Trajectory Tracking Experiments Research Articles

A Lightweight and Affordable Wearable Haptic Controller for Robot-Assisted Microsurgery.

In robot-assisted microsurgery (RAMS), surgeons often face the challenge of operating with minimal feedback, particularly lacking in haptic feedback. However, most traditional desktop haptic devices have restricted operational areas and limited dexterity. This report describes a novel, lightweight, and low-budget wearable haptic controller for teleoperated microsurgical robotic systems. We designed a wearable haptic interface entirely made using off-the-shelf material-PolyJet Photopolymer, fabricated using liquid and solid hybrid 3D co-printing technology. This interface was designed to resemble human soft tissues and can be wrapped around the fingertips, offering direct contact feedback to the operator. We also demonstrated that the device can be easily integrated with our motion tracking system for remote microsurgery. Two motion tracking methods, marker-based and marker-less, were compared in trajectory-tracking experiments at different depths to find the most effective motion tracking method for our RAMS system. The results indicate that within the 4 to 8 cm tracking range, the marker-based method achieved exceptional detection rates. Furthermore, the performance of three fusion algorithms was compared to establish the unscented Kalman filter as the most accurate and reliable. The effectiveness of the wearable haptic controller was evaluated through user studies focusing on the usefulness of haptic feedback. The results revealed that haptic feedback significantly enhances depth perception for operators during teleoperated RAMS.

Anti-delay Kalman filter fusion algorithm for inter-vehicle sensor network with finite-step convergence

Intelligent vehicle applications in autonomous driving and obstacle avoidance commonly require the precise relative state of vehicles. Accordingly, this study focuses on the coordinate fusion of vehicle state problem experienced by an inter-vehicle sensor network with time-varying transmission delays. Using the ingeniously designed low-complexity integration with a consensus strategy and buffer technology, an anti-delay distributed Kalman filter (DKF) with finite-step convergence is proposed. By introducing an appropriate weight matrix to assess local estimates, the optimal fusion state result is available as a linear minimum variance. In addition, to accommodate practical engineering in intelligent vehicles, the communication weight coefficient and directed topology with unidirectional transmission are considered. From a theoretical perspective, the proof of error covariances’ upper bounds with different communication topologies with delays are presented. Furthermore, the maximum allowable delays of inter-vehicle sensor network are derived backwards. Simulations verify that while considering the various non-ideal factors presented above, the proposed DFK algorithm produces more accurate and robust fusion estimation state results than those of the existing algorithms, making it more valuable in practical applications. Moreover, a mobile car trajectory tracking experiment is conducted, which further verifies the feasibility of the proposed algorithm.

Design of a new Robot Operating System-MATLAB-based autonomous robot system and trajectory tracking experiment

The trajectory tracking performance of an actual robot is usually used as an important standard to evaluate a nonlinear controller algorithm. The aim of this article is to design a new experimental robot system based on the Robot Operating System-MATLAB framework. To validate the performance, a nonlinear tracking controller includes the following features. Firstly, the surface robot model is in Lie algebra [Formula: see text] format, which keeps the potential of cooperation control between space vehicles. Secondly, the nonlinear controller has good robustness under unknown disturbances. Thirdly, the control algorithm focuses on the practical robot, Turtlebot2. The robot system experimental results demonstrate the effectiveness of the proposed controller for trajectory tracking under unknown disturbance. The controller’s potential applications include autonomous robots for indoor examination, extreme condition rescuing, and cooperation between unmanned aerial vehicle and unmanned surface vehicle autodrives.

Design and motion control of exoskeleton robot for paralyzed lower limb rehabilitation.

Patients suffering from limb movement disorders require more complete rehabilitation treatment, and there is a huge demand for rehabilitation exoskeleton robots. Flexible and reliable motion control of exoskeleton robots is very important for patient rehabilitation. This paper proposes a novel exoskeleton robotic system for lower limb rehabilitation. The designed lower limb rehabilitation exoskeleton robot mechanism is mainly composed of the hip joint mechanism, the knee joint mechanism and the ankle joint mechanism. The forces and motion of the exoskeleton robot were analyzed in detail to determine its design parameters. The robot control system was developed to implement closed-loop position control and trajectory planning control of each joint mechanism. Multiple experiments and tests were carried out to verify robot's performance and practicality. In the robot angular response experiments, the joint mechanism could quickly adjust to different desired angles, including 15°, 30°, 45°, and 60°. In the trajectory tracking experiments, the exoskeleton robot could complete tracking movements of typical actions such as walking, standing up, sitting down, go upstairs and go downstairs, with a maximum tracking error of ±5°. Robotic wearing tests on normal people were performed to verify the assistive effects of the lower limb rehabilitation exoskeleton at different stages. The experimental results indicated that the exoskeleton robot has excellent reliability and practicality. The application of this exoskeleton robotic system will help paralyzed patients perform some daily movements and sports.

