Dynamic Trajectory Tracking Control Research Articles

Improved SSA‐RBF neural network‐based dynamic 3‐D trajectory tracking model predictive control of autonomous underwater vehicles with external disturbances

AbstractThis paper studies the three‐dimensional (3‐D) dynamic trajectory tracking control of an autonomous underwater vehicle (AUV). As AUV is a typical nonlinear system, each degree of freedom is strongly coupled, so the traditional control method based on the nominal model of AUV cannot guarantee the accuracy of the control system. To solve this problem, we first propose a prediction model based on a radial basis function neural network (RBF‐NN). The nonlinearity of AUV is learned and modeled offline by RBF‐NN based on previous data. This model can reflect the time sequence state and control variables of AUV. Secondly, to avoid the overfitting problem in network training based on the traditional gradient descent method, a new adaptive chaotic sparrow search algorithm (ACSSA) is proposed to optimize the network parameters, to improve the full approximation ability of RBF‐NN to nonlinear systems. To eliminate the steady‐state error caused by external interference during AUV trajectory tracking, a nonlinear optimizer is designed by updating the deviation of the NN model output layer. In each sampling period, the predictive control law is calculated online according to the deviation between the predicted value and the actual value. In addition, the stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the 3‐D dynamic trajectory tracking the performance of AUV under different external disturbances is verified by MATLAB/Simulink, and the results show that the proposed controller is more efficient and robust than the standard model predictive controller (MPC) controller and the standard NN model predictive controller (NNPC).

APSO-MPC and NTSMC Cascade Control of Fully-Actuated Autonomous Underwater Vehicle Trajectory Tracking Based on RBF-NN Compensator

In this paper, a model predictive control (MPC) method optimized by an adaptive particle swarm optimization (APSO) algorithm is proposed. Combined with non-singular terminal sliding mode control (NTSMC), the inner and outer double-closed-loop control system is constructed to solve the fully actuated autonomous underwater vehicle (AUV) dynamic trajectory tracking control problem. First, the outer loop controller generates the expected optimal velocity commands and passes them to the inner loop velocity controller, which generates the available control inputs to ensure the entire closed-loop trajectory tracking. In the controller design stage, system input and state constraints are effectively considered. After that, a compensator based on an adaptive radial basis function (RBF) neural network (NN) is designed to compensate for the model error and external sea state disturbances and to improve the control accuracy of the system. Then, the stability of the proposed controller is proved based on Lyapunov analysis. Finally, the dynamic trajectory tracking performance of an AUV with different sea state disturbances is verified by simulation, and the simulation results are compared with double-closed-loop PD control and cascade control of standard MPC based on PSO and SMC. The results show that the designed controller is effective and robust.

Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data.

In this paper, a novel model predictive control (MPC) method based on the population normal probability division genetic algorithm and ant colony optimization (GA-ACO) method is proposed to optimally solve the problem of standard MPC with constraints that generally cannot yield global optimal solutions when using quadratic programming (QP). Combined with dynamic sliding mode control (SMC), this model is applied to the dynamic trajectory tracking control of autonomous underwater vehicles (AUVs). First, the computational fluid dynamics (CFD) simulation platform ANSYS Fluent is used to solve for the main hydrodynamic coefficients required to establish the AUV dynamic model. Then, the novel model predictive controller is used to obtain the desired velocity command of the AUV. To reduce the influence of external interference and realize accurate velocity tracking, dynamic SMC is used to obtain the control input command. In addition, stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the trajectory tracking performance of the AUV in an underwater, three-dimensional environment is verified by using the MATLAB/Simulink simulation platform. The results verify the effectiveness and robustness of the proposed control method.

Robot dynamic trajectory tracking control algorithm based on steady-state closed-loop learning

In order to improve the stability control ability of flexible lower limb exoskeleton robot, a dynamic trajectory tracking control algorithm of flexible lower limb exoskeleton robot based on steady-state closed-loop learning is proposed. Gyroscope and rangefinder are used as information sensors of flexible lower limb exoskeleton robot to collect position information of flexible lower limb exoskeleton robot, fuse collected positioning information of flexible lower limb exoskeleton robot, fuse physical information and measure parameters of flexible lower limb exoskeleton robot by using dynamic information measurement method, and obtain mapping feature set of Cartesian space according to trajectory components of flexible lower limb exoskeleton robot. The pose information of the flexible lower limb exoskeleton robot is obtained through the forward kinematics, and the information is enhanced according to the spatial position information. The steady-state closed-loop learning method is adopted to realize the adaptive learning of the robot dynamic trajectory tracking control. The simulation results show that this method is adaptive to the dynamic trajectory tracking control of the robot, and the positioning control ability of the robot is strong.

Optimization-based nonlinear control laws with increased robustness for trajectory tracking of non-holonomic wheeled mobile robots

This paper presents the integration of kinematic and dynamic trajectory tracking control problem for non-holonomic wheeled mobile robots (WMRs) in the presence of model uncertainties and disturbances. The non-holonomic constraints, inherent nonlinearities and model uncertainties of WMRs are the main difficulties arising in trajectory tracking control for these systems. To address this problem, after modeling of a WMR incorporating wheel actuators dynamics, optimization-based nonlinear control laws are analytically developed for the kinematic and dynamic based controllers using the prediction of WMR responses. The tracking precision is more increased, under disturbances and uncertainties by appending the integral feedback technique to the design method. The performed analyses as well as the simulation results show that the tracking errors are remarkably decreased by the proposed control system in the presence of model uncertainties and disturbances. Finally, the effectiveness of the proposed controller is compared with that of a sliding mode controller, reported in the literature, through simulation results.

