A dynamic modeling and trajectory tracking control method for dual-arm mechanism for cabin docking

In order to further reduce the incisions of laparoscopic surgery and the possibility of infection, the organic combination of single-incision laparoscopic surgery (SILS) and robotics has made the degree of minimally invasive surgery further improved. A new 5-DOF single-incision laparoscopic surgery robot was designed based on Axiomatic Design Theory, whose structure consisting of the movement mechanism, the endoscope and the position and pose adjustment mechanism. The robot parts are connected in series and parallel, allowing a pivotal motion of the endoscope in the center of the robot for realizing the incision. In order to achieve a performance optimization and a dynamic control of the single-incision laparoscopic surgical robot, the kinematics and dynamic modeling and dynamic stiffness analysis of the robot are especially important. The forward kinematics equation, inverse kinematics equation and Jacobian matrix of the SILS robot are derived based on D-H method and geometric method, and the kinematics numerical simulation is carried out by Matlab. The dynamic equation of the robot is derived by Kane method. Subsequently, a numerical simulation of the robot dynamics equation is performed, with its virtual prototype utilized to set the motion plan of the robot mechanism. After robot’s dynamic simulation, the numerical changes of the driving force and torque for each robot’s moving mechanism are obtained. The performed simulation results further verify the correctness of the established robot’s dynamic model. Finally, utilizing the above methods, the dynamic stiffness model and evaluation index of the robot are determined, and the dynamic stiffness of the robot is analyzed and evaluated. The results of the kinematics, dynamic and stiffness analysis of the SILS robot further validate that the 5-DOF SILS robot has a reasonable structure, motion and sufficient stability to meet the needs of single-incision laparoscopic surgery.

The key attributes of Two Wheeled Balancing Mobile Robots (TWBMRs) are nonholonomic constraints and inherent instability. This paper deals with the problem of balancing and trajectory tracking of TWBMR using backstepping Sliding Mode Controller (SMC). First, the mathematical representation of TWBMR is derived using Lagrangian method by incorporating the dynamics of DC motors. Then, a decoupling approach is applied for simplifying the dynamic equations. The backstepping SMC technique is finally adopted to achieve the balancing and trajectory tracking of the TWBMR, whereas both model uncertainties and exogenous disturbance are taken into account in the controller design methodology. In order to determine the velocity, the trajectory tracking is achieved by the kinematic control, which is a common backstepping controller. For the velocity convergence of TWBMR to the generated desired value, two SMCs are designed, in which the motors voltage are directly controlled as the control laws. Simplicity in practical implementation and control law, ability to overcome uncertainties and appropriate performance are the main advantages of the proposed controller. The effectiveness of the proposed controller is verified through simulation and experimental results.

Dynamically modeling the flexible characteristics of large-scale jointed composite spacecraft is challenging. In this study, a dynamic modeling method for rigid–flexible composite spacecraft is proposed based on the absolute nodal coordinate formulation (ANCF). First, the spacecraft in the jointed composite is simplified as a rigid body, and the docking mechanisms between spacecraft are approximated using the fully parameterized beam model. Next, regarding the constraints between the beam and the rigid body, the beam’s absolute nodal coordinates are converted into rigid body coordinates. This allows the dynamic equations to be simplified using independent coordinates, reducing the model dimension. Finally, system damping is increased through the mean stress noise reduction method, which suppresses high-frequency components in the dynamic model and further reduces the rigidity of the dynamic equations for the composite body. This modeling method decreases the complexity of the composite body dynamics and avoids the difficulty of solving algebraic–differential equations exhibited by Lagrange multiplier methods, facilitating numerical simulations. The proposed method is applicable to both tree and mesh topologies. MATLAB simulations demonstrate that the proposed dynamic model alleviates the dimensionality disaster caused by conventional algorithms, significantly reducing computation time. The simulation results are consistent with ADAMS. The proposed model exhibits displacement errors less than 1 mm, highlighting its efficiency and accuracy.

Lower-limb exoskeleton robots play a pivotal role in rehabilitation medicine and assistive augmentation, where precise dynamic modelling and trajectory tracking control are fundamental to effective assistance. Existing models predominantly focus on hip and knee rotational degrees of freedom, with insufficient attention to ankle dynamics and pelvic translation. To address these limitations, this paper establishes a sagittal-plane dynamic model comprising nine generalised coordinates, treating the human lower limb and exoskeleton as an integrated coupled system. A seven-segment kinematic model encompassing the trunk, bilateral thighs, shanks, and feet is constructed via a modified Denavit-Hartenberg parameter method, and dynamic equations are derived using Lagrangian formulation. Three control strategies-PD control, PD with gravity compensation, and the computed torque method-are designed and evaluated through simulations using gait data from five subjects (two self-collected, three from a public dataset) acquired via Vicon motion capture. Results demonstrate that the computed torque method achieves a joint angle tracking root mean square error (RMSE) of 0.59°, representing an 86.3% improvement over conventional PD control, while maintaining a low control torque RMS of 4.44 N·m. The controller exhibits stable tracking performance across walking speeds of 0.4-1.45 m/s, validating the effectiveness of the proposed model and control strategies.

