Dynamic Tracking Control Research Articles

Dynamic Modeling and Robust Trajectory Tracking Control of a Hybrid Propulsion-Based Small Underwater Robot

This paper proposes a hybrid propulsion-based small underwater robot for robust trajectory tracking control in a harsh and complex underwater environment. The robot is equipped with a Coanda-effect jet thruster and a pair of propeller-based reconfigurable magnetic-coupling thrusters, allowing it to traverse safely in confined or cluttered spaces as well as cruise efficiently in the open water. To investigate the robot dynamic modeling, we first formulated its simplified mathematical model and estimated the hydrodynamic coefficients by performing the planar motion mechanism using CFD (computational fluid dynamics) simulation. Then, a double-loop trajectory tracking control architecture was designed considering the model uncertainties and environmental disturbances. Based on Lyapunov theory, the outer-loop kinematic control produces the virtual velocity command, while the inner-loop dynamic control adopts the full-state feedback L1-adaptive control to match the command. The asymptotic convergence of the tracking errors and the stability of the whole closed-loop system are guaranteed. Finally, comparative simulations in the presence of unknown disturbances and the variation of model parameters were carried out to verify the robustness of our proposed trajectory tracking control, which is also suitable for the separated son robots.

Research on Dynamic Path Planning and Tracking Control for Ship Collision Avoidance

Ship collisions are prevalent every year, leading to significant maritime traffic accidents. This paper presents research on dynamic path planning and tracking control for ship collision avoidance by integrating ship automatic avoidance technology to address this issue. We conducted a comprehensive study on artificial potential fields, trajectory tracking, and route trajectory tracking in response to the current state of ship collision avoidance and trajectory tracking. The study employed vector decomposition and slider control as research methods to analyze, optimize, and modify ship collision avoidance methods. Additionally, we carried out collision avoidance simulations using MATLAB to verify the stability and safety of ship trajectory tracking under various methods to advance the research on ship collision avoidance and trajectory. The proposed approach has the potential to significantly reduce ship collisions and enhance ship trajectory safety.

Dynamic event-triggered neural adaptive preassigned fast finite-time tracking control for stochastic nonaffine structure nonlinear systems

Dynamic event-triggered neural adaptive preassigned fast finite-time tracking control for stochastic nonaffine structure nonlinear systems

Neural Adaptive Dynamic Surface Asymptotic Tracking Control of Hydraulic Manipulators With Guaranteed Transient Performance.

In this article, a novel neural network (NN)-based adaptive dynamic surface asymptotic tracking controller with guaranteed transient performance is proposed for n-degrees of freedom (DOF) hydraulic manipulators. To fulfill the work, the entire manipulator system model, including hydraulic actuator dynamics, is first established. Then, the neural adaptive dynamic surface controller is designed, in which the NN is utilized to approximate the unknown joint coupling dynamics, while the approximation error and uncertainties of the actuator dynamics are addressed by the nonlinear robust control law with adaptive gains. In addition, a modified funnel function that ensures the joint tracking errors remains within a predefined funnel boundary and is skillfully incorporated into the adaptive dynamic surface control (ADSC) design to achieve a guaranteed transient tracking performance. The theoretical analysis reveals that both the guaranteed transient tracking performance and asymptotic stability can be achieved with the proposed controller. Contrastive simulations are performed on a 2-DOF hydraulic manipulator to demonstrate the superiority of the proposed controller.

Fuzzy Dynamic Event-Triggered Tracking Control for Semilinear Time-Delay Parabolic Systems

In this article, the fuzzy dynamic event-triggered tracking control problem of semilinear parabolic systems (SLPSs) with time-varying delay is investigated. Firstly, T-S fuzzy partial differential equation (PDE) models are introduced to describe the SLPSs. Secondly, a less conservative and more general fuzzy dynamic event-triggered strategy (DETS) is proposed to reduce communication consumption and avoid unnecessary continuous signal monitoring. Since the dynamic threshold is closely related to the currently sampled signal and the latest successfully transmitted signal, it can be promptly dynamically adjusted. In addition, on the basis of a reasonable assumption, a novel linear matrix inequality (LMI) relax technique is introduced to deal with the mismatched premise variables between the fuzzy systems and the fuzzy controller. By constructing the appropriate Lyapunov-Krasovskii candidate functional (LKCF), the criteria that the SLPSs can asymptotically track the target systems is derived, and the desired dynamic event-triggered control (DETC) gains can be obtained by solving a set of LMIs. The DETS reduces effectively communication resource consumption. Finally, the control problem of temperature distribution of catalytic rod in practical engineering application is given to verify the effectiveness and superiority of the proposed control scheme.

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).

