High-precision Trajectory Tracking Research Articles

Model-based Control of the Hydraulic Manipulator for the High-precision Trajectory Tracking

Model-based Control of the Hydraulic Manipulator for the High-precision Trajectory Tracking

TDE-Based Adaptive Super-Twisting Multivariable Fast Terminal Slide Mode Control for Cable-Driven Manipulators With Safety Constraint of Error

An adaptive super-twisting multivariable fast terminal sliding mode control scheme based on time delay estimation (TDE) and asymmetric error constraints are proposed to guarantee high-precision trajectory tracking control of cable-driven manipulators under complicated unknown uncertainties. The control system is constrained by joint position errors, which are asymmetric and time-varying. First, the error range of the joints is designed to ensure that the deviation of the joint position from the desired profile is not too large while ensuring the safety performance of the manipulator, and the remaining set-total system dynamics are estimated and compensated using time-delay estimation. Secondly, the design analysis of the angular false expectation using safety constraint functions allows the constraints of different cross sections to be handled in a unified system architecture; the problem of robotic arm motion trajectories with output constraints and uncertainties is addressed through rigorous analysis. The super-twisting adaptive control effectively ensures fast, accurate and robust convergence of the algorithm in satisfying all constraints in operation, and the stability of the closed-loop control system is analyzed using the Lyapunov method. Finally, the effectiveness and superiority of the proposed adaptive hyper-torsional non-singular terminal sliding mode control scheme are verified by simulations and experiments in 2-DOF.

Control of Trajectory Tracking for Mobile Manipulator Robot with Kinematic Limitations and Self-Collision Avoidance

In this paper, we propose an optimized differential evolution algorithm based on kinematic limitations and structural complexity constraints to solve the trajectory tracking problem for a mobile manipulator robot. The traditional method mainly involves obtaining the speed of the control variable based on the Jacobian inverse or linearization of the robot’s kinematic model, which cannot avoid the singularity position and/or self-collision phenomena. To address these problems, we directly design an optimized differential evolution algorithm to solve the trajectory planning problem for mobile manipulator robots. First, we analyze various constraints on the actual movement and describe them specifically using various equations or inequalities, including non-holonomic constraints on the mobile platform, the physical limitations of the driving motors, self-collision avoidance restriction, and smoothly traversing the singularity position. Next, we re-define the trajectory tracking of a mobile manipulator robot as an optimization problem under multiple constraints, including the trajectory tracking task and various constraints simultaneously. Then, we propose a new differential evolution (DE) algorithm by optimizing some critical operations to solve the optimization problem, such as improving the population’s distribution, limiting the population distribution range, and adding a success index. Additionally, we design two simple trajectories and two complex trajectories to determine the performance of the optimized DE algorithm in solving the trajectory tracking problem. The results demonstrate that the optimized DE algorithm can effectively realize the high-precision trajectory tracking task of a differential wheeled mobile manipulator robot through the consideration of kinematic limitations and self-collision avoidance.

Discrete open-closed-loop PID-type iterative learning control for trajectory tracking of tracked mobile robots

In this article, a robust discrete-time open-closed-loop proportion integral differential (PID) -type iteration learning control (ILC) algorithm is developed for the high-precision trajectory tracking control of tracked mobile robots (TMRs) with external disturbances and noises. The proposed ILC algorithm adopts the past, current, and predictive learning error items of the former and current iterations to correct the current control input variables, which finally converges to the desired trajectory through continuous iterative learning. The convergence characterization of the algorithm for TMRs under both external disturbances and noises is carried on rigorous mathematical proof. Numerical simulations and physical experiments are provided to verify the feasibility and effectiveness of the algorithm. The comparative results of two ILC algorithms indicate that the tracking performance of the proposed ILC algorithm is superior to the traditional PID-type ILC algorithm in terms of tracking accuracy and convergence rate.