Neural network approximation-based backstepping sliding mode control for spacecraft with input saturation and dynamics uncertainty

The paper introduces an adaptive strategy to effectively control a nonlinear dual-arm robot under external disturbances and uncertainties. By the use of the backstepping sliding mode control (BSSMC) method, the proposed algorithm first allows the manipulators to be able to robustly track the desired trajectories. Furthermore, due to the nonlinear, uncertain and unmodeled dynamics of the dual-arm robot, it is proposed to employ the radial basis function network (RBFN) to adaptively estimate the robot’s dynamic model. Though the estimation of the dynamics is approximate, the adaptation law is derived from the Lyapunov theory, which provides the controller with ability to guarantee stability of the whole system in spite of its nonlinearities, parameter uncertainties and external load variations. The effectiveness of the proposed RBFN–BSSMC approach is demonstrated by implementation in a simulation environment with realistic parameters, where the obtained results are highly promising.

The industrial robot is a highly nonlinear and highly coupled device, and the calculation of the dynamic model needs to spend a lot of time, which result in difficult to achieve dynamic control. Using of intelligent algorithm to model the robot dynamics has been the field of robotics research focus. This paper proposes a method based on support vector machine regression algorithm to model the robot dynamics. The angle acceleration coefficient, and the centripetal acceleration and Coriolis acceleration coefficient, and the gravity in the robot dynamics equation are predicted based on support vector machine regression. When the support vector machine is trained, parameters in the robot dynamics equation don’t rely on the position of each joint any more. Modeling process was described in detail by using a two degree of freedom robot. All the parameters are simulated. Simulation results show that the method has the characteristic of high precision, and short training time.

Cost reduction on system tests with hardware in the loop during qualification phase of the onboard attitude control software imply in increasingly simulation realism. Attitude simulation software not only has to mimic the dynamical behavior of the spacecraft, but also shall be easily configurable, to adapt to different orbit, attitude pointing requirements, environment disturbances, sensors, actuators, and to run in real-time among other minor requirements. As the requirements for accuracy and stability for attitude pointing has increased in many modern missions, so do the complexity of the simulation algorithms and necessity for dynamical realism. Rigid body dynamics can be considered excessively simple to model a satellite with several reaction wheels, non-rigid structures, unbalanced solar arrays, fuel and liquid sloshing, not to mention crew motion and vehicle docking. This work presents the efforts being carried out to fulfill the requirements of the Brazilian space missions, nominally Multi Mission Platform, with a simulation environment capable to accomplish from early definition and mission analysis phase up to acceptance tests of the attitude control software. Development is being conducted in order to achieve a high degree of realism and range of applicability of the simulation software, compatible with the expected cost reduction of the simulation hardware (ordinary PCs). The kinematics and dynamic models to support AOCS (Attitude and Orbit Control Subsystem) simulation and testing with or without hardware-in-the-loop for Brazilian space missions are presented. Normally the dynamic equations are expressed in angular momentum components, due to the high complexity of these equations when derived in terms of the angular velocity. This, of course, is particularly important in satellites composed by several articulated rigid bodies as solar panels, robotic arms, space booms, deployable antennas, etc. Nevertheless, the inverse of the inertia matrix shall be calculated on the run, since it varies in time. This can be somewhat slow, mainly considering the high degree of freedom in dynamics of satellites with articulated panels and reaction wheels. In this case a more complex but fast set of equations in terms of the angular velocity is the best choice. This work describes the attitude dynamic equations expressed in angular velocities for satellites with appendages (solar panels, telescopes, directional antennas, etc.) and reaction wheels. They can be easily extended, however, to include nutation dampers and robotic arms. As usual, this formulation requires the knowledge of the acting torque in the connection joint, which is not always understood, modeled or known. So the dynamic model presented in this work uses the angular acceleration instead of the torque at the joint, which simplifies and reduces the order of the differential equations. Simulation results are presented, emphasizing the difference between the behavior of rigid body and articulated rigid bodies.

English

This paper focuses on the robust control of a coaxial eight-rotor UAV in the presence of model uncertainties and external disturbances. The dynamical and kinematical model of the eight-rotor with high drive capability is established. On account of the uncertainties, a robust back-stepping sliding mode control (BSMC) with self-recurrent wavelet neural network (SRWNN) method is proposed as the attitude controller of the eight-rotor. SRWNN as the uncertainty observer can effectively estimate the lumped uncertainties. All weights of SRWNN can be trained online by the adaptation laws based on Lyapunov stability theorem. Then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the eight-rotor under model uncertainties and external disturbances.

