaptive sliding mode control with nonlinear MPC-based obstacle avoidance using LiDAR for an autonomous surface vehicle under disturbances

This manuscript proposes a path-following and collision avoidance guidance and control system for an autonomous surface vehicle operating in cluttered environments under perturbations. A nonlinear model predictive control strategy handles the state and control constraints for obtaining a guidance law. The approach considers the vessel sideslip angle to provide the desired heading angle, satisfying path-following and obstacle avoidance. Additionally, to solve the nonlinear optimization problem for obtaining the guidance law in real-time, the acados software package is used. Furthermore, in order to guarantee that the vehicle tracks the guidance in presence of model uncertainties and external disturbances, a robust adaptive sliding mode low-level controller is adopted. This controller is finite time convergent, and dynamically adapts its control gain to use only the necessary control inputs. Furthermore, a low-memory and computationally-light obstacle detection strategy employing a LiDAR sensor is presented and proven to run in real-time. Simulation and experimental tests conducted using a customized prototype validate the advantages of the proposed strategy and demonstrate the effectiveness of the path following while evading multiple obstacles in the presence of external disturbances and model discrepancies.

Improved delay-dependent stability analysis of digital filters with generalized overflow arithmetic

This paper introduces a new set of conditions that establish Input-to-State Stability (ISS) for digital filters with external disturbance input and time-varying delays, all within the context of generalized overflow arithmetic. A unique augmented Lyapunov function and a novel summation inequality are applied to formulate a new criterion for ISS. This criterion assesses the ISS of digital filters in the presence of external disturbance, enabling the identification of asymptotic stability even in the absence of such disturbance. The criteria are developed using linear matrix inequalities (LMIs). The efficacy of the results is demonstrated through three numerical examples, highlighting that the proposed criteria are less conservative than some recent results.

Application of improved appointed time control in helicopter mode of a tilt-rotor eVTOL aircraft

In this paper, an appointed time sliding mode control scheme is proposed for trajectory tracking control of a tilt-rotor electric vertical takeoff and landing (eVTOL) aircraft. First, the error dynamic model of helicopter mode of the tilt-rotor eVTOL aircraft is presented. Second, an appointed time prescribed performance function (ATPPF) is designed as the expected value of the error, which guides the error to converge to zero at the appointed time. Then, a predefined time controller ensures that the actual error tracks the expected error before the settling time set by the ATPPF. By setting the time parameters of the ATPPF and the predefined time controller to the same value, the actual error will converge to zero vicinity at the appointed time. Finally, the predefined time convergence of the closed-loop system is proved via Lyapunov analysis. The effectiveness of the appointed time control strategy is demonstrated in the simulation results in the presence of external disturbances, dynamic uncertainties and control input constraints.

Fixed-time formation tracking for unmanned surface vehicles: A multi-layer neural networks approach

This paper investigates the distributed fixed-time formation problem of unmanned surface vehicles (USVs) in the presence of external disturbances, model uncertainties, input saturation and quantization constraints. To deal with the problem, a fixed-time sliding-mode control algorithm is proposed, where multi-layer neural networks (MNNs) are designed to approximate the unknown dynamics and composite disturbances of the system. The proposed MNNs combine the advantages of fuzzy neural networks (FNNs) and radial basis function neural networks (RBFNNs), exhibiting robust dynamic characteristics. Furthermore, the non-singular fast terminal sliding mode (NFTSM) is integrated into the fixed-time control framework to improve the robustness and speed of convergence for uncertain USV systems. Comparative simulations conducted with USVs demonstrate the superiority and effectiveness of the proposed algorithm.

Research on a Path Tracking Control Strategy for Autonomous Vehicles Based on State Parameter Identification

With the rapid development of autonomous driving technology, estimating and controlling key vehicle state parameters under complex road conditions have become critical challenges. This study combines Unscented Kalman Filtering (UKF) and Sliding Mode Control (SMC) methods to propose an integrated control model for achieving more efficient control. First, a three-degrees-of-freedom vehicle dynamics model based on the Dugoff tire model is constructed to accurately estimate key vehicle state parameters. Next, UKF is used to estimate road friction coefficients and key vehicle state parameters, and its performance is compared with Extended Kalman Filtering (EKF) under various conditions. The results show the superiority of UKF in identifying road friction coefficients. Based on SMC theory, a sliding surface is designed, and the functional relationship between state variables and control variables is derived to establish the corresponding control model. Joint simulations using Carsim and Simulink under different conditions validate the real-time performance and effectiveness of the designed UKF-SMC integrated control strategy in the presence of external disturbances and system uncertainties. Simulation results indicate that this strategy effectively enhances the overall performance and safety of autonomous vehicles, providing an accurate real-time solution capable of handling complex and variable road conditions. The proposed UKF-SMC integrated control strategy not only proves its theoretical superiority but also demonstrates promising practical applications in simulation experiments. This study provides reliable technical support for the development of autonomous driving technology under complex road conditions.