Development of a Minimally Invasive Surgical Robot Using Self-Helix Twisted Artificial Muscles

In the field of surgery, minimally invasive surgical robots (MISRs) have emerged as the persistent pursuit of physicians to minimize harm and perform precise surgery. The structure of newly proposed MISRs tends to change form rigid to flexible, which poses a challenge to precise control. In this work, a MISR actuated by self-helix twisted artificial muscles (SHTAMs) with large bending angle and high precision is developed. The configuration and operation principle of the MISR are illustrated. Then, the physical model of SHTAM and the kinematics model of the end effector are established. The contraction ratio of SHTAM reaches 27.5%, and the output force is 5424.2 mN. Besides, the maximum bending angle (about 92°) of MISR is measured to verify the kinematic analysis. Moreover, in the point positioning control experiments, the steady-state error is less than 0.11°. Subsequently, the trajectory tracking tests on sinusoidal trajectories with different frequencies and amplitudes are carried out, and the circular trajectory tracking experiment is also performed with a maximum error of 1.8° in cold gel environment. The experimental results indicate that the proposed MISR has a large bending angle and can achieve accurate trajectory control, which exhibits excellent prospect in precision minimally invasive surgery.

A design of pneumatic-driven translational pyramidal manipulator and its actively disturbance rejection tracking control

In this study, we have undertaken the design and implementation of a pneumatic-driven translational pyramidal manipulator (PTPM) with the primary objective of achieving reliable and precise motion control, even under conditions where the PTPM may be exposed to disturbances arising from coupling effects and internal uncertainties. To achieve this purpose, the ALADRC, an adaptive controller that integrates a linear active disturbance rejection controller (LADRC) with a reduced-order linear extended state observer (RLESO) and a radial basis function neural network optimizer (RFNNO), is presented in this paper. This ALADRC has the following advantages in the view of practical applications: (1) it rejects total disturbances and coupling effect, (2) it reduces the order of the extended state observer, (3) it selects observer gains according to system frequencies, and (4) it optimizes controller gains in real time. Two trajectory tracking experiments and three disturbance rejection experiments were conducted to verify the trajectory tracking and disturbance compensation performance of the designed PTPM, respectively. The experimental results indicated that the PTPM controlled using the ALADRC increased the robustness in external disturbance rejection and provided accurate trajectory tracking.

A Novel Electromagnetic Driving System for 5-DOF Manipulation in Intraocular Microsurgery.

This work presents a novel electromagnetic driving system that consists of eight optimized electromagnets arranged in an optimal configuration and employs a control framework based on an active disturbance rejection controller (ADRC) and virtual boundary. The optimal system configuration enhances the system's compatibility with other ophthalmic surgical instruments, while also improving its capacity to generate magnetic force in the vertical direction. Besides, the optimal electromagnet parameters provide a superior comprehensive performance on magnetic field generation capacity and thermal power. Hence, the presented design achieves a stronger capacity for sustained work. Furthermore, the ADRC controller effectively monitors and further compensates the total disturbance as well as gravity to enhance the system's robustness. Meanwhile, the implementation of virtual boundaries substantially enhances interactive security via collision avoidance. The magnetic and thermal performance tests have been performed on the electromagnet to verify the design optimization. The proposed electromagnet can generate a superior magnetic field of 2.071 mT at a distance of 65 mm with an applied current of 1 A. Moreover, it demonstrates minimal temperature elevation from room temperature (25 °C) to 46 °C through natural heat dissipation in 3 h, thereby effectively supporting prolonged magnetic manipulation of intraocular microsurgery. Furthermore, trajectory tracking experiments with disturbances have been performed in a liquid environment similar to the practical ophthalmic surgery scenarios, to verify the robustness and security of the presented control framework. The maximum root mean square (RMS) error of performance tests in different operation modes remains 35.8 μm, providing stable support for intraocular microsurgery.

