Motion Tracking Control Research Articles

Intelligent Motion Tracking Control of Vehicle Suspension Systems With Constraints via Neural Performance Analysis

A novel adaptive control scheme is developed for active suspension systems (ASSs) based on neural networks (NNs) and backstepping control strategies. Since the springs and piecewise dampers are nonlinear, the unknown internal dynamics are approximated by radial basis function neural networks (RBFNNs). Then, to solve the time-varying constrains of both vertical displacement and corresponding speed in vehicle body, the Tangent Barrier Lyapunov Functions (TBLFs) are incorporated into the controller design. Furthermore, the adaptive controller and adaptive laws are designed to improve the riding comfortable, handling stability and driving safety. In the end, the simulation results show the effectiveness and feasibility of the proposed adaptive algorithm compared with unconstrained adaptive approach.

Simultaneous Motion Tracking and Joint Stiffness Control of Bidirectional Antagonistic Variable-Stiffness Actuators

Since safe human-robot interaction is naturally linked to compliance in these robots, this requirement presents a challenge for the positioning accuracy. The class of variable-stiffness robots features intrinsically soft contact behavior where the physical stiffness can even be altered during operation. Here we present a control scheme for bidirectional, antagonistic variable-stiffness actuators that achieve high-precision link-side trajectory tracking while simultaneously ensuring compliance during physical contact. Furthermore, the approach enables to regulate the pretension in the antagonism. The theoretical claims are confirmed by formal analyses of passivity during physical interaction and the proof of uniform asymptotic stability of the desired link-side trajectories. Experiments on the forearm joint of the DLR robot <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">David</i> verify the proposed approach.

A novel end-effector upper limb rehabilitation robot: Kinematics modeling based on dual quaternion and low-speed spiral motion tracking control

For patients with upper limb dysfunction after stroke, robot-assisted rehabilitation training plays an important role in functional recovery. The existing upper limb rehabilitation robots have some problems, such as complex mechanisms, insufficient compliance, and can only realize the rehabilitation training of shoulder and elbow joints in the horizontal plane. This research proposes a novel end-effector upper limb rehabilitation robot with three degrees of freedom. Two horizontal rotation freedoms are driven by motors and one vertical translation freedom is driven by a pneumatic cylinder. So it can realize the spatial rehabilitation training of shoulder and elbow joints. The rotation and translation transformation of the robot can be represented by a dual quaternion, which is concise in form and clear in the physical meaning. Therefore, this article adopts dual quaternions to complete the robot’s kinematics modeling, inverse kinematics calculation, and terminal spiral motion trajectory planning. To improve the low-speed moving performance of the spiral motion, a sliding mode control strategy plus feedforward compensation is employed to control the displacement of the cylinder. Experiments show that the robot can realize proximal joints training and has good position tracking accuracy (tracking error is within 2 mm) with smoothness under the proposed control strategy, which can guarantee the accuracy and comfort of passive rehabilitation training, contributing to restoring the function of the impairment upper limbs.

Post-Impact Motion Planning and Tracking Control for Autonomous Vehicles

There is an increasing awareness of the need to reduce traffic accidents and fatality due to vehicle collision. Post-impact hazards can be more serious as the driver may fail to maintain effective control after collisions. To avoid subsequent crash events and to stabilize the vehicle, this paper proposes a post-impact motion planning and stability control method for autonomous vehicles. An enabling motion planning method is proposed for post-impact situations by combining the polynomial curve and artificial potential field while considering obstacle avoidance. A hierarchical controller that consists of an upper and a lower controller is then developed to track the planned motion. In the upper controller, a time-varying linear quadratic regulator is presented to calculate the desired generalized forces. In the lower controller, a nonlinear-optimization-based torque allocation algorithm is proposed to optimally coordinate the actuators to realize the desired generalized forces. The proposed scheme is verified under comprehensive driving scenarios through hardware-in-loop tests.

Personalized Motion Planning and Tracking Control for Autonomous Vehicles Obstacle Avoidance

A personalized motion planning and tracking control framework is proposed to prevent collisions between autonomous vehicles and obstacles ahead. The presented framework consists of a motion planning generator and a cooperative tracking controller. For motion planning, a multi-constrained numerical optimization method is used to generate the obstacle avoidance motion planning for autonomous vehicles according to the individual needs of the passengers. For tracking control, a cooperative longitudinal and lateral motion controller is designed to track the planning results. The longitudinal controller is composed of a model predictive controller, and the lateral controller is composed of a nonlinear steering controller. The planning results show that the adopted motion planning algorithm can achieve personalized motion planning according to the needs of the passengers. Simulink and CarSim co-simulation results show the excellent tracking performance of the proposed cooperative controller. The performance analysis results show that the proposed framework can improve the corresponding driving performance according to the individual needs of the passengers.