Adaptive-neural command filtered synchronization control of tele-robotic systems using disturbance observer with safety enhancement

Due to the nature of the tele-robotic systems, some main problems are imposed, such as external disturbances, time-varying delays and external torques. However, some applications such as tele-surgery systems, require high precision, safety and accurate transmission of information between the leader and follower robots. In order to facilitate the imposed restrictions and to guarantee transient-state, and even steady-state performances in the presence of external disturbances and system uncertainties, the time-varying full state constrained control is employed by applying the Barrier Lyapunov function (BLF). In this regard, a new adaptive neural network torque observer is proposed to make the system independent from the force sensors. Moreover, the independence of the proposed algorithm from perfect knowledge of the manipulator dynamics and time-delay’s derivative of communication channels are advantages of this paper. Furthermore, the key idea of this paper is to lessen the computational complexity in the backstepping-based adaptive neural controller by means of the command filter strategy and the BLF approach is combined to converge the synchronization error signals into a predefined constraint. In order to consider practical limitations, time-varying delays in the communication channel and input saturation constraint are also considered in the design mechanism. Finally, the stability analysis of the observer and controller together is conducted and the evaluation of the performance of the proposed method is performed through a series of various scenarios and comparisons.

A High-Gain Observer Approach to Robust Trajectory Estimation and Tracking for a Multi-Rotor Uav

Abstract Using the context of trajectory estimation and tracking for multi-rotor UAVs, we explore the challenges in applying high-gain observers to highly dynamic systems. The multi- rotor will operate in the presence of external disturbances and modeling errors. At the same time, the reference trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume the only measurements that are available are the position and orientation of the multi-rotor and the position of the reference sys- tem. We adopt an extended high-gain observer (EHGO) estimation framework to estimate the unmeasured multi-rotor states, modeling errors, external disturbances, and the reference trajectory. We design a robust output feedback con- troller for trajectory tracking that comprises a feedback linearizing controller and the EHGO. The proposed control method is rigorously analyzed to establish its stability properties. Finally, we illustrate our theoretical results through numerical simulation and experimental validation in which a multi-rotor tracks a moving ground vehicle with an unknown trajectory and dynamics and successfully lands on the vehicle while in motion.

Enhancing Surgical Robotics: A Dynamic Model and Optimized Control Strategy for Cable-Driven Continuum Robots

Abstract This paper tackles the challenges encountered in surgical continuum robotics by introducing a dynamic model tailored for a cable-driven continuum robot. The intricacies of dynamic modeling and control frequently lead to suboptimal outcomes. Prior studies have often lacked comprehensive descriptions of individual robot component movements, thereby impeding control processes, especially in the presence of external disturbances. Although machine learning-based models show promise across different domains, they face hurdles in continuum robotics due to the complexity of the systems involved. Traditional mathematical models, in contrast, offer explicit equations, providing better interpretability, unlike machine learning models that may struggle with generalization, especially in highly nonlinear systems like continuum robots. The developed model adeptly captures the kinematic and dynamic constraints of various robot segments, serving as the foundation for a robust optimized control strategy. This strategy, which integrates computed torque control and particle swarm optimization (PSO-CTC), enables real-time computation of joint torques based on feedback, ensuring precise and stable task execution even amidst external perturbations. Comparative analysis with an optimized proportional integral derivative (OPID) controller unequivocally demonstrates the superiority of the optimized computed torque controller (OCTC) in settling time, overshoot, and robustness against disturbances. This advancement represents a noteworthy contribution to robotics, with the potential to significantly enhance continuum robot performance in surgical and inspection applications, thereby fostering innovative advancements across various fields.

Preview‐Based Path‐tracking Stability Control with Vehicle Dynamic Uncertainty via Robust Weighted LPV/[formula omitted] Technique

This article proposes a preview-based robust path-tracking control technique for maintaining lateral stability and tracking performance of autonomous vehicles, particularly in the presence of external disturbances and modeling uncertainties. First, a vehicle-road dynamic model with tire norm-bounded uncertainty is developed, which includes time-varying velocities and preview distances. The lateral and yaw dynamic characteristics are also analyzed in the frequency domain. Subsequently, an optimal preview model corresponding to sideslip-yaw rate states and longitudinal velocities is formulated employing a fuzzy logic model, and the sideslip angle is estimated using a sliding mode observer. Furthermore, a linear parameter-varying (LPV)/H∞ path-tracking controller that satisfies the pole placement and performance constraint is constructed to guarantee robustness and lateral stability across the whole parameters space with the coexistence of external disturbances and parametric uncertainties. Finally, the simulation results demonstrate that the proposed controller substantially enhances tracking performance while also maintaining excellent lateral stability.