Trajectory tracking control of a pneumatically actuated continuum manipulator in the presence of obstacles by using terminal sliding mode control

This paper proposes an efficient trajectory planning and dynamic tracking controller scheme for a pneumatic continuum manipulator under the effect of material hysteresis. First of all, generalized governing nonlinear dynamic equations in the form of partial differential equations for pneumatic continuum manipulator dynamics are developed by using discrete Cosserat-rod theory, where the manipulator material hysteresis is modeled by using a fractional order Bouc–Wen model. Then, the trajectory planning for the end-effector of a continuum manipulator is proposed, which accounts for the static obstacles in the workspace and the Jacobian singularity. Subsequently, an adaptive terminal sliding mode controller for the joint space control combined with a simple PI controller for task space control is proposed. The proposed controller guarantees exponential convergence of the manipulator tip positional error in finite time, even in the existence of external disturbances and model uncertainties, without any need for prior knowledge of their upper bounds. Finally, the proposed controller is applied to a two-segment continuum manipulator, the trunk of Robotino-XT, through numerical simulations and the performance gain over two controllers proposed in the literature for similar pneumatic continuum manipulators is demonstrated.

Finite-Time Adaptive Dynamic Surface Asymptotic Tracking Control of Uncertain Multi-Agent Systems with Unknown Control Gains

In this work, the finite-time asymptotic tracking control problem of uncertain multi-agent systems with unknown control gains is studied. For the unknown control gain of each subsystem in multi-agent systems, we consider using the Nussbaum gain function techniques to handle them. To deal with the unknown uncertain nonlinear dynamics, the radial basis function neural network is introduced in each step of the dynamic surface control design. In addition, a nonlinear compensating term with the estimation of an unknown bounded parameter is designed to avoid repeated differentiation of each virtual control law. Then, based on the neural network control method, dynamic surface control technique, and finite-time control theory, an adaptive neural network finite-time dynamic surface control law is finally designed. Using stability analysis, it is proven that the presented adaptive control law can guarantee all signals of the closed-loop system semi-global practical finite-time stable, and the tracking error of each follower agent can converge to a small neighborhood of zero in finite time. Finally, a class of single-link robot systems is provided to illustrate the effectiveness of the designed control law.

Dynamic Event-Triggered Adaptive Fixed-Time Fuzzy Tracking Control for Stochastic Nonlinear Systems Under Asymmetric Time-Varying State Constraints

Dynamic Event-Triggered Adaptive Fixed-Time Fuzzy Tracking Control for Stochastic Nonlinear Systems Under Asymmetric Time-Varying State Constraints

Dynamic scaling immersion and invariance adaptive pose tracking control without scaling factor

Dynamic scaling immersion and invariance adaptive pose tracking control without scaling factor

Dynamic modeling and tracking control strategy for flexible telescopic space manipulator based on neural network compensation

Flexible telescopic space manipulator (FTSM) is playing an increasingly important role in assisting astronauts to accomplish space missions. The telescopic structure leads to time-varying dynamical parameters of the FTSM, thereby reducing the tracking control accuracy. At the same time, the lightweight and large length-diameter ratios are very likely to cause the vibration phenomenon of FTSM during the motion. To solve the above problems, in the dynamics modeling, this paper uses the assumed modal method (AMM) and Lagrange principle to establish the dynamics model considering two-dimensional deformation and disturbance torque. Furthermore, the dynamics models are simplified and derived by ignoring the nonlinear terms (INTs). Besides, the influence of the simplified dynamics models on the accuracy of FTSM’s deformation is analyzed by simulation. It is found that the INTs simplified dynamics model has higher modeling accuracy. Based on the INTs simplification model, the control law is designed. More importantly, the RBF neural network is used to identify and compensate for the time-varying terms and disturbance torque in the FTSM. Then the sliding mode control strategy is proposed by the hyperbolic tangent function as the approximation rate. Finally, the effectiveness of the RBF neural network compensated sliding mode control strategy is illustrated by simulation and ground control experiments of the FTSM.

Hybrid Controller for a Single Flexible Link Manipulator

In this study, the dynamic modeling and step input tracking control of single flexible link is studied. The Lagrange-assumed modes approach is applied to get the dynamic model of a planner single link manipulator. A Step input tracking controller is suggested by utilizing the hybrid controller approach to overcome the problem of vibration of tip position through motion which is a characteristic of the flexible link system. The first controller is a modified version of the proportional-derivative (PD) rigid controller to track the hub position while sliding mode (SM) control is used for vibration damping. Also, a second controller (a fuzzy logic based proportional-integral plus derivative (PI+D) control scheme) is developed for both vibration damping and hub position tracking. A comparison is made between the performances of these two controllers. The Hybrid controller with PD and SM shows better tracking behavior than obtained from the suggested fuzzy (PI+D)2 controller for a single link flexible manipulator.

