Robust Position Control Research Articles

Predefined-time sliding mode position and attitude coupling control for in-cabin robots on space stations

As space science advances, the significance of space robotics research escalates, particularly in control methodologies. Addressing the work efficiency requirements during the mission of the in-cabin robot, it is necessary to develop a fast and robust position and attitude coupling controller for in-cabin robots. This paper presents a predefined-time sliding mode position and attitude coupling control method, employing the SE(3) frame description. The first, the dynamic model of the in-cabin robot is developed within the SE(3) framework. Then, the predefined-time position and attitude coupling sliding mode controller is designed with a sliding surface based on the dynamic error model, and a hyperbolic tangent function is used to suppress system chattering. In addition, the stability of the proposed control method is rigorously confirmed using the Lyapunov theorem, ensuring that the convergence time is accurately predefined. Finally, simulation results are provided to demonstrate the effectiveness of this novel control strategy. A comparative analysis with fixed-time sliding mode control highlights its superior performance.

Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning

This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient to model uncertainties, unmodeled dynamics, external disturbances, and actuator faults. The proposed approach synergistically combines a neural network for handling model uncertainties, unmodeled dynamics, and actuator faults, with a disturbance observer for compensating external disturbances and neural network estimation errors. The command-filtered backstepping technique avoids the explosion of complexity inherent to traditional backstepping, while integrating integral action into the design results in the elimination of the steady-state tracking error. Besides, a composite learning method optimizes the update laws for the neural network and disturbance observer weights, enhancing control performance. Despite the presence of uncertainties, the closed-loop system stability is guaranteed by the Lyapunov stability theorem. Simulation results demonstrate the proposed controller’s ability to handle severe actuator faults, unmodeled dynamics, and measurement noise without requiring explicit fault detection and isolation schemes.

Dynamic Modeling and Control of a 4-Wheel Narrow Tilting Vehicle

The automotive industries currently face challenges such as emission limits, traffic congestion, and limited parking, which have prompted shifts in consumer preferences and modern passenger vehicle requirements towards compact vehicles. However, given the inherent limited width of compact vehicles, the potential risk of vehicle rollover is greater than that of regular vehicles. This paper addresses the safety concerns associated with vehicle rollover, focusing on narrow tilting vehicles (NTVs). Quantifying stability involves numerical indicators such as the lateral load transfer ratio (LTR). Additionally, a unique approach is taken by applying ZMP (zero moment point), commonly used in the robotics field, as an indicator of vehicle stability. Effective roll control requires a detailed analysis of the vehicle’s characteristic model and the derivation of lateral and roll dynamics. The paper presents the detailed roll dynamics of an NTV with a MacPherson strut-type suspension. A stability-enhancing method is proposed using a cascade structure based on the internal robust position controller and outer roll stability controller, addressing challenges posed by disturbances. Experimental verification using Simscape Multibody and CarSim validates the dynamic model and controller’s effectiveness, ensuring the reliability of the proposed tilting control for NTVs in practical scenarios.

Sensing and Control Integration for Thrust Vectoring in Heavy UAVs: Real-World Implementation and Performance Analysis

Unmanned Aerial Vehicles (UAVs) have garnered significant attention among researchers due to their versatility in diverse missions and resilience in challenging conditions. However, electric UAVs often suffer from limited flight autonomy, necessitating the exploration of alternative power sources such as thermal engines. On the other hand, managing thermal engines introduces complexities and internal uncertainties into the system. In this paper, an Adaptive Robust attitude controller (ARAC) is proposed to address these challenges by drawing inspiration from helicopter solutions while minimizing mechanical intricacies. Specifically, the designed algorithm employs Thrust Vector Control (TVC) for an industrial heavy Multi-Ducted Fan (MDF), known for its superior static stability compared to conventional ducted fans. Subsequently, an integrated flap vanes system is positioned at the exhaust of the ducts for precise attitude control, effectively removing unwanted yaw moments associated with traditional propellers. This research builds on prior authors’ works to establish a proper mathematical and aerodynamic model. Also, using former simulation results to conduct real flight experiments aimed at enhancing TVC functionality. The findings highlight the effectiveness of this approach for heavy UAV applications. It is worth noting that the practical value of this research lies in its potential to significantly extend flight autonomy supplied by thermal engines and improve the resilience of UAVs in challenging real-world missions. This is particularly achievable provided that the design of flap vanes aligns closely with the dimensions of the duct system, offering a promising solution to a critical engineering challenge in the field of UAV technology.