Sliding mode control of position commanded robot manipulators

This paper investigates the method of implementing dynamic controller on a manipulator which do not have direct drive joints. A previously proposed torque to position conversion method for servo actuated robot manipulators is used with an adaptive backstepping based sliding mode controller for dynamic trajectory tracking control. The proposed controller uses a nonsingular finite time sliding surface to achieve finite time stability as well as higher tracking performance. To avoid a structurally complex control law as well as obtain partial model independency of the controller, the soft nonlinearities of the manipulator are estimated using the time delay control philosophy where delayed signals are used to estimate the model nonlinearities. The entire system is validated using simulation and experimental studies.

QPSO-model predictive control-based approach to dynamic trajectory tracking control for unmanned underwater vehicles

In this paper, a model predictive control (MPC) method based on quantum-behaved particle swarm optimization (QPSO), combining with sliding mode control (SMC), is studied to solve the dynamic trajectory tracking problem of Unmanned Underwater Vehicle (UUV) in three-dimensional underwater environment. First, the kinematic controller based on QPSO-MPC is designed to obtain the velocity signal. QPSO limits the velocity of UUV within a specified range to handle the speed jump problem when compared with backstepping control. Then kinematic control is extended to dynamic control for verifying the feasibility of QPSO-MPC in the realistic dynamical UUV system. Here SMC is adopted in dynamic control because of its good robustness and anti-interference performance. The combination of kinematic and dynamic controllers can efficiently realize the trajectory tracking of UUV. The experimental simulation results prove that the algorithm proposed in this paper can solve the speed jump and the driven saturation caused by speed jump, and also can satisfy the propeller constraints of UUV.

Computed force and velocity control for spatial multi-DOF electro-hydraulic parallel manipulator

A novel dynamic trajectory tracking controller for spatial 6-DOF electro-hydraulic parallel manipulator considering system nonlinearity-computed force and velocity controller is proposed, with a view of improving the control performance with high computational efficiency of control algorithm. The dynamic model of electro-hydraulic parallel manipulator, both mechanical and hydraulic system, is described by using Kane and hydromechanics method. The requisite system states are estimated via forward kinematics based upon global Newton–Raphson with monotonic descent algorithms under the measured actuator position. The desired leg position and velocity required for the proposed controller are calculated by an analytical method corresponding to the desired generalized pose, and the desired driven force is computed with an effectively simplified inverse dynamics. Under feed-forward of the desired driven force and velocity, the computed force and velocity controller is developed with actual leg position as its feedback only, and the desired leg position, velocity and driven force as its input. The control performance of the proposed controller for multi-DOF parallel manipulator is evaluated in theory and experiment, especially for dynamic tracking performance. Experimental results show that the presented controller can greatly improve the dynamic trajectory tracking performance for high real time electro-hydraulic parallel manipulator.

Control of flexible joint robot manipulators using a combined controller with neural network and linear regulator

This paper addresses the issue of dynamic trajectory tracking control of flexible joint robot manipulators. A new control strategy based on the technologies of linear feedback and neural network is proposed. It is well known that linear feedback controllers are simpler and easier to design, and neural networks have learning ability. These two advantages are utilized in order to deal with the control problems arising from non-linearity and dynamic instability of the flexible joint robot. The linear feedback control scheme is designed using optimal control approach, and the neural network is then designed based on the BP method and added to the control system. There are two different ways to add the neural network into the control system: being an additional regulator in parallel with the linear feedback controller, and being a dynamic compensator with some function of regulator. This leads to two different control systems. Performance of these two control systems are compared based on simulation results. Experiments are carried out using a two-link flexible joint robot as a test bed. The results demonstrate the effectiveness and usefulness of the proposed control method.

Adaptive feedback linearizing control of nonholonomic wheeled mobile robots in presence of parametric and nonparametric uncertainties

In this paper, the integrated kinematic and dynamic trajectory tracking control problem of wheeled mobile robots (WMRs) is addressed. An adaptive robust tracking controller for WMRs is proposed to cope with both parametric and nonparametric uncertainties in the robot model. At first, an adaptive nonlinear control law is designed based on input-output feedback linearization technique to get asymptotically exact cancellation of the parametric uncertainty in the WMR parameters. The designed adaptive feedback linearizing controller is modified by two methods to increase the robustness of the controller: (1) a leakage modification is applied to modify the integral action of the adaptation law and (2) the second modification is an adaptive robust controller, which is included to the linear control law in the outer loop of the adaptive feedback linearizing controller. The adaptive robust controller is designed such that it estimates the unknown constants of an upper bounding function of the uncertainty due to friction, disturbances and unmodeled dynamics. Finally, the proposed controller is developed for a type (2, 0) WMR and simulations are carried out to illustrate the robustness and tracking performance of the controller.

マクロ‐マイクロシステムによるフレキシブルアームの動的軌道制御

In this paper, a dynamic trajectory tracking controller for a macro-micro manipulator system is proposed, in which the dynamics of the system is considered. The macro-micro manipulator system is a system consisting of a wide-range, low-response manipulator as a macro part, and a narrow-range, high-response micro part at the tip of the macro part. When the proposed scheme is applied to the system, the macro part is controlled by PD feedback controller for each joint, while the micro part is controlled to track the desired trajectory considering the dynamics of the whole system. An experimental result demonstrates the effectiveness of the proposed control scheme.