Multi-Objective Optimal Torque Control with Simultaneous Motion and Force Tracking for Hydraulic Quadruped Robots

Model-based force control for motion and force tracking faces significant challenges on real quadruped platforms due to the apparent model inaccuracies. In this paper, we present a multi-objective optimal torque control for hydraulic quadruped robots under significant model errors, such as non-modelable hydraulic components, linearization, disturbances, etc. More specifically, the centroidal dynamics are first modeled to project the dynamics of the floating-based whole-body behaviors to the centroidal frame. Model error compensation mechanisms are subsequently developed to track the reference motion of the CoM, torso, and foot-end trajectories, which are mapped into the joint space. Furthermore, a multi-objective optimal torque control scheme is formulated using quadratic programming (QP) to coordinate follow the reference motion and ground reaction forces simultaneously while satisfying all constraints. Finally, we present a series of simulations as well as experiments on a real hydraulic quadruped platform, EHbot. The results demonstrate that the proposed torque control scheme is robust to large model inaccuracies and improves the performance of the overall system.

Feedback tracking control of optimal reference trajectories for spacecraft relative motion

In this paper, first the optimal trajectories are defined by a fixed end-time, continuous-thrust trajectory which minimizes a terminal cost at the end of an inter-planetary trajectory by appending the multi-body dynamic equations defining the motion by a set of Lagrange multipliers or co-states. The solution of these co-states and the optimal trajectories for a fixed end-time are obtained by solving a two-point boundary value problem along with a steering control law, which is used to provide a reference trajectory. Inspired by the general expression for the gravitation potential and the Q-law, a tracking control law is designed to track the reference trajectory. As the steering controller is not a feedback controller, a feedback tracking control law is proposed to continuously correct the spacecraft's actual relative motion trajectory so it tracks the reference relative motion trajectory. The nonlinear relative motion tracking control law is reduced to a linear feedback control law, when the spacecraft is sufficiently close to the reference relative motion trajectory. The feedback laws demonstrate the feasibility of developing practical tracking laws for continuous trajectory correction manoeuvre control of spacecraft relative motion, for implementing low-thrust inter-planetary trajectory following control in electrically propelled spacecraft, in practice.

Robust Output-Feedback Control for Spacecraft Proximity Operations With Forbidden Zone

This article seeks to address the robust output-feedback motion tracking control problem for spacecraft close-range proximity maneuvers under the influence of forbidden zone, external disturbances, and input saturation. First, two superquadric cuboids are constructed in the body-fixed frame of the target to represent the forbidden zone in final proximity. Then, an adaptive output-feedback control scheme is proposed under the framework of extended state observer technique and sliding mode control methodology. The presented scheme can achieve concurrent position and attitude tracking without prior information of the velocity-level states, and avoid any unwanted collisions between the two spacecraft. Finally, simulation scenarios are investigated to show the effectiveness of the proposed control scheme.

On Active Vibration Absorption in Motion Control of a Quadrotor UAV

Conventional dynamic vibration absorbers are physical control devices designed to be coupled to flexible mechanical structures to be protected against undesirable forced vibrations. In this article, an approach to extend the capabilities of forced vibration suppression of the dynamic vibration absorbers into desired motion trajectory tracking control algorithms for a four-rotor unmanned aerial vehicle (UAV) is introduced. Nevertheless, additional physical control devices for mechanical vibration absorption are unnecessary in the proposed motion profile reference tracking control design perspective. A new dynamic control design approach for efficient tracking of desired motion profiles as well as for simultaneous active harmonic vibration absorption for a quadrotor helicopter is then proposed. In contrast to other control design methods, the presented motion tracking control scheme is based on the synthesis of multiple virtual (nonphysical) dynamic vibration absorbers. The mathematical structure of these physical mechanical devices, known as dynamic vibration absorbers, is properly exploited and extended for control synthesis for underactuated multiple-input multiple-output four-rotor nonlinear aerial dynamic systems. In this fashion, additional capabilities of active suppression of vibrating forces and torques can be achieved in specified motion directions on four-rotor helicopters. Moreover, since the dynamic vibration absorbers are designed to be virtual, these can be directly tuned for diverse operating conditions. In the present study, it is thus demonstrated that the mathematical structure of physical mechanical vibration absorbers can be extended for the design of active vibration control schemes for desired motion trajectory tracking tasks on four-rotor aerial vehicles subjected to adverse harmonic disturbances. The effectiveness of the presented novel design perspective of virtual dynamic vibration absorption schemes is proved by analytical and numerical results. Several operating case studies to stress the advantages to extend the undesirable vibration attenuation capabilities of the dynamic vibration absorbers into trajectory tracking control algorithms for nonlinear four-rotor helicopter systems are presented.