This paper presents the dynamics analysis of laparoscope arm for Minimally Invasive Surgical Robot(MISR), and the inverse dynamics model of the laparoscope arm is established according to the general equation of multi-body dynamics and Lagrange equations. Complete virtual prototype model of laparoscope arm is established in Adams. The theoretical driving torques/force of the dynamic model are got by comparing the dynamic simulation results in Adams, the dynamic model of the laparoscope arm is demonstrated; According to the actual operative track of laparoscope, intermittent motion is added to the end of laparoscope arm to simulate the real movement. Considering the influence of the trocar-force on dynamic characteristics, a complete dynamical model is established and the influence of the trocar-force on the joints of the robotic arm is analyzed. Lastly, force sensing strategy for the force on trocar point is performed. The dynamical model and analysis are presented to provide the basis of the robotic control.

Robots are used to provide help in unsafe, repetitive and dreary situations. This paper concentrates on a robotic arm which can be utilized for crossing of the dead wire structure to the jumper cable of an electrical transmission line. In order to achieve a good positional accuracy of the end-effector, the robotic arm is modeled and analyzed. Denavit-Hartenberg (DH) representation is used for the kinematic modeling and Lagrangian-Euler (LE) method for the dynamic modeling. Since the kinematic and dynamic modeling of robotic arm is highly nonlinear in nature, the optimum trajectory planning is a challenging issue in almost all obstacle avoidance problems. In trajectory planning, the desired trajectory needs to be followed for end-effector positioning. This paper presents kinematic modeling, dynamic modeling, workspace analysis and trajectory planning of a 5 Degree of freedom (DOF) robotic arm. The robotic arm is designed using SolidWorks 2014 software. The complete analysis of the proposed manipulator is simulated using MATLAB Software.

The nonlinear trajectory tracking control problem is studied for a flying-wing aircraft. Starting from a nonlinear dynamics model of the flying-wing aircraft, the trajectory tracking control is decomposed into multiple loops of position control, flight path control, and attitude control. An incremental backstepping sliding mode control is proposed to implement attitude control, while an incremental nonlinear dynamic inversion and a nonlinear dynamic inversion design are used to deal with the nonlinear system model for the flight path and position control, respectively. In addition, a radial basis function neural-network-based extended state disturbance observer is proposed to deal with model uncertainties, gust disturbances, and unknown faults of the aircraft. The closed-loop control system is proved to be stable using Lyapunov theory. The performance of the proposed disturbance-observer-based incremental backstepping sliding mode control is demonstrated in simulation through a set of three-dimensional tracking scenarios. Compared with both backstepping control and backstepping sliding mode control, tracking performance measured by settling time, tracking error, and overshoot are improved by the proposed design when realistic trajectory tracking missions are executed.

Today, with the increasing growth in road traffic, many countries are welcoming long articulated vehicles because of their economic and environmental benefits and the positive effects on the problem of traffic congestion and the reduction in fuel consumption and environmental pollutants. The major problem with such vehicles is poor maneuverability at low speeds and inappropriate lateral performance at high speeds, resulting in accidents and financial losses. Therefore, in order to improve their safety, they need a control system that can improve the performance of the long articulated vehicles. In this article, a 19-degree of freedom dynamic model of the long articulated vehicle has been developed in MATLAB software. This vehicle consists of a tractor and two semi-trailer units. To adjust the articulated vehicle lateral dynamics, a robust control method based on the combination of active disturbance rejection control and back-stepping sliding mode control is introduced. Four control variables such as yaw rate and lateral velocity of the tractor and also first and second articulation angles are regulated by steering the axles of the tractor and two trailers. Furthermore, in order to measure the state variables of the long articulated vehicle, the extended Kalman filter is used. The results of the simulation in high-speed lane change and low-speed steep steer maneuvers indicate the superiority of this method over linear-quadratic regulator and sliding mode controllers. Finally, the robustness of this controller than conventional sliding mode and active disturbance rejection sliding mode controllers have been shown in the presence of noises.

In this brief, finite-time backstepping sliding mode control technique is investigated to handle with the yaw control of non-affine nonlinear unmanned aerial vehicle (UAV) helicopters with actuator faults and unknown disturbances. Using a taylor series expansion technique, the control input in non-affine nonlinear system is presented as an explicit expression, which is convenient for controller design. Based on it, nonlinear disturbance observer is designed to estimate unknown compound disturbance. Furthermore, finite-time convergence backstepping sliding mode controller is designed to force tracking errors to small bounded sets in finite time, in which dynamic surface control avoids explosion of complexity and the sliding mode variable enhances system robustness. Then, the Lyapunov function is employed to verify the stability of the control system. Finally, the validity of proposed scheme adopted in yaw-channel dynamic model of UAV helicopters under actuator faults is demonstrated via simulation results.