High-Precision Trajectory Tracking Control of a Multifrequency Whirling Orbit Using Adaptive Feedforward Cancellation for the Efficient Experimental Estimation of Fluid Force

Abstract In recent years, there has been a demand for turbomachinery with increasing speed, pressure, and compactness owing to the increasing demand for high output and efficiency. Consequently, various shaft vibration problems have become apparent. The rotor dynamic (RD) forces are one of the causes of these axial vibrations, which can lead to instability of the rotor system. To ensure the reliability of turbomachinery, the effect of RD fluid force should be analyzed at the design stage. In this study, high-precision tracking control of a complex orbit was analyzed using an experimental approach, and a highly efficient and low-dispersion estimation of the RD fluid force was achieved. The target was a parallel annular seal, and an experimental system was developed to control the rotor position using a piezo-electric actuator. Using the adaptive feedforward cancellation (AFC), we suppressed the periodic disturbance caused by rotation, whirling, and disturbances, such as noise that existed uniformly at all frequencies. Thus, a high-precision tracking control with an orbit tracking error of approximately 1 μm was achieved. The RD fluid force was estimated from the output signal of the AFC during high-precision tracking control, and the RD coefficient was calculated using spectral analysis. Consequently, a highly accurate RD fluid force estimation with reduced dispersion was achieved. In addition, a multifrequency orbit control and RD fluid force estimation method were developed to improve the efficiency of the RD force estimation, which agreed well with the results of a single-frequency orbit.

Trajectory tracking control strategy of the gantry welding robot under the influence of uncertain factors

In this paper, aiming at the problem of high-precision trajectory tracking of the gantry welding robot system under the influence of uncertain factors, a composite adaptive fuzzy compensation controller based on the computed torque control strategy has been proposed. The controller is composed of an adaptive fuzzy feedback control strategy and a dead zone adaptive fuzzy control strategy, which realizes high-precision trajectory tracking of the gantry welding robot, improves the solving speed of the control algorithm, and its tracking errors has been reduced. Screw theory and Lie group lie algebra are used to solve the problem of high algorithm complexity of controllers in multi-degree-of-freedom robot systems due to Newton-Euler dynamics modeling. Based on the computed torque control, the adaptive fuzzy feedback control strategy is adopted to compensate for the modeling errors and external disturbances in the gantry welding robot system, and the dead zone adaptive fuzzy control strategy is designed to compensate for the nonlinear dead zone structure with unknown parameters. The Lyapunov equation is introduced to prove the stability of the controller. Finally, the controller designed in this paper is compared with the conventional controller through simulation and experiment on the gantry welding robot platform, which verifies the effectiveness and superiority of the controller.

A PI Control Method with HGSO Parameter Regulator for Trajectory Planning of 9-DOF Redundant Manipulator.

In order to solve the tracking accuracy problem of the redundant manipulator, a PI control method with Henry gas solubility optimization parameter regulator (PI-HGSO) is proposed in this paper. This method consists of the controller and the parameter regulator. The characteristic is that the position deviation of a manipulator is equivalent to a specific function; namely, the proportional-integral (PI) controller is used to adjust the deviation input. The error can be better corrected by the processing of the PI controller so that the inverse kinematics solution of the minimum error can be realized. At the same time, the parameter selection of PI controllers has always been a difficulty in controller design. To address the problem, Henry gas solubility optimization (HGSO) is selected as a parameter regulator to optimize the parameters and obtain the optimal controller, thereby achieving high-precision trajectory tracking. Experiments on 9-DOF redundant manipulator show that our method achieves competitive tracking accuracy in contrast with others. Meanwhile, the efficiency and accuracy of the PI controller are greatly guaranteed by using HGSO to automatically optimize controller parameters instead of making approximate adjustments through infinite manual trial and error. Therefore, the feasibility and competitive superiority of PI-HGSO is fully proved in trajectory planning of redundant manipulators.

Gain-Scheduled Sliding-Mode-Type Iterative Learning Control Design for Mechanical Systems

In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on the SM variable, the proposed controller is synthesized involving a feedback regulation item, a feedforward learning item, and a robust switching item. The feedback regulation item is adopted to regulate the position and velocity tracking errors, the feedforward learning item is applied to handle the model uncertainties and repetitive disturbance, and the robust switching item is introduced to compensate the nonrepetitive disturbance and linearization residual error. Moreover, the gain-scheduled mechanism is employed for both the feedback regulation item and feedforward learning item to enhance the convergence speed. Convergence analysis illustrates that the position and velocity tracking errors can eventually regulate to zero under the proposed controller. By combining the advantages of both SM control and IL control, the proposed controller has strong robustness against model uncertainties and disturbances. Lastly, simulations and comparisons are provided to evaluate the efficiency and excellent performance of the proposed control approach.

Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode

To resolve the problem of high-precision trajectory tracking control under interference conditions in a missile’s mid-guidance phase, according to the constructed nominal trajectory, an improved adaptive high-order sliding mode trajectory tracking controller (AHSTC) is proposed. In this method, the open-loop nominal trajectories are established according to the nonlinear programming and Gaussian pseudospectra method. A high-precision trajectory tracking controller is developed by designing a nonlinear sliding mode surface and an adaptive high-order sliding mode approaching law combined with the trajectory tracking nonlinear error model. To verify the effectiveness and superiority of the proposed method, analysis and simulation are carried out through the example of a missile mid-guidance phase tracking control. Compared to the linear quadratic regulator (LQR) and active disturbance rejection controller (ADRC) method, the simulation results show that the proposed AHSTC method shows faster convergence and improved tracking effect. Therefore, the proposed AHSTC method has a good results and engineering application value.

Novel fast fixed-time sliding mode trajectory tracking control for manipulator

This paper studies the issue of high-precision trajectory tracking control for manipulator systems with uncertainty disturbances and develops a fast fixed-time control scheme. First, a new type of (FTSM) surface is constructed by combining inverse trigonometric functions, regardless of the size of the system state, which can obtain a faster convergence velocity and high accuracy. The stabilization time of this sliding mode (SM) surface is independent of the initial state. Subsequently, on this basis, an adaptive fixed-time control method is constructed by combining adaptive techniques through the new fixed-time stability lemma proposed in this paper, under which the system's SM variables and tracking errors are guaranteed to reach the region near the equilibrium point at a fixed time while the adaptive law estimates the upper bound of the disturbance and effectively weakens the chattering phenomenon. Finally, the simulation comparison is carried out under different conditions, and the simulation results show the superiority of the control scheme with high precision, fast velocity, and good robustness.

Research on vibration suppression and trajectory tracking control strategy of a flexible link manipulator

Residual vibrations caused by point-to-point motions reduce the positioning accuracy and dynamic performance of flexible link manipulators, and the trajectory planning method, a well-known open-loop control strategy, has been proven to effectively suppress the residual vibration in point-to-point motion. However, this open-loop control method has limitations in dealing with model uncertainty and disturbance problems. In this paper, a control strategy based on dynamics-based trajectory planning and fuzzy self-tuning PD (FST PD) controller is proposed, and applied to achieve residual vibration suppression and trajectory tracking of a flexible link manipulator. To improve the trajectory tracking performance under external disturbances, this control strategy is combined with a linear extended state observer (LESO). Firstly, the dynamic model of the system is deduced using the floating reference frame and Lagrange equation. On this basis, the objective function to minimize the lateral deformation of the tip is constructed, and then the optimal trajectory is obtained by using the Crossbreeding PSO (CBPSO) algorithm. Secondly, the optimal trajectory is used as the desired trajectory to suppress residual vibration, and the FST PD controller combined with the LESO is used to achieve high-precision trajectory tracking control of the flexible link manipulator. Finally, the effectiveness and feasibility of the composite control strategy is verified through multiple simulations. The proposed method can provide support for vibration suppression of flexible link manipulators; meanwhile, the controller structure of this method is simple and easy to implement, and the use of sensors/actuators and other equipment in the system is reduced, which effectively reduces the economic cost.

Dynamic trajectory tracking method of UAV using L1 Adaptive Control

Abstract An attitude control method based on the L1 adaptive structure was proposed for the quadrotor aircraft with nonlinear, strong coupling, and underactuated characteristics during flight. Then introducing a dynamic point trajectory algorithm to solve the problem that traditional linear controller lacks robustness in the quadrotor aircraft. The adaptive controller can quickly estimate the state of the system then track and compensate for interference items. The dynamic point trajectory algorithm was able to calculate the expected trajectory and trajectory deviation of the disturbed aircraft, which improved the anti-interference ability and fault tolerance ability of the system. The simulation results indicate that the flight control system designed based on the L1 adaptive dynamic point method is qualified for achieving high-precision trajectory tracking control and the system has strong anti-interference capability and robustness in the nonlinear state.

Continuous Sliding Mode Control of Robotic Manipulators Based on Time-Varying Disturbance Estimation and Compensation

To address the high-precision trajectory tracking task of uncertain robotic manipulators, a continuous sliding mode control (CSMC) scheme based on time-varying disturbance estimation and compensation is developed. The proposed control approach can not only avoid the chattering of the traditional sliding mode control (SMC) scheme and solve the problems existing in the application of the CSMC method but also make the robot obtain stronger disturbance rejection performance. Meanwhile, the stability of the closed-loop system under the recommended control approach is guaranteed by a defined Lyapunov function. Finally, the trajectory tracking and disturbance rejection performance of the 2-DOF robot under the action of the PID control, traditional SMC, CSMC, and present control methods are carried out, respectively. The results show the effectiveness and superiority of the proposed control method.

Closed-LSTM neural network based reference modification for trajectory tracking of piezoelectric actuator

In this article, we propose a trajectory tracking control method for piezoelectric actuators (PEAs) based on long short-term memory neural network (LSTM-NN). Different from traditional control framework where neural network is used to approximate the open-loop PEA dynamics, LSTM-NN is used to establish the mapping between the actual trajectory and the reference trajectory of the closed-loop PEA, leading to a Closed-LSTM neural network control framework. With this framework, the trained LSTM-NN is used to modify the reference trajectory to compensate for the tracking error without changing the controller. First, we analyze and simplify the modeling of the linear and nonlinear characteristics of the PEA, and select the training input features of the LSTM-NN. Then, we use the actual trajectory and reference trajectory of the closed-loop PEA to train the LSTM-NN. The Closed-LSTM neural network control framework enables independent designs of the baseline feedback controller and feedforward compensator. In particular, the feedback controller is used to guarantee the system stability, and the LSTM-NN reference modification module is used as the feedforward compensator to achieve high-precision trajectory tracking, which does not affect the system stability and can be easily applied to off-the-shelf motion control systems. Its validity is experimentally verified on a PEA platform.

Dual-Arm Coordinated Control Strategy Based on Modified Sliding Mode Impedance Controller.

To meet the high-accuracy position/force control requirements of dual-arm robots for handling a target object, a control algorithm for dual-arm robots based on the modified sliding mode impedance controller MSMIC(tanh) is proposed. First, the combinative kinematics equation of the dual-arm robots and the unified dynamics model combining the manipulated object is established. Second, according to the impedance control motion model for the object, the desired joint angular accelerations of the manipulators are obtained, and the sliding mode controller based on the hyperbolic tangent function as the switch function is introduced to design the coordinated control strategy for dual-arm robots. The stability and convergence of the designed controller are proved according to the Lyapunov function theory. Finally, the operation tasks of the coordinated transport the target object for dual-arm robots are carried out in the simulated experiment environment. Simulation results show that the proposed control scheme can stably output the required internal force and achieve a high-precision trajectory tracking effect while reducing the periodic torque and joint chattering amplitude generated in the conventional sliding mode control algorithm.

Trajectory-following of a 4WID-4WIS vehicle via feedforward–backstepping sliding-mode control

Realisation of high-precision trajectory tracking is the key technology to achieve unmanned driving, which has an important impact on vehicle handling stability, safety and comfort. However, many confounding factors seriously restrict tracking performance and pose a great challenge to the design of the controller for tracking the desired trajectory. A comprehensive method that combines feedforward and backstepping sliding mode control is proposed for a four-wheel independent driving–four-wheel independent steering (4WID-4WIS) vehicle, which has the advantages of over-coupling, multiple degrees of freedom and flexible operation. The desired value is the target for the feedforward output of the controller, and the backstepping sliding mode control is used to overcome all kinds of disturbances. A mature and reliable Luenberger observer is designed to achieve good trajectory tracking performance for reducing some sensors. The proposed method is verified via MATLAB/Simulink simulation, which proved that the method has an excellent trajectory tracking performance.