Velocity Obstacle-Based Collision Avoidance and Motion Planning Framework for Connected and Automated Vehicles

This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.

Motion planning and tracking control of unmanned underwater vehicles: technologies, challenges and prospects

The motion planning and tracking control techniques of unmanned underwater vehicles (UUV) are fundamentally significant for efficient and robust UUV navigation, which is crucial for underwater rescue, facility maintenance, marine resource exploration, aquatic recreation, etc. Studies on UUV motion planning and tracking control have been growing rapidly worldwide, which are usually sorted into the following topics: task assignment of the multi-UUV system, UUV path planning, and UUV trajectory tracking. This paper provides a comprehensive review of conventional and intelligent technologies for motion planning and tracking control of UUVs. Analysis of the benefits and drawbacks of these various methodologies in literature is presented. In addition, the challenges and prospects of UUV motion planning and tracking control are provided as possible developments for future research.

Excavator Posture Estimation and Position Tracking System Based on Kinematics and Sensor Network to Control Mist-Spraying Robot

In the construction field, when an excavator dismantles a building using an attachment, a large amount of fine dust is generated, causing air pollution. In this study, a mist-spraying robot was proposed, aiming to effectively suppress the generation of fine dust in the construction field. The robot could spray mist while motion tracking the attachment of the excavator. The mist-spraying robot recognized and tracked the source of the fine dust by estimating the posture and position of the excavator. Forward kinematics was used to estimate the posture of the excavator, and the position was estimated using a real-time kinematic global positioning system (RTK-GPS). The fine dust source motion tracking control performance of the mist-spraying robot was verified by measuring the error when the excavator attachment moved horizontally, vertically, and helically. The mean distance between the spraying direction of the mist-spraying robot and the attachment of the excavator was within 0.4 m. Considering the range in which mist is ordinarily sprayed, this mist-spraying robot is expected to successfully track and suppress the generation of fine dust.

Adaptive sliding mode control with hysteresis compensation-based neuroevolution for motion tracking of piezoelectric actuator

In this paper, an adaptive sliding mode control with hysteresis compensation-based neuroevolution (ACNE) is proposed for precise motion tracking of the piezoelectric actuator (PEA) in the presence of uncertainties, disturbances, and nonlinearity hysteresis characteristics. Firstly, a new memetic differential evolution (MeDE) algorithm is proposed to optimize the weights of a 3-layer neural network (called neuroevolution or NE). In MeDE, a differential evolution algorithm is used as a global search scheme and the Jaya algorithm is used as local search exploitation. Secondly, an inverse hysteresis model of PEA is identified by the neuroevolution model to provide a feed-forward NE control signal to compensate for the hysteresis behavior of PEA system. Thirdly, an adaptive neural sliding mode control plus feedforward NE (ACNE) control is designed to enhance the quality control and guarantee asymptotical stability for PEA system. Based on the Lyapunov method, the stability of the closed-loop system is analyzed and proved. Finally, the experimental Thorlabs piezoelectric actuator (PEA) is set up to verify the robustness and effectiveness of the proposed approach. Results show that the identified MeDE-Neuroevolution model has successfully applied to model the inverse hysteretic of PEA system and the performance of MeDE has better than Jaya, DE, and PSO in terms of best, worst, average, and standard deviation. Furthermore, in motion tracking control, the performance of proposed ACNE control has more accurate than a classical PID control, a feedforward control, a hybrid feedback–feedforward control, and an adaptive neural sliding mode control without a compensator.

Robust Hybrid Control Using Recurrent Wavelet- Neural-Network Sliding-Mode Controller for Two- Axis Motion Control System

In this paper, a robust hybrid control system (RHCS) for achieving high precision motion tracking performance of a two-axis motion control system is proposed. The proposed AHCS incorporating a recurrent wavelet-neuralnetwork controller (RWNNC) and a sliding-mode controller (SMC) to construct a RRWNNSMC. The two-axis motion control system is an x-y table of a computer numerical control machine that is driven by two field-oriented controlled permanent-magnet synchronous motors (PMSMs) servo drives. The RWNNC is used as the main motion tracking controller to mimic a perfect computed torque control law and the SMC controller is designed with adaptive bound estimation algorithm to compensate for the approximation error between the RWNNC and the ideal controller. The on-line learning algorithms of the connective weights, translations and dilations of the RWNNC are derived using Lyapunov stability analysis. A computer simulation and an experimental are developed to validate the effectiveness of the proposed RHCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results using star and four leaves contours are provided to show the effectiveness of the RHCS. The motion tracking performance is significantly improved using the proposed RHCS and robustness to parameter variations, external disturbances, cross-coupled interference and frictional torque can be obtained as well for the two-axis motion control system.