Whole Body Control of an Autonomous Mobile Manipulator Using Series Elastic Actuators

The flexible joint robot has superior attractive features because of its high mobility, high load ratiohigh torque fidelity, robustness for external disturbance, task adaptability, and safety. In this paper, an autonomous mobile manipulator driven by the designed series elastic actuators (SEAs) is developed. A complete dynamic model of nonholonomic mobile manipulator including a mobile platform and manipulator with joint flexibility operating simultaneously is proposed. An integrated whole body trajectory control framework is proposed for such robot to perform mobile manipulation tasks. Considering the nonholonomic and holonomic constraints in the mobile manipulation, the whole-body dynamics is formulated and reduced. To address the highly nonlinear of the dynamics and model uncertainty, a novel integral Lyapunov function (ILF)-based adaptive neural network control for task tracking under uncertainties of the flexible joint robot model is proposed. Compared with existing methods, the proposed method provides an alternative for controlling flexible joint robots. The feasibility of the proposed method is verified by the extensive trajectory tracking experiment results in our developed flexible joint manipulator.

Soft Lightweight Small-Scale Parallel Robot With High-Precision Positioning

Small-scale (from centimeters down to micrometers) parallel robots with high precision are widely utilized in various industrial and biomedical settings, while such superiorities remain challenges for soft parallel robots (SPRs). In this work, we propose an integrated design and fabrication strategy to make up a soft lightweight (3.5 g) small-scale ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi 60\times$</tex-math></inline-formula> 40 mm) parallel robot based on the dielectric elastomer actuator. Then, a hybrid model is established to describe the mapping between driving space and workspace, taking advantage of the robustness and security of the model-based method and the strong nonlinear fitting ability of the data-driven neural network method. The stiffness and workspace of the robot are analyzed. The results of trajectory tracking experiments demonstrate the accuracy and robustness of the proposed hybrid model. The average positioning error of the different trajectories is 13.4–16.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\,\mu\mathrm{m}$</tex-math></inline-formula> . Finally, the zebrafish embryo puncture experiment is carried out to show the ability of micromanipulation. This research paves a new avenue for designing and controlling high-positioning SPRs, which is expected to be applied in the micromanipulation field.

Disturbance observer–based backstepping sliding mode cascade control of 2–degrees of freedom hydraulic tunneling robot

For the 2–degrees of freedom position tracking problem of the robotized hydraulic-driven roadheader with high nonlinearities and strong uncertainties, a practical disturbance observer–based backstepping sliding cascade control method together with an adaptive compensator is proposed and investigated. The presented methodology mainly includes a continuous nonsingular fast terminal sliding mode with the backstepping technique and the power reaching law with time-varying coefficients used to achieve satisfactory performance against the multi-source disturbances, two disturbance observers used to approximately estimate the unknown dynamics in the mechanical and hydraulic subsystem, respectively. Simultaneously, a continuous robustifying term is also utilized to compensate for the residual disturbances and enhance the robustness. The presented control method doesn’t need the precise model thanks to the auxiliary disturbance observer, and can ensure fast convergence and small tracking error thanks to the backstepping sliding cascade control and the adaptive robust compensator. Based on Lyapunov theory, stability of the overall closed-loop system is proved rigorously, and asymptotically bounded tracking performance of the robotic manipulator is guaranteed. Finally, 2–degrees of freedom trajectory tracking experiments are conducted, and comparative results effectively verify the superiorities of the proposed method.

Optimized Trajectory Tracking for Robot Manipulators with Uncertain Dynamics: A Composite Position Predictive Control Approach

This study addresses the trajectory tracking control challenges of robot manipulators with uncertain dynamics. The aim is to achieve precise and smooth trajectory regulation through a novel composite position predictive control (PPC) scheme that integrates motion profile and disturbance preview techniques. First, we perform offline dynamics identification and feedforward compensation alongside a pre-defined motion profile. To handle the disturbances arising from uncertain dynamics, a super-twisting disturbance observer is designed, resulting in a dynamically compensated prediction model. Furthermore, the receding optimization operations for PPC are executed by solving an optimal solution associated with a joint angle tracking error. The combination of feedforward and feedback control improves the robot manipulator’s absolute positioning accuracy as opposed to the conventional model predictive control method, especially when dealing with uncertain dynamics. The effectiveness of the proposed control method is confirmed through trajectory tracking experiments conducted on a six-degree-of-freedom robot platform with varying end-effector loads. The experimental results demonstrate that the proposed PPC method enhances tracking accuracy by approximately 45% and 25% when compared to the traditional inverse dynamic control (IDC) and the robust IDC approaches, respectively.

Master-slave Control Strategy of Surgical Robots for Gastrointestinal Surgery

The precise construction of the master-slave control strategy is a fundamental requirement for robot-assisted surgery. In this study, we propose a master-slave control strategy for gastrointestinal surgical robots based on the natural orifice transluminal endoscopic surgery surgical robot developed by Tianjin University. The forward and inverse kinematics of the continuum mechanism at the slave manipulator are investigated. A master-slave coordinate mapping system for surgical robots is established by analyzing the kinematic characteristics of the master and slave manipulators. Additionally, an intuitive mapping algorithm and a control flowchart for the robot are developed. The proportional control and incremental control strategies tailored for natural orifice transluminal endoscopic surgery procedures are proposed based on intuitive mapping control. Finally, a master-slave trajectory tracking experiment is performed to validate the feasibility of the proposed master-slave control strategy.

Compound Control of Trajectory Errors in a Non-Resonant Piezo-Actuated Elliptical Vibration Cutting Device.

To improve the machining quality of the non-resonant elliptical vibration cutting (EVC) device, a compound control method for trajectory error compensation is proposed in this paper. Firstly, by analyzing the working principle of non-resonant EVC device and considering the elliptical trajectory error caused by piezoelectric hysteresis, a dynamic PI (Prandtl-Ishlinskii) model relating to voltage change rate and acceleration was established to describe the piezoelectric hysteresis characteristics of EVC devices. Then, the parameters of the dynamic PI model were identified by using the particle swarm optimization (PSO) algorithm. Secondly, based on the dynamic PI model, a compound control method has been proposed in which the inverse dynamic PI model is used as the feedforward controller for the dynamic hysteresis compensation, while PID (proportion integration differentiation) feedback is used to improve the control accuracy. Finally, trajectory-tracking experiments have been conducted to verify the feasibility of the proposed compound control method.

Adaptive iterative learning control and inverse compensation of macro fiber composite system with hysteresis

Macro fiber composite (MFC) actuators, which exhibit excellent flexibility and strong deformability, are widely used in aerospace exploration, flexible robotics, and other applications. However, the rate-dependent hysteresis nonlinearity of the flexible beam driven by MFCs significantly affects positioning accuracy. This study utilizes a new Hammerstein model to describe the hysteresis of an MFC system and proposes a control strategy that combines adaptive iterative learning control and inverse compensation. The adaptive iterative learning control allows the learning gain parameters to be adjusted based on the tracking error. A high gain ensures the convergence speed of iterative learning, whereas a small learning gain provides high robustness and convergence accuracy. Trajectory tracking experiments are conducted to verify the effectiveness of the proposed control strategy. Compared with the case of direct inverse compensation, after the sixth iteration, the tracking maximum absolute error, relative error, and corresponding root mean square error of the proposed control strategy reduce by 73% on average. Moreover, compared with the case of P-type iterative learning control combined with inverse compensation, the metrics above reduce by 12% on average. The proposed control strategy offers higher tracking accuracy and greater practicality in the trajectory tracking of MFC systems.

Research on workspace visual-based continuous switching sliding mode control for cable-driven parallel robots

AbstractAchieving the high-precision control of cable-driven parallel robots (CDPRs) is complex because of their structural properties. In this paper, a quintessential redundant CDPR is designed as the research subject, and a continuous switching sliding mode controller based on workspace vision is implemented to enhance the accuracy and stability of trajectory tracking. In addition, a virtual prototype of the CDPR with uncertainties is created in the simulation analysis software ADAMS, and co-simulation is performed with the control system designed in Simulink to validate the effectiveness of the proposed control strategy. Furthermore, a CDPR platform is established for trajectory tracking experiments using the visual-based position feedback method. The trajectory tracking performance with the three control schemes is then evaluated. The experimental results show that the continuous switching sliding mode control algorithm can significantly decrease trajectory tracking errors and exhibit superior trajectory tracking performance compared to the other control strategies.

Velocity observer-based robust control of piezoelectric compliant motion systems with disturbance and hysteresis estimation

This paper proposes a velocity observer-based robust control methodology for piezoelectric compliant motion systems with both unmeasurable hysteresis states and unknown disturbances. A dynamic asymmetric Bouc–Wen model is introduced to describe hysteresis behaviors of the piezoelectric compliant motion systems, a nonlinear state estimator is constructed to compensate the hysteresis nonlinearities. With the aid of the hysteresis state estimation, a velocity and disturbance observer is designed to simultaneously achieve output feedback control and disturbance rejection. Based on the estimations of the hysteresis state, the velocity, and the disturbance, a novel gain adaptation sliding mode control (SMC) structure is developed to robustly stabilize the tracking error without the requirement of velocity measurements, where the hyperbolic tangent function is employed to avoid the chattering, and the gain of the SMC is captured by an adaptive observer. By means of Lyapunov stability theory, the convergence of the hysteresis state estimation error, the velocity observation error, and the disturbance estimation error is analyzed, and the stability of such a control system is rigorously proved. Finally, the proposed control approach is applied to a piezoelectric compliant motion system without velocity sensors. The parameters of the asymmetric Bouc–Wen model are identified by using practical experimental data from the piezoelectric compliant motion system. Simulation and experimental results based on the identified model are provided to demonstrate the proposed control approach achieves excellent tracking performance with the relative error less than 0.6%. Compared with recently reported control approaches, more than 74.42% improvement on the control accuracy is confirmed in the complicated multifrequency trajectory tracking experiments.