EFFECTIVENESS OF STEP SQUARE EXERCISES TO PREVENT FALLS IN MULTIPLE SCLEROSIS PATIENTS – A NARRATIVE REVIEW

In this study the literature review focus on the available evidence for the physiotherapy intervention to prevent falls in among multiple sclerosis. Research involved a computerized data base pertaining to studies that include the physiotherapy among multiple sclerosis patients 15 articles that showed outcomes relative to falls, physiotherapy intervention to prevent falls among selected multiple sclerosis patients with multiple sclerosis are more likely to fall, especially when walking, due to their slower proactive equilibrium reactions, decreased ability to maintain balance when reaching, central integration impairment, slow somatosensory conduction, fatigue, delayed reaction, attention deficit, difficulty maintaining stability in the presence of external disturbances, and overload fatigue of the motor cortex. Stand-related trunk instability was observed in MS patients, a rise in trunk sway, and a rise in postural sway when performing two tasks at once. Minimal literature on falls prevention there is scarcity of study which intervention among Step square exercises and Wii board exercises. AIM: The study aimed to find the effectiveness of Step square exercises to prevent falls in MS patients. KEY WORDS: Multiple sclerosis, intervention, equilibrium reactions, integration impairment, somatosensory conduction, attention deficit, motor cortex, trunk stability, trunk sway,

Formation tracking control with disturbance rejection in leader-follower multi-agent systems under dynamic event-triggered mechanism

This paper investigates the problem of time-varying formation tracking (TVFT) in a linear multi-agent systems (MASs) with external disturbance in a type of directed network topology, and proposes a formation control strategy in a leader-follower control framework with disturbance rejection under a dynamic event-triggering mechanism (DETM). Firstly, in order to improve the overall performance of the system, an extended state observer is used to estimate the status of the follower and the presence of external disturbance in real time. Secondly, a DETM is designed to avoid continuous communication between adjacent intelligent agents. And the formation tracking error between adjacent multi-agents is used to formulate a distributed TVFT control strategy. Furthermore, a compensation function is designed to actively offset the impact of time-varying disturbances on system. A Lyapunov function is then established for stability analysis, verifying the absence of Zeno behavior with this DETM. Finally, a comparative numerical simulation was conducted using an unmanned aerial vehicle (UAV) cluster case to verify that the proposed method can save communication resources to a greater extent and has good control performance.

Integrated dynamics and control of space multi-joint robotic arm

This paper presents research on the integrated dynamics and control methods of a multi-joint robotic arm system installed on a spatial inertial platform. An integrated recursive dynamic model of the posture of the multi-joint robotic arm is established by using the method of dual quaternions. This model can succinctly describe the position and attitude relationships among the joints of the robotic arm. Based on this model, a sliding mode variable structure controller is designed, which can achieve convergence of position and attitude errors even in the presence of external disturbances. The designed controller is validated through simulations, the results of which demonstrate satisfactory control performance, offering reference value for practical engineering applications.

Chattering Free Sliding-Mode Controller Design for Underactuated Tower Cranes With Uncertain Disturbance

Tower cranes are widely used in various industrial sites and require a high level of anti-disturbance performance from the crane controller due to the variability of the application environment. Although sliding mode control is a typical anti-disturbance control method, most sliding mode controllers suffer from output chattering and do not reflect well under severe external disturbances. To address these issues from a practical application perspective, a finite time sliding mode control method using the super-twisting algorithm (STA) is proposed for underactuated tower cranes operating in the presence of external disturbances. The proposed method includes a finite-time chattering-free sliding mode controller that eases the output torque chattering problem and a finite-time sliding mode observer based on STA to observe the uncertain disturbances. The observed values are then applied to the controller through feedforward compensation. Experimental results show that the proposed control method effectively weakens output torque chattering, compensates for disturbance quickly, and accurately completes control tasks.

Adaptive backstepping controller based on a novel framework for dynamic solution of an ankle rehabilitation spherical parallel robot