Design and motion control of an under-actuated snake arm maintainer

AbstractThis study presents an under-actuated snake arm maintainer (SAM) for complex and extreme environments such as nuclear power plants. The structure adopts the layered cable drive principle, whereby a single drive layer drives multiple joints. This design significantly reduces the complexity of the control system while increasing the spatial curvature. The traction of multiple wire ropes with a composite capstan drives the synchronous angular motion of several adjacent joints. By changing the number of joints in the single driver layer of the snake arm, the arm can be adapted to various complex environments. The trajectory planning and trajectory tracking motion control methods of the under-actuated SAM are established based on the improved backbone method and the variable rod length algorithm. Finally, a 10-joint prototype with an arm length of 2300 mm is designed for nuclear reactor maintenance. Trajectory experiments confirmed the rationality of the under-actuated SAM, the correctness of the inverse kinematics, and the effectiveness of the motion control methods.

Adaptive Robust Motion Control of Quadrotor Systems Using Artificial Neural Networks and Particle Swarm Optimization

Most of the mechanical dynamic systems are subjected to parametric uncertainty, unmodeled dynamics, and undesired external vibrating disturbances while are motion controlled. In this regard, new adaptive and robust, advanced control theories have been developed to efficiently regulate the motion trajectories of these dynamic systems while dealing with several kinds of variable disturbances. In this work, a novel adaptive robust neural control design approach for efficient motion trajectory tracking control tasks for a considerably disturbed non-linear under-actuated quadrotor system is introduced. Self-adaptive disturbance signal modeling based on Taylor-series expansions to handle dynamic uncertainty is adopted. Dynamic compensators of planned motion tracking errors are then used for designing a baseline controller with adaptive capabilities provided by three layers B-spline artificial neural networks (Bs-ANN). In the presented adaptive robust control scheme, measurements of position signals are only required. Moreover, real-time accurate estimation of time-varying disturbances and time derivatives of error signals are unnecessary. Integral reconstructors of velocity error signals are properly integrated in the output error signal feedback control scheme. In addition, the appropriate combination of several mathematical tools, such as particle swarm optimization (PSO), Bézier polynomials, artificial neural networks, and Taylor-series expansions, are advantageously exploited in the proposed control design perspective. In this fashion, the present contribution introduces a new adaptive desired motion tracking control solution based on B-spline neural networks, along with dynamic tracking error compensators for quadrotor non-linear systems. Several numeric experiments were performed to assess and highlight the effectiveness of the adaptive robust motion tracking control for a quadrotor unmanned aerial vehicle while subjected to undesired vibrating disturbances. Experiments include important scenarios that commonly face the quadrotors as path and trajectory tracking, take-off and landing, variations of the quadrotor nominal mass and basic navigation. Obtained results evidence a satisfactory quadrotor motion control while acceptable attenuation levels of vibrating disturbances are exhibited.

Command filtered adaptive backstepping control for high-accuracy motion tracking of hydraulic systems with extended state observer

High-accuracy motion tracking of hydraulic systems is of great significance in industrial applications. Nevertheless, dynamic nonlinearity, modeling uncertainty, generalized disturbance, and measurement noise are inevitably existed in hydraulic systems, which severely deteriorates the practical control performance. Aimed at enhancing the motion-tracking accuracy of hydraulic systems, a novel command filtered adaptive backstepping controller with extended state observer is proposed in this article. On the basis of the established system’s nonlinear model, the extended state observer utilizing only position output feedback information is first designed to estimate the system’s unmeasurable states, and time-varying disturbances of the hydraulic system are also obtained for subsequent active disturbance compensation. Next, a second-order command filter is constructed to generate specific command signals and their derivatives, which significantly simplifies the controller design process by avoiding complicated analytical differential calculations in contrast to traditional adaptive backstepping algorithm. Subsequently, with consideration of system’s nonlinearity, parametric uncertainty, and time-varying disturbance, the developed extended state observer and command filter are introduced into the adaptive backstepping design process of the proposed controller, and theoretical stability of the proposed controller is guaranteed via Lyapunov analysis. Finally, the effectiveness and superiority of the proposed controller are demonstrated by comparative experimental results.

Synchronization Control of a Dual-Cylinder Lifting Gantry of Segment Erector in Shield Tunneling Machine under Unbalance Loads

Segment assembling is one of the principle processes during tunnel construction using shield tunneling machines. The segment erector is a robotic manipulator powered by a hydraulic system to assemble prefabricated concrete segments onto the excavated tunnel surface. Nowadays, automation of the segment erector has become one of the definite developing trends to further improve the efficiency and safety during construction; thus, closed-loop motion control is an essential technology. Within the segment erector, the lifting gantry is driven by dual cylinders to lift heavy segments in the radial direction. Different from the dual-cylinder mechanism used in other machines such as forklifts, the lifting gantry usually works at an inclined angle, leading to unbalanced loads on the two sides. Although strong guide rails are applied to ensure synchronization, the gantry still occasionally suffers from chattering, “pull-and-drag”, or even being stuck in practice. Therefore, precise motion tracking control as well as high-level synchronization of the dual cylinders have become essential for the lifting gantry. In this study, a complete dynamics model of the dual-cylinder lifting gantry is constructed, considering the linear motion as well as the additional rotational motion of the crossbeam, which reveals the essence of poor synchronization. Then, a two-level synchronization control scheme is synthesized. The thrust allocation is designed to coordinate the dual cylinders and keep the rotational angle of the crossbeam within a small range. The motion tracking controller is designed based on the adaptive robust control theory to guarantee the linear motion tracking precision. The theoretical performance is analyzed with corresponding proof. Finally, comparative simulations are conducted and the results show that the proposed scheme achieves high-precision motion tracking performance and simultaneous high-level synchronization of dual cylinders under unbalanced loads.

Unified Motion/Force/Impedance Control for Manipulators in Unknown Contact Environments Based on Robust Model-Reaching Approach

With the developments of intelligent and autonomous robotic technology, robots are usually designed to confront sophisticated tasks such as automated assembly, which requires both high-speed positioning capabilities and compliance with unknown contact environments. As we know, a high-performance motion tracking control in free space can achieve efficient and accurate positioning, while impedance or force control shows superior performance in terms of sensitive force and compliance with unknown contact environments. However, it is still challenging to achieve both high-performance motion tracking and compliance within one single control framework, especially in unknown contact environments. To this end, in this article, a unified motion/force/impedance approach for unknown contact environments is proposed by robust model-reaching control with dynamic trajectory adaptation. Specifically, the overall control scheme includes two loops: the outer loop replans the trajectories of motion and force in real time to meet the environmental constraints, which are estimated by the recursive least squares estimation law; in the inner loop, the robust model-reaching control law is proposed to realize a target model, which is designed to establish a dynamic relationship between the motion and force tracking errors of the replanned trajectories. Then, by changing the matrices of the target model, the compromise between motion tracking and force tracking can be achieved, as well as different control objectives. Experiments are conducted on a seven-degree-of-freedom manipulator to validate the advantages of the proposed scheme.

Motion Capture and Intelligent Correction Method of Badminton Movement Based on Machine Vision

In recent years, badminton has become more and more popular in national fitness programs. Amateur badminton clubs have been established all over the country, and amateur badminton events at all levels have increased significantly. Due to the lack of correct medical supervision and health guidance, many people have varying degrees of injury during sports. Therefore, it is very important to study the method of badminton movement capture and intelligent correction based on machine vision to provide safe and effective exercise plan for amateur badminton enthusiasts. This article aims to study the methods of motion capture and intelligent correction of badminton. Aiming at the shortcoming of the mean shift algorithm that it is easy to lose the target when the target is occluded or the background is disturbed, this paper combines the mean shift algorithm with the Kalman filter algorithm and proposes an improvement to the combined algorithm. The improved algorithm is added to the calculation of the average speed of the target, which can be used as the target speed when the target is occluded to predict the area where the target may appear at the next moment, and it can also be used as a judgment condition for whether the target is interfered by the background. The improved algorithm combines the macroscopic motion information of the target, can overcome the problem of target loss when the target is occluded and background interference, and improves the robustness of target tracking. Using LabVIEW development environment to write the system software of the Japanese standard tracking robot, the experiment verified the rationality and correctness of the improved target tracking algorithm and motion control method, which can meet the real-time performance of moving target tracking. Experimental results show that 83% of amateur badminton players have problems with asymmetric functions and weak links. Based on machine vision technology, it can provide reliable bottom line reference for making training plans, effectively improve the quality of action, improve the efficiency of action, and promote the development of sports competitive level.