Vision Guided Dynamic Synchronous Path Tracking Control of Dual Manipulator Cooperative System

Abstract Compared with a single manipulator manufacturing cell, a dual manipulator cooperative system has more advantages in reconfigurability and flexibility. However, there are calibration errors and multi-source disturbances in the collaborative process, which lead to the processing trajectory accuracy defects of large-scale associated machining features. To solve the above problems, a practical path tracking synchronous control algorithm based on position-based visual servoing (PBVS) is proposed in this paper for the dual manipulator cooperative system, the proposed dynamic path tracking cross-coupled sliding mode controller (PTCSMC) scheme can realize dynamic paths correction while executing the pre-planned paths. In addition, for the cross-coupled technology to be applied into the proposed control algorithm for dynamic path tracking based on the real-time feedback of the highly repeatable 3D visual measurement instrument (VMI), the tracking and synchronous errors of the dual manipulators converge synchronously to zero. Finally, the stability of proposed control algorithm is proven by the Lyapunov method. In the end, the real-time line and circle path tracking experimental results using two industrial manipulators demonstrate the effectiveness of the proposed algorithm.

Reduced-order model based dynamic tracking for soft manipulators: Data-driven LPV modeling, control design and experimental results

We propose a generic nonlinear reduced-order tracking control method for elastic soft robots. To this end, a new linear parameter varying (LPV) control framework is developed using the data collected from the soft robots. Specifically, for LPV modeling we first derive a nonlinear robot model, which is large-scale by nature, using finite element methods (FEM). Then, a proper orthogonal decomposition (POD) algorithm is used to generate a set of linearized reduced-order models, representing the local behaviors of the soft robot at different operating points within the workspace. Via a unified POD projector, not only the order of these linearized models can be significantly reduced but also their mechanical structure and stability properties can be preserved for LPV modeling and control design. Next, using radial basis function (RBF) networks, we propose an iterative training method to build the LPV robot model by interpolating a subset of selected linearized models with a specified interpolation error. For LPV control design, the equivalent-input-disturbance (EID) concept is exploited to develop a dynamic tracking control scheme, which is composed of three core components: feedforward control, disturbance-estimator control and feedback control. The feedforward control is designed to account for the effects of the trajectory reference and the time-varying affine term, issued from the FEM-based model linearization. The disturbance-estimator control is obtained from a generalized proportional integral LPV observer, which also provides the estimated reduced-order states for feedback control. The observer-based feedback control design is reformulated as a convex optimization problem under linear matrix inequality (LMI) constraints. The globally uniformly ℓ∞ stability of the closed-loop LPV robot model is guaranteed by means of Lyapunov stability theory. Experimental tests are conducted with a soft Trunk robot, inspired by the elephant trunk, under several scenarios with small and large deformations to show the effectiveness of the proposed LPV tracking control framework. A comparative study is also performed with a recent linear EID-based controller and an iterative learning controller to emphasize the interests this nonlinear control method for soft elastic robots. This paper is complemented with a series of demonstration videos: https://bit.ly/3D8C4Vd.

Modeling and Trajectory Tracking Control for a Multi-Section Continuum Manipulator

This paper presents a dynamic modeling and trajectory tracking control design for a hyper-redundant continuum manipulator (HRM), which is able to continuously bend along its length. The HRM has the advantages of high flexibility, large workspace, and low inertia, but also poses challenges of complex kinematics, and dynamic coupling. To address these challenges, the kinematics model was developed based on the piecewise constant curvature (PCC) assumption that captures the relationship between the cable lengths, joint angles, and end-effector pose. Inverse kinematics is presented geometrically by solving trigonometric equations. The dynamic model for the continuum robot (CR) is derived using the Euler–Lagrange representation, which incorporates the effects of gravity and elasticity. Accordingly, three control strategies are developed and applied to a two-section continuum robot, which are the inverse dynamic proportional integral derivative controller (PID), fuzzy logic controller (FLC), and sliding mode controller (SMC). We validate our proposed methods through simulations on various 3D trajectories, utilized MATLAB symbolic math toolbox in conjunction with Simulink simulation; demonstrate and evaluate different close-loop dynamic control responses for different scenarios. This simulation used in the creation of a unique animated simulation for a whole 3D continuum robot while tracking its desired trajectories. Finally, the simulation results demonstrate that our proposed methods can effectively control the HRM with high tracking performance, exhibiting enhanced responses in terms of settling time, rising time, and steady-state error. While the developed SMC outperforms Poth's PID and FLC in terms of robustness and settling time (51% and 10.3%), respectively.

Novel robust approach–based dynamic global terminal synergetic tracking control for uncertain second-order systems: Wind turbine system and power converter applications

This article provides a systematic approach for synthesizing a novel robust synergetic control mechanism. This controller employs a dynamic global terminal synergetic surface with additional sinusoidal function. The overall macrovariable gives the system a rapid approaching rate, maintains the finite time stability, and provides a chattering-free control law. The stability analysis is validated for any uncertain second-order system via indirect approach of Lyapunov theorems. Furthermore, a hyperbolic tangent nonlinear disturbance observer is designed in order to estimate any lumped disturbance. The feasibility and the effectiveness of the proposed composite control law is tested and evaluated through simulations for simple model, wind turbine system and DC-DC buck converter. Finally, the simulation results show that the presented dynamic global terminal synergetic control–based hyperbolic tangent nonlinear disturbance observer can effectively reject the disturbances, enhance the robustness, and guarantee the tracking accuracy and rapidity.

Neuroadaptive Dynamic Surface Asymptotic Tracking Control of a VTOL Aircraft with Unknown Dynamics and External Disturbances

This work studies the asymptotic tracking control problem of a vertical take-off and landing (VTOL) aircraft with unknown dynamics and external disturbances. The unknown nonlinear dynamics of the VTOL aircraft are approximated via the introduction of radial basis function neural networks. Then, the weight update laws are designed. Furthermore, the parameter update control laws are presented to deal with the errors generated during the approximation process and the external disturbances of the aircraft system. Moreover, first-order filters are introduced to avoid repeated differentiation of the designed virtual control laws, thereby effectively eliminating the “complexity explosion” problem caused by traditional backstepping control. Based on the application of the neural network control method, dynamic surface control technique, weight update laws and parameter update control laws, neuroadaptive dynamic surface control laws for the aircraft system are finally proposed. Theoretical analysis shows that the proposed control law can ensure that the aircraft system asymptotically tracks the reference trajectories and the tracking errors can converge to a small neighborhood of zero by choosing the appropriate designed parameters. Finally, simulation examples are provided to verify the effectiveness of proposed control laws.

Dynamic event-triggered observer-based control for autonomous underwater vehicles in the Trans-Atlantic Geotraverse hydrothermal field using rotation matrices

The issue of dynamic event-triggered trajectory tracking control of autonomous underwater vehicles (AUVs) in the Trans-Atlantic Geotraverse (TAG) active mound is addressed. Concretely, a novel adaptive event-triggered disturbance observer along with auxiliary functions is constructed to handle with actuator faults and ocean current in the TAG active mound. By using rotation matrices, a new tracking error system involving position and attitude is developed such that potential singularities caused by the large pitch will be sufficiently avoided. By deep combination with model parameters, a sliding mode manifold based on additional velocity errors is established to promote robustness and convergence. Following the detailed analysis, sufficient conditions for the input-to-state practical stability in mean square are given to ensure the tracking performance. An adaptive dynamic event-trigger method is applied to reduce the communication frequency between the embedded PC module and actuators. Finally, the applications of an AUV tracking system in the near-bottom hydrothermal mound are carried out to verify the efficiency of the presented method.

Dynamic Event-Triggered Adaptive Tracking Control for Multiagent Systems with Power Exponential Function Using Fuzzy Disturbance Observer

Dynamic Event-Triggered Adaptive Tracking Control for Multiagent Systems with Power Exponential Function Using Fuzzy Disturbance Observer

Robust H∞ Output Feedback Trajectory Tracking Control for Steer-by-Wire Four-Wheel Independent Actuated Electric Vehicles

This paper investigates the trajectory tracking control issue of four-wheel independently actuated electric vehicles (FWIA EVs) with steer-by-wire devices concerning parameter uncertainties, external disturbances, input saturation, and vehicle sideslip angle not easily obtained. A robust H∞ dynamic output feedback control strategy is proposed for the integrated control of the steering motor current and direct yaw moment without using sideslip angle information to ensure the reference trajectory tracking and the improvement of handling performance and yaw stability. In the proposed integration control framework, the steer-by-wire device dynamic is involved in the polyhedral linear parameter-varying (LPV) trajectory tracking error model considering the time-varying longitudinal velocity, and the norm-bounded parameter uncertainties such as road adhesion coefficient, tire cornering stiffness, vehicle mass, and vehicle moment of inertia. With the help of the LPV model of all the states of the steer-by-wire FWIA EV, a dynamic output feedback trajectory tracking controller is designed using the robust H∞ technique. The controller gain matrices are obtained by solving the linear matrix inequalities. Finally, the high-fidelity full-vehicle model based on the CarSim-MATLAB/Simulink joint simulation platform verifies the robustness and advantages of the designed control strategy in the accelerated lane-change scenario.