AbstractThis research offers an adaptive model-based methodology for autonomous control of 3-RRR spherical parallel manipulator (RSPM) based on a novel modeling framework. RSPM is an overconstrained parallel mechanism that has a variety of applications in medical procedures such as ankle rehabilitation because of its precision and accuracy. However, obtaining a complete explicit dynamic model of these mechanisms for tracking purposes has been a problematic challenge due to their inherent singularities, coupling effects of the limbs, and redundant constraints imposed by the intermediate joints. This paper presents a novel algorithm to obtain the analytical kinematic solutions of RSPMs based on the closed-loop vector method, which includes constraint analysis. By incorporating constrained kinematics into the dynamic model, a comprehensive explicit dynamic solution of the non-overconstrained version 3-RCC of RSPM is developed in task space, based on screw theory and the linear homogeneous property of algebraic equations on the manipulator twist. Based on the proposed computational framework, a robust self-tuning backstepping control (STBC) strategy is applied to the robot to overcome the effect of external disturbances and time-varying uncertainties. Furthermore, an observer-based compensation (OBC) method is presented for dealing with the nonlinear hysteresis loops of the ankle during trajectory tracking purposes. The closed-loop stability of the whole system including STBC and OBC is theoretically performed by Lyapunov methods. The proposed methodologies are validated by realistic co-simulations in different scenarios. For instant, in the presence of external disturbances, the maximum tracking error norm of STBC is 37.5% less than the sliding mode approach.

Modified Universal Integral Regulator for Quadrotor UAV in the Presence of External Disturbances

Universal Integral Regulator (UIR) is a widely used control law for control problems, it is based on sliding mode control with the inclusion of a Conditional Integrator (CI). In the original approach of this control law, the main gain is considered a constant. The purpose of this paper is to solve a quadrotor tracking problem using a new nonlinear control law based on the quadrotor tracking problem using a new nonlinear control law based on the UIR, considering the main gain dependent on the tracking error (variable gain), the control law is called in the present work as Modified Universal Integral Regulator (MUIR). It is expected that the MUIR can improve the transient response and reduce the control demand when compared to previous approaches of similar controllers. The adopted Newton-Euler quadrotor model and the controller design are treated separately in two subsystems, attitude and position control loops. The stability of MUIR is demonstrated by the use of a candidate Lyapunov function. Finally, in order to validate the robustness and choice of the proposed controllers, several numerical simulations were developed in the presence of external disturbances. Less error and control activity during transient response were observed when compared to the original Universal Integral Regulator controller.

Predictor-based constrained fixed-time sliding mode control of multi-UAV formation flight

In this work, a predictor-based fixed-time sliding mode control is designed to tackle the problem of achieving precise trajectory tracking control of multiple unmanned aerial vehicle formation flight. The proposed approach simultaneously addresses various practical requirements, such as optimality, constraints, fixed-time convergence, external interferences, and computational burden. Initially, using the Taylor series concept, we derive the predictive models for the fixed-time sliding surface and reaching law. Subsequently, we formulate a constrained performance index by integrating these predictions with the control input. Ultimately, the optimal control input is computed through solving the constrained quadratic optimization problem. An outstanding feature of this study is the theoretical guarantee of closed-loop fixed-time system stability in the presence of external disturbances and constraints. Furthermore, the comparative analysis with sliding mode predictive control law and fixed-time sliding mode control methods demonstrates that the proposed approach achieves a straightforward control method with improved performance and acceptable accuracy. Finally, an experimental test is conducted to demonstrate the algorithm's potential for hardware implementation by employing the Hardware-in-the-Loop test.

Fuzzy Super-Twisting Sliding Mode Controller for Switched Reluctance Wind Power Generator in Low-Voltage DC Microgrid Applications

There is limited research focused on achieving optimal torque control performance of Switched Reluctance Generators (SRGs). The majority of existing studies tend to favor voltage or power control strategies. However, a significant drawback of SRGs is their susceptibility to high torque ripple. In power generation systems, torque ripple implicates fluctuations in the generated power of the generator. Moreover, high torque ripple can lead to mechanical vibrations and noise in the powertrain, impacting the overall system performance. In this paper, a Torque Sharing Function (TSF) with Indirect Instantaneous Torque Control (IITC) for SRG applied to Wind Energy Conversion Systems (WECS) is proposed to minimize torque ripple. The proposed method adjusts the shared reference torque function between the phases based on instantaneous torque, rather than the existing TSF methods formulated with a mathematical expression. Additionally, this paper introduces an innovative speed control scheme for SRG drive using a Fuzzy Super-Twisting Sliding Mode Command (FSTSMC) method. Notably robust against parameter uncertainties and payload disturbances, the proposed scheme ensures finite-time convergence even in the presence of external disturbances, while effectively reducing chattering. To assess the effectiveness of the proposed methods, comprehensive comparisons are made with traditional control techniques, including Proportional–Integral (PI), Integral Sliding Mode Control (ISMC), and Super-Twisting Sliding Mode Control (STSMC). The simulation results, obtained using MATLAB®/SIMULINK® under various speeds and mechanical torque conditions, demonstrate the superior performance and robustness of the proposed approaches. This study presents a thorough experimental analysis of a 250 W four-phase 8/6 SRG. The generator was connected to a DC resistive load, and the analysis focuses on assessing its performance and operational characteristics across different rotational speeds. The primary objective is to validate and confirm the efficacy of the SRG under varying conditions.