Scaling procedure for on-ground testing of a robust attitude and vibration control architecture for a large flexible satellite

The agility of satellites equipped with large and flexible structures, such as solar panels or antennas, poses a significant challenge due to the influence of elastic dynamics on attitude motion. This results in reduced pointing accuracy and dimensional stability of the payload. To address this issue, an Active Collocated Attitude Control (ACAC) system has been developed, utilizing actuators for both the elastic dynamics (distributed piezoelectric devices) and attitude control (ON-OFF thrusters). In the initial phase of the project, the algorithm underwent extensive verification using a high-fidelity numerical model of a spacecraft with two very-low frequency flexible solar panels and a payload consisting of two long booms and a transversal antenna's reflector. The paper focuses on the second phase of the project, which revolves around the experimental verification of the control architecture concept. To achieve this, a scaling procedure employing the dynamic analogy approach was implemented to determine the design parameters of a free-floating platform equipped with elastic appendages. This ensured that the resulting elastic multibody system displayed similar dynamic behavior to the full-scale satellite. Based on the scaling parameters, a test rig was manufactured, and actuators and sensors were optimally positioned. The performance of the ACAC system was then compared to that of traditional benchmark controllers. The results show that the ACAC system demonstrates superior capabilities in performing fast slew maneuvers without developing unstable interactions with the elastic dynamics.

Robust attitude coordinated control for gravitational-wave detection spacecraft formation with large-scale communication delays

This paper concentrates on robust attitude control issues for spacecraft formation in gravitational-wave detection missions. Notably, with the large-scale communication delays brought by the million kilometers-scale inter-satellite distance, the exponential convergence of the closed-loop delayed system is firstly achieved via a new delay-dependent nominal attitude coordinated control scheme. Then, a finite-time disturbance observer is deployed to reconstruct the unknown lumped disturbance from internal uncertainties and the external environment. Meanwhile, an integral sliding manifold is embedded in the delay-dependent attitude controller for superior systems’ robustness. Finally, two simulation examples illustrate the feasibility and effectiveness of the proposed attitude control strategy for in-orbit gravitational-wave detection spacecraft systems.

Human–Robot Cooperation Control Strategy Design Based on Trajectory Deformation Algorithm and Dynamic Movement Primitives for Lower Limb Rehabilitation Robots

Compliant physical interactions, interactive learning, and robust position control are crucial to improving the effectiveness and safety of rehabilitation robots. This paper proposes a human–robot cooperation control strategy (HRCCS) for lower limb rehabilitation robots. The high-level trajectory planner of the HRCCS consists of a trajectory generator, a trajectory learner, a desired trajectory predictor, and a soft saturation function. The trajectory planner can predict and generate a smooth desired trajectory through physical human–robot interaction (pHRI) in a restricted joint space and can learn the desired trajectory using the locally weighted regression method. Moreover, a triple-step controller was designed to be the low-level position controller of the HRCCS to ensure that each joint tracks the desired trajectory. A nonlinear disturbance observer is used to observe and compensate for total disturbances. The radial basis function neural networks (RBFNN) approximation law and robust term are adopted to compensate for observation errors. The simulation results indicate that the HRCCS is robust and can achieve compliant pHRI and interactive trajectory learning. Therefore, the HRCCS has the potential to be used in rehabilitation robots and other fields involving pHRI.

Design and real-time implementation of a robust backstepping non-singular integral terminal sliding mode attitude control for a quadrotor aircraft

This paper concerns the problem of robust attitude control of a quadrotor aircraft subjected to modeling inaccuracies and wind disturbances. First, the model dynamics and state space representation of the quadrotor system are designed by considering the impact of disturbances and uncertainties. Then, a novel robust backstepping non-singular integral terminal sliding mode control (BNITSMC) is synthesized for the quadrotor attitude with the aim of enhancing the tracking accuracy. To strengthen the system’s robustness and guarantee the reachability of the sliding surface while reducing chattering, the supertwisting algorithm (ST) is used. Closed-loop stability and state convergence are guaranteed via the Lyapunov theory. Some experimental flight tests have been executed to validate the workability of the developed flight control. The obtained results prove that the recommended technique can provide improved performance in terms of stabilization and tracking precision.

