Dynamic Trajectory Tracking Research Articles

Sharp Curve Trajectory Tracking of a Universal Omni-Wheeled Mobile Robot Using a Sliding Mode Controller

Abstract In our previous works, Amarasiri et al. (2022, “Robust Dynamic Modeling and Trajectory Tracking Controller of a Universal Omni-Wheeled Mobile Robot,” ASME Lett. Dyn. Sys. Control 2(4), p. 040902) and Amarasiri et al. (2024, “Investigating Suitable Combinations of Dynamic Models and Control Techniques for Offline Reinforcement Learning Based Navigation: Application of Universal Omni-Wheeled Robots,” ASME Lett. Dyn. Sys. Control 4(2), p. 021007), two dynamic models of a universal omni-wheeled mobile robot (UOWMR), and trajectory-tracking controllers (three linear and one nonlinear) were developed and presented. The purpose of that work was to identify suitable combinations of dynamic models and trajectory-tracking controllers, choosing the combination with the highest tracking accuracy and the lowest solver execution time. The ultimate purpose is to utilize these physics-based tools in a reinforcement learning (RL) agent developed for path planning and navigation in an unstructured environment. Three trajectories were investigated including a smooth path, a sharp curved path, and a smooth path with disturbance forces. The sliding mode controller (SMC) following a trajectory with sharp (right-angled) curves was not investigated in that work. The sharp corners caused indeterminacy in the path derivatives required for the SMC algorithm. In this short article, we complete our studies of the SMC on sharp corner paths utilizing slight trajectory corner smoothing.

Dynamic performance-guaranteed adaptive event-triggered trajectory tracking control for underactuated surface vehicles

This paper investigates the trajectory tracking control problem of underactuated surface vehicles in the presence of model uncertainties and external disturbances. A dynamic performance-guaranteed adaptive event-triggered control scheme is proposed to transform the original constrained control problem into an equivalent unconstrained problem and ensure that the trajectory tracking is achieved with prescribed performance. By developing a dynamic event-triggered mechanism with adjustable thresholds that can be updated in an adaptive way, the communication burden and actuator wear can be effectively alleviated. To avoid differential explosions that may increase system complexities, an improved transformation-adapted dynamic surface control (DSC) is designed to eliminate adverse effects induced by performance functions and make the DSC applicable to performance-transformed systems. By utilizing the parameter compression algorithm, model uncertainties and external disturbances can be effectively addressed, and the adaptive laws can be integrated into a concise form, with only two online updating parameters required. Comprehensive analysis is provided to verify that all error signals are semi-globally uniformly ultimately bounded (SGUUB). Compared to existing approaches, the proposed control scheme exhibits lower update frequency.

Investigating Suitable Combinations of Dynamic Models and Control Techniques for Offline Reinforcement Learning Based Navigation: Application of Universal Omni-Wheeled Robots

Abstract Omnidirectional locomotion provides wheeled mobile robots (WMR) with better maneuverability and flexibility, which enhances their energy efficiency and dexterity. Universal omni-wheels are one of the best categories of wheels that can be used to develop a WMR (Amarasiri et al., 2022, “Robust Dynamic Modeling and Trajectory Tracking Controller of a Universal Omni-Wheeled Mobile Robot,” ASME Letters Dyn. Sys. Control., 2(4), p. 040902. 10.1115/1.4055690). We study dynamic modeling and controllers for mobile robots to train in a reinforcement learning (RL)-based navigation algorithm. RL tasks require copious amounts of learning iteration episodes, which makes training very time consuming. The choice of dynamic model and controller has a significant impact on training time. In this paper, we compare a traditional Kane’s equations model to a non-holonomic canonical momenta model (Barhorst, 2019, “Generalized Momenta in Constrained Non-Holonomic Systems—Another Perspective on the Canonical Equations of Motion,” Int. J. Non-Linear Mech., 113, pp. 128–145.). We implemented four controllers: proportional integral derivative, linear quadratic regulator with integral action, pole placement, and a full nonlinear sliding mode controller. We summarize the pros and cons of each of the modeling techniques, and control laws implemented. The outcomes of our analysis will improve RL training time for path generation in unstructured environments.