Tracking Control of Single-Axis Feed Drives Based on ADRC and Feedback Linearisation

The accuracy of manufacturing highly characterises the performance of industrial motion control. However, detrimental effects such as nonlinear coupling, model uncertainty and unknown external disturbances severely affect trajectory tracking performance. In this paper, we proposed an ADRC and feedback linearisation-based control algorithm for the high-precision trajectory tracking of feed drives. The controller was rigorously designed to ensure the convergence of observer states and tracking error. The applicability of the proposed approach was successfully demonstrated via high-fidelity simulation, and the numerical results based on different tracking methods were compared.

Hybrid adaptive negative imaginary- neural-fuzzy control with model identification for a quadrotor

Quadrotor system is subject to multiple disturbances, including both internal and external effects (e.g. wind gusts, coupling effects, and unmodeled dynamics). For example, severe wind disturbances may significantly degrade trajectory tracking during the flight of autonomous aerial vehicles, or even cause loss of control or failure of a tracking mission. This paper introduces a robust hybrid control system, including a linear Strictly Negative Imaginary (SNI) controller and an adaptive nonlinear Neural-Fuzzy control law, to enable high-precision trajectory tracking tasks for a quadcopter drone. Based on a parallel form, both proposed controllers are able to enhance the transient performance, the system response, and the robustness of the quadcopter controllers. Also, a linear time-invariant SNI UAV dynamic model, in combination with an online adaptive residual nonlinear model using the neural network identification, is proposed to model the natural behavior of a quadcopter system. Through a series of numerical simulations, this paper highlights the effectiveness of our hybrid controller in the face of some parameter variations, such as nonlinear aerodynamic models and exogenous disturbances (e.g., wind gusts). Moreover, it compares its tracking performance with that of a fixed-gain SNI controller and the adaptive Neural-Fuzzy controller separately. Finally, the stability of the closed-loop control system is also discussed using the SNI theorem.

Application of Robust Discontinuous Control Algorithm for a 5-DOF Industrial Robotic Manipulator in real-time

Robotic manipulators are nonlinear systems that are extensively used in industrial applications to perform many complex and specialized tasks which requires high precision and accurate trajectory tracking. This paper proposes the real-time implementation of a Robust Discontinuous Controller Algorithm to achieve trajectory tracking for a 5 degrees of freedom (DOF) robotic manipulator in a fast and accurate form with low chattering on the joint motors even under uncertainty and disturbance conditions. The trajectory design is developed by a fifth-order polynomial which ensures that robotic manipulator reaches the final desired position in a smooth way and enables to specify the endpoint positions, speeds and accelerations for each joint. A Lyapunov analysis is performed to guarantee system stability with the proposed controller algorithm. To verify the effectiveness of the proposed controller algorithm some simulations in Simulink ${}^{{\circledR }}$ environment are presented in comparison with a PD controller which is widely used for control of industrial robotic manipulators. Real time experimental results in a robotic manipulator CRS Catalyst-5 by Thermo Electron ®; are presented to support the simulation results and prove the effectiveness of the proposed controller.

Trajectory tracking of an oscillating movement with a low-cost IMU in geodetic surveying applications

In this paper, we design a method to use a low-cost IMU (Inertial Measurement Unit) sensor for the absolute positioning of an oscillating object and for improvement of the kinematic RTS (Robotic Total Station) high-precision trajectory tracking accuracy. Typically when using standard methods for position estimation based on IMU measurements, a drift of several hundred meters occurres after only 1 min of operation. When processing IMU measurements with our proposed method, which is based on the Zero-Phase filter (ZPF), the accuracy of the oscillating object’s position improved to a few centimeters. We used our method to improve the RTS trajectory tracking system. By combining low-cost IMU and RTS measurements, we were able to obtain highly accurate trajectory at a frequency of IMU measurements. We improved the accuracy of the trajectory tracking compared to linear interpolation between measured RTS samples by 40% and the SNR (Signal-to-Noise Ratio) was more than three times higher.