An LMI-based robust state-feedback controller design for the position control of a knee rehabilitation exoskeleton robot: Comparative analysis

Rehabilitation exoskeleton robots play a crucial role in restoring functional lower limb movements for individuals with locomotor disorders. Numerous research studies have concentrated on adapting the control of these rehabilitation robotic systems. In this study, we investigate an affine state-feedback control law for robust position control of a knee exoskeleton robot, taking into account its nonlinear dynamic model that includes solid and viscous frictions. To ensure robust stabilization, we employ the Lyapunov approach and propose three methods to establish stability conditions using the Schur complement, the Young inequality, the matrix inversion lemma, and the S-procedure lemma. These conditions are formulated as Linear Matrix Inequalities (LMIs). Furthermore, we conduct a comprehensive comparison among these methods to determine the most efficient approach. At the end of this work, we present simulation results to validate the developed LMI conditions and demonstrate the effectiveness of the adopted control law in achieving robust position control of the knee exoskeleton robot.

Designing a fixed-time observer-based adaptive non-singular sliding mode controller for flexible spacecraft

This paper investigates the stabilization problem with a fixed-time approach for a flexible spacecraft subject to vibrations of flexible modes, unknown bounded disturbance, and inherent uncertainty. To estimate the modal variables of a flexible spacecraft which are often unmeasurable in practice, an observer with guaranteed fixed-time convergence is designed. Using the estimated modal variables, a fixed-time non-singular sliding mode controller is designed so that the desired attitude can be reached before a pre-specified time threshold regardless of the spacecraft's initial attitude. By incorporating the estimated modal variables in the control design, significant reduction in the steady-state error of the system response is achieved. The proposed control system is further enhanced with an adaptive law to increase robustness against unknown external disturbances and uncertainties. Stability analysis based on Lyapunov theory guarantees the convergence of observer estimation error and spacecraft attitude error to a pre-determined set before a fixed threshold. Simulation results validate the promising performance of the proposed control system, highlighting its effectiveness in achieving accurate and robust attitude control for flexible spacecraft.

Robust attitude tracking control for a variable‐pitch quadrotor with uncertainties

SummaryVariable‐pitch quadrotors have demonstrated their capability to enhance control performance and overcome limitations compared to conventional fixed‐pitch quadrotors, while still facing challenges such as nonlinearity, couplings, uncertainties and so on. This article aims to address the robust attitude control problem of variable‐pitch quadrotors afflicted with parametric uncertainties and unpredictable external disturbances. Initially, a practical actuator allocation strategy for the motor‐propeller is proposed, followed by the development of a nonlinear robust attitude tracking controller capable of addressing all uncertainties. The effectiveness of the proposed controller in ensuring exponential stability of the closed‐loop system, thereby driving the attitude tracking error to a small range within a finite time, has been substantiated. To evaluate the control performance, a hardware‐in‐the‐loop testbed is constructed to conduct real‐time experiments. The experimental results from attitude control case, comparative case, and position control case demonstrate that the designed motor‐propeller allocation method and robust controller are effective in achieving a fast response and excellent control performance.

Robust Attitude Control With Fixed Exponential Rate of Convergence and Consideration of Motor Dynamics for Tilt Quadrotor Using Quaternions

Robust Attitude Control With Fixed Exponential Rate of Convergence and Consideration of Motor Dynamics for Tilt Quadrotor Using Quaternions

Energy-based modeling and robust position control of a dielectric elastomer cardiac assist device

This paper is concerned with the port Hamiltonian modeling and control of a dielectric elastomer actuator used for a cardiac assistance device. The proposed non-linear model is identified under different applied voltages and pressures, and validated against experimental results with relative errors of less than 0.3%. Subsequently, two passivity-based controllers are designed to stabilize the actuator at a desired position. The first controller is designed using control by interconnection. The second one considers additional integral action to reject disturbances while preserving the passivity of the closed-loop system.