Distributed Dynamic Surface Control for a Class of Quadrotor UAVs with Input Saturation and External Disturbance

An adaptive dynamic surface trajectory tracking control method based on the Nussbaum function is proposed for a class of quadrotor UAVs encountering unknown external disturbances and unidentified nonlinearities. By transforming controller expressions into numerical solutions, the challenge of overly complex controller design expressions is addressed, simplifying the overall controller design process and enhancing the efficiency of simulation programs. Additionally, an adaptive controller based on Nussbaum gain is introduced to effectively resolve actuator saturation issues. This approach mitigates complexities associated with traditional control design and ensures smooth operation of the quadrotor UAVs. The proposed methodology offers promising prospects for enhancing the robustness and performance of quadrotor UAVs under uncertain operating conditions. Finally, to validate the effectiveness of the proposed control scheme, a hardware-in-the-loop experimental setup is constructed. The dynamic model of the quadrotor UAVs and the proposed controller scheme are implemented on the Rapid Control Prototype (RCP) and Real-Time Simulator (RTS), respectively. This facilitates a semi-physical simulation experiment, providing a basis for the subsequent application of the control scheme to actual aerial vehicles. The concluding experimental results affirm the effectiveness of the proposed control scheme and highlight its potential for practical applications.

Dynamic Current Trajectory Tracking Strategy With Optimized Overmodulation Method for Permanent Magnet Synchronous Motor Drives

Dynamic Current Trajectory Tracking Strategy With Optimized Overmodulation Method for Permanent Magnet Synchronous Motor Drives

ПОРІВНЯЛЬНИЙ АНАЛІЗ ПОКАЗНИКІВ ЯКОСТІ ВІДПРАЦЮВАННЯ КУТОВОЇ ШВИДКОСТІ В ОДНОКОНТУРНІЙ СИСТЕМІ ПРОГНОЗНОГО КЕРУВАННЯ ДВИГУНОМ ПОСТІЙНОГО СТРУМУ

The results of a comparative study of two speed control systems for a DC motor with permanent magnets are presented: a cascaded system based on PI controllers, and a single-loop system developed on the basis of model predictive control methods. The research was carried out by the simulations for the case of speed trajectory tracking task. It is shown that, under the conditions of known parameters, the controller based on predictive control, designed as a system with one input and one output, unlike the system with PI controllers and compensations of derivatives of the reference signal, does not provide asymptotic speed trajectory tracking. The transients during constant load torque compensation are similar, both control schemes provide asymptotical speed regulation with similar performances. In the case of introduc-ing variations of DC motor moment of inertia, the levels of dynamic trajectory tracking errors become commensurate for both control systems. Unlike the system based on PI regulators, the controller based on model predictive control does not need to measure the armature current and provides improved dynamics in voltage limiting modes. Ref. 17, fig. 7. Keywords: model predictive control, DC motor, speed trajectory tracking, PI-controller.

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.

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).

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.

An intelligent tracking system for surgical instruments in complex surgical environment

In recent years, the problem of visual occlusion in optical surgical tracking system has attracted widespread attention. To solve this problem, this paper proposes a tracking system for surgical instruments based on data fusion of multicamera modules. Multicamera modules are used to track surgical instruments from different angles to solve the problem of tracking occlusion in monocular tracking. Firstly, occlusion of monocular tracking based on different fiducial marker is analyzed, and a fast calibration method of tracking system for surgical instrument based on multicamera module is proposed. The calibration of the tracking system is mainly divided into the calibration of surgical instruments and the fast calibration between multicamera modules. On this basis, the occlusion detection mechanism is proposed to screen out the data with large errors, and the fuzzy control theory is used to calculate the support degree between different modules. Finally, the serviceable data is fused based on the least square method and the position of surgical instruments is estimated. The average accuracy of static positioning and dynamic trajectory tracking is mm and mm respectively. The real-time performance of the proposed system is 35 frames per second (fps). Compared with the existing optical tracking system, the method proposed in this paper has stronger anti-occlusion and simpler operation.

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.

Fault-Tolerant Trajectory Tracking Based on Improved Chaotic Firefly Algorithm Model Prediction Control for Human-Occupied Vehicle With Ocean Current

In this article, aiming at the characteristics of model uncertainty and low-speed of human-occupied vehicle (HOV) system, a cascaded controller in ocean current environment is proposed. First, the kinematics controller is designed by improved chaotic firefly algorithm (ICFA)-model predictive control (MPC) to get the speed control signal. The multivariable constraint capability of ICFA-MPC can solve the problem of speed jump caused by thruster fault and further solve the problem of driving saturation. Second, the kinematic controller is combined with the dynamics controller which is according to the adaptive algorithm. The adjustable control parameters capability of the adaptive control can further obtain the dynamic control law under the influence of ocean current. Finally, the hybrid allocation strategy based on ICFA is used to redistribute the thruster force/moments. Simulation results displayed the dynamic trajectory tracking fault-tolerant controller can steadily track the reference trajectory under thruster fault without tracking disturbance, speed jump, and driving saturation problem.