Research on the Model Predictive Trajectory Tracking Control of Unmanned Ground Tracked Vehicles

This article summarizes the research significance and the development status of the unmanned ground tracked vehicles (UGTVs). According to the speed and steering principle of the UGTVs in plane motion, the kinematic state space equation of the vehicle is established. Based on the model predictive control (MPC), the UGTVs trajectory tracking controller is also established. After introducing the overall control system solution, based on the vehicle model, the track speed on both sides is used as the control input to solve the predicted output of the system. After constraining the control amount, the model prediction controller is established by using S function. Several simulations with different preset speeds are performed under linear conditions, and the numerical simulation with different prediction time domains and control time domains are performed under continuous curves. The test validates the effectiveness of the controller, and the effects of speed and time domain parameters on the deviation are analyzed based on the results. Based on satellite positioning technology, trajectory tracking tests of the UGTVs are carried out. After analyzing the positioning principle and common errors, the real-time kinematic technology is used to improve the positioning accuracy of the vehicle prototype. The hardware of the test platform includes a data acquisition system, a control execution system and a walking execution system, and the software part includes data processing and optimization control. The trajectory tracking experiments under different driving conditions are conducted, and the results show that the navigation tracking system can achieve good tracking performance.

An Adaptive Control Method with Disturbance Estimation for Underwater Manipulation

In this paper, an adaptive control scheme with disturbance estimation is proposed for the station keeping and trajectory tracking of an underwater vehicle-manipulator system (UVMS) in a task space. The control of UVMSs encounters time-varying and lumped disturbances introduced by the motion of the manipulator and currents. The proposed control scheme includes an observer to identify disturbances without introducing additional sensors. Using state-of-the-art UVMS control technologies, it alleviates the restriction of the feedforward control applied to the UVMS. In addition, the proposed control scheme is not designed based on the worst case. In other words, the robustness of the controller is not achieved at the price of performance, and the performance of the controller can be recovered in the absence of disturbance. Meanwhile, the controller can also realize quick responses to attenuating disturbance and closed-loop stability when encountering an unexpected disturbance. The effectiveness of the proposed control scheme was demonstrated by a series of spatial trajectory tracking experiments under various disturbances. A limitation of this study is that the proposed control scheme is specialized for the underwater manipulation of UVMSs.

Discrete Cosserat Static Model-Based Control of Soft Manipulator

In this letter, the continuous Cosserat static model is re-formulated by employing the piecewise linear strain (PLS) approach, which can obtain the analytic formula of the model to facilitate the controller design. The robust closed-loop control architecture based on the PLSCosserat static model for the soft arm around its workspace is then established. To validate the performance of the proposed model-based control scheme, the point-to-point and time-varying trajectory tracking experiments under external disturbances have been conducted on the soft manipulator actuated by cables.

Whole-Body Control of an Autonomous Mobile Manipulator Using Model Predictive Control and Adaptive Fuzzy Technique

Whole-body control (WBC) has emerged as an important framework in manipulation for mobile manipulators. However, most existing WBC frameworks require known dynamics. Considering whole-body manipulation and optimization with unknown dynamics, this article presents the WBC of a nonholonomic mobile manipulator using model predictive control (MPC) and fuzzy logic system. First, by constructing a dynamics-based feedback linearized robotic multi-input-multi-output (MIMO) system, an MPC-based WBC strategy is proposed for mobile manipulator. Such a strategy can provide the optimal control inputs with the specified optimization index and constraints. Thereafter, a primal-dual neural network effectively addresses the constrained quadratic programming (QP) problem over a finite receding horizon brought by the MPC. Then, in order to convert the intermediate control signals into the optimal control torques that can be executed by actuators, an adaptive FLS is employed to approximate the unknown dynamics. The novel elements of the current design control approach refer to the dynamics-based feedback linearized robotic MIMO system and the combination of an MPC module with an adaptive fuzzy controller. Finally, the trajectory tracking experiments performed on a mobile dual-arm robot demonstrate the effectiveness of the proposed method.