Robust Position Control of a Continuum Manipulator Based on Selective Approach and Koopman Operator

Continuum manipulators have infinite degrees of freedom and high flexibility, making it challenging for accurate modeling and control. Some common modeling methods include mechanical modeling strategy, neural network strategy, constant curvature assumption, etc. However, the inverse kinematics of the mechanical modeling strategy is difficult to obtain while a strategy using neural networks may not converge in some applications. For algorithm implementation, the constant curvature assumption is used as the basis to design the controller. When the driving wire is tight, the linear controller under constant curvature assumption works well in manipulator position control. However, this assumption of linearity between the deformation angle and the driving input value breaks upon repeated use of the driving wires which get inevitably lengthened. This degrades the accuracy of the controller. In this work, the Koopman theory is proposed to identify the nonlinear model of the continuum manipulator. Under the linearized model, the control input is obtained through model predictive control (MPC). As the lifted function can affect the effectiveness of the Koopman operator-based MPC (K-MPC), a novel design method of the lifted function through the Legendre polynomial is proposed. To attain higher control efficiency and computational accuracy, a selective control scheme according to the state of the driving wires is proposed. When the driving wire is tight, the linear controller is employed; otherwise, the K-MPC is adopted. Finally, a set of static and dynamic experiments has been conducted using an experimental prototype. The results demonstrate high effectiveness and good performance of the selective control scheme.

Fault-tolerant attitude control of the satellite in the presence of simultaneous actuator and sensor faults

In this paper, a robust attitude control algorithm is developed based on backstepping sliding mode control for a satellite using reaction wheels and thrusters that can perform its mission despite faulty actuators. In this method, the actuator dynamics have been considered to design the controller and the asymptotic stability of the proposed algorithm has been proven based on the Lyapunov theory. The designed controller can converge the attitude of the system into the desired path in the presence of faulty actuators. Then a fault-tolerant attitude estimation system is designed based on federated unscented Kalman filters that can be effectively employed to detect and isolate sensor faults. Finally, the performance of the designed attitude estimation and controller is investigated by simulation in the presence of both actuator and sensor faults.

Robust Collision-Free Guidance and Control for Underactuated Multirotor Aerial Vehicles

This paper is concerned with the robust collision-free guidance and control of underactuated multirotor aerial vehicles in the presence of moving obstacles capable of accelerating, linear velocity and rotor thrust constraints, and matched model uncertainties and disturbances. We address this problem by using a hierarchical flight control architecture composed of a supervisory outer-loop guidance module and an inner-loop stabilizing control one. The inner loop is designed using a typical hierarchical control scheme that nests the attitude control loop inside the position one. The effectiveness of this scheme relies on proper time-scale separation (TSS) between the closed-loop (faster) rotational and (slower) translational dynamics, which is not straightforward to enforce in practice. However, by combining an integral sliding mode attitude control law, which guarantees instantaneous tracking of the attitude commands, with a smooth and robust position control one, we enforce, by construction, the satisfaction of the TSS, thus avoiding the loss of robustness and use of a dull trial-and-error tweak of gains. On the other hand, the outer-loop guidance is built upon the continuous-control-obstacles method, which is incremented to respect the velocity and actuator constraints and avoid multiple moving obstacles that can accelerate. The overall method is evaluated using a numerical Monte Carlo simulation and is shown to be effective in providing satisfactory tracking performance, collision-free guidance, and the satisfaction of linear velocity and actuator constraints.

Anomaly Detection for Agile Satellite Attitude Control System Using Hybrid Deep-Learning Technique

Agile low-Earth-orbit (LEO) observation satellites need a robust attitude control and determination system. It is a critical satellite subsystem, which stabilizes the satellite to different desired orientations during its mission using different actuators. The detection of satellite misorientation is a highly challenging problem because it requires continuous monitoring of data from hundreds of satellite sensors to guarantee healthy operability. In this paper, the authors propose a data-driven deep-learning framework for detecting satellite misorientation by analyzing attitude control subsystem telemetry data. The proposed approach combines a hybrid predictive deep-learning model that consists of long short-term memory and convolutional neural networks in two parallel paths to predict telemetry data and a robust isolation forest classifier for anomaly detection purposes that can classify output residuals as normal or anomalous. The hybrid model was optimized by the particle swarm optimization algorithm to ensure faster fitness function convergence with optimal model hyperparameters. The suggested data-driven model was validated using real telemetry datasets, including real anomalous case studies. The experimental results proved the suggested approach’s superiority for identifying satellite misorientation as well as helping satellite operators monitor the system’s health and deduce the causes of anomalies to aid in decision-making.

Robust trajectory tracking control for collaborative robots based on learning feedback gain self-adjustment

Abstract. A robust position control algorithm with learning feedback gain automatic adjustment for collaborative robots under uncertainty is proposed, aiming to compensate for the disturbance effects of the system. First, inside the proportional-derivative (PD) control framework, the robust controller is designed based on model and error. All of the model's uncertainties are represented by functions with upper bounds in order to surmount the uncertainties induced by parameter changes and unmodeled dynamics. Secondly, the feedback gain is automatically adjusted by learning, so that the control feedback gain is automatically adjusted iteratively to optimize the desired performance of the system. Thirdly, the Lyapunov minimax method is used to demonstrate that the proposed controller is both uniformly bounded and uniformly ultimately bounded. The simulations and experimental results of the robot experimental platform demonstrate that the proposed control achieves outstanding performance in both transient and steady-state tracking. Also, the proposed control has a simple structure with few parameters requiring adjustment, and no manual setting is required during parameter setting. Moreover, the robustness and efficacy of the robot's trajectory tracking with uncertainty are significantly enhanced.

Robust adaptive three-dimensional trajectory tracking control scheme design for small fixed-wing UAVs

This paper aims to tackle the three-dimensional trajectory tracking problems of small fixed-wing unmanned aerial vehicles subject to nonlinearities, uncertainties and wind disturbances via adaptive techniques. The control objective is to efficiently control the thrust and the deflections of control surfaces to ensure the unmanned aerial vehicle arrives at a specified location within a given time frame. However, achieving this goal for small fixed-wing unmanned aerial vehicles can be challenging because the precise dynamic model and several parameters are not accessible, making most existing control strategies unworkable. Motivated by these facts, based on feedback linearization techniques, we derive linear models with equivalent disturbances to describe the translational dynamics without requiring precise aerodynamic force model information. To deal with the dilemmas where the norm bounds of equivalent disturbances depend on control inputs, system states, and unknown disturbances, a novel robust adaptive control strategy is designed for position control. Based on the assumption of two-time separation, the control scheme incorporates two parts, namely, a position controller containing the horizontal-plane and altitude parts and a robust filter-based attitude regulator. Also, to prevent chattering issues, we design a practical and robust adaptive position controller under which the tracking error is ultimately bounded The overall closed-loop stability is theoretically investigated based on the Lyapunov arguments. Hardware-in-loop simulation experiments are performed to testify our developed control scheme.

Robust Output Feedback Position Control of Hydraulic Support with Neural Network Compensator

Hydraulic support is important equipment in the fully mechanized mining face, and the control performance of the hydraulic support multi-cylinder system directly affects the smooth progress of coal mining process, which is the basis for the continuous advancement of the coal face. However, the friction force of the hydraulic support in the process of pulling the frame is complex due to the underground environmental load. Moreover, the parameters of the moving cylinder are uncertain, and the state of the system cannot be fully measured, which increases the difficulty of control. A proportional-integral-derivative controller is usually used in electro-hydraulic closed-loop control systems because of its computational complexity, but its robustness is poorly adapted to variable load conditions in the coal mine. Therefore, a robust output feedback position controller is proposed in this paper to improve control accuracy and system robustness with only position signal. The multi-cylinder system of hydraulic support is modeled as a standard type, and then a high-order differentiator is proposed to estimate the immeasurable system states using the output position signal. A neural network compensator is applied to estimate and compensate for the external disturbance of the moving cylinder. Furthermore, the parameters of the ZY3200/08/18D hydraulic support are adopted to analyze the effectiveness of the designed controller in simulations. Finally, a real-time control system of hydraulic support is built, and the experimental results show that the novel robust output feedback controller has improved by 47.2% and 30.6% in tracking accuracy compared to PI controller.