Adaptive Dynamic Programming-Based Cross-Scale Control of a Hydraulic-Driven Flexible Robotic Manipulator

This paper focuses primarily on adaptive dynamic programming (ADP)-based tracking control of the hydraulic-driven flexible robotic manipulator system (HDFRMS) with varying payloads and uncertainties via singular perturbation theory (SPT). Firstly, the dynamics is derived using a driven Jacobin matrix, which represents the coupling between the hydraulic servo-driven system and rigid–flexible manipulator established using the assumed mode method and Lagrange principle. Furthermore, the whole dynamic model of the manipulator system is decoupled into a second slow subsystem (SSS), a second fast subsystem (SFS) and a first fast subsystem (FFS). The three subsystems can describe a large range of movement, flexible vibration and electro-hydraulic servo control, respectively. Hereafter, an adaptive dynamic programming trajectory tracking control law with a critic-only policy iteration algorithm is presented in the second slow timescale, while both robust optimal control (ROC) in the second first timescale and adaptive sliding mode control (ASMC) in the first fast timescale are also designed using the Lyapunov stability theory. Finally, the numerical simulations are carried out to illustrate the rightness and robustness of the singular perturbation decomposition and proposed composite control algorithm.

Passive Decoupled Multitask Controller for Redundant Robots

Kinematic redundancy in robots makes it possible to execute several control tasks simultaneously. As some tasks are usually more important than others, it is reasonable to dynamically decouple them in order to ensure their execution in a hierarchical way or even without any interference at all. The most widely used technique is to decouple the system by feedback linearization. However, this requires actively shaping the inertia and consequently modifying the natural dynamics of the robot. Here, we propose a passivity-based multitask tracking controller that preserves these inertial properties but fully compensates for task-space cross couplings using external-force feedback. In addition, three formal proofs are provided: uniform exponential stability for trajectory tracking, passivity during physical interaction, and input-to-state stability. The controller is validated in simulations and experiments and directly compared with the hierarchical PD+ approach and the feedback linearization. The proposed approach is well suited for safe physical human–robot interaction and dynamic trajectory tracking if measurements or estimations of the external forces are available.

Combined MPC and Dynamic Neural Network-Based UAVs Trajectory Tracking Control

Combined MPC and Dynamic Neural Network-Based UAVs Trajectory Tracking Control

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.

Development of neural‐network‐based stereo bionic compound eyes with fiber bundles

SummaryThe compound eye system has many unique advantages, enabling organisms to quickly and accurately obtain the three‐dimensional spatial information of the target. Therefore, its bionic applications have great potential in object localization, dynamic tracking, and 3D reconstruction. However, the current bionic compound eye systems still have great difficulties in target detection. Most of the bionic compound eye systems only verify the possibility of three‐dimensional (3D) detection, and there is no suitable calibration and detection scheme. In this article, a fiber‐optic stereo bionic compound eye is designed, and a simple and rapid calibration method suitable for this system is selected by comparing two effective optical information. Further, the system quantitatively analyzes the detection performance of the target to achieve precise positioning and dynamic trajectory tracking. In the static detection experiment, the average detection errors of the 3D position at the horizontal angle, the elevation angle and the depth are 0.33°, 0.32° and 1.84 mm, respectively. In the dynamic trajectory tracking experiment, the circle radius errors in the X‐Z plane and Y‐Z plane are 0.04 and 2.20 mm, respectively. The proposed system provides a new perspective to understand the nature of the compound eye.

Dynamic event-triggered trajectory tracking control for underactuated marine surface vessels with positive minimum inter-event time guarantees

This paper proposes a dynamic event-triggered trajectory tracking control scheme for underactuated marine surface vessels (MSVs) with positive minimum inter-event time (MIET) guarantees. Under the presented scheme, control inputs only need to transmit to actuators at the designed event triggering time. Additionally, the maximum signal transmission frequency can be prior known by designers. To facilitate the design of the above control architecture, an unified second-ordered tracking error dynamic system is firstly derived. Then, based on the transformed error dynamic system, an event-based adaptive control law is developed. To counteract external disturbances and unmodeled dynamics, the minimum-learning-parameter (MLP) based neural approximator is constructed to enhance system robustness. Finally, by introducing internal dynamic variables, a dynamic event-triggered mechanism (DETM) is established to manage communications between the control module and actuators. Compared with existing event-triggered methods, the proposed event-driven method is dynamic and more flexible to satisfy network bandwidth requirements. Besides, MIETs of the presented DETM are also proved to be strictly positive and computable from control parameters. Theoretical analysis and simulation results verify the validity and effectiveness of the proposed DETM based control scheme.

Deep Reinforcement Learning for Autonomous Dynamic Skid Steer Vehicle Trajectory Tracking

Designing controllers for skid-steered wheeled robots is complex due to the interaction of the tires with the ground and wheel slip due to the skid-steer driving mechanism, leading to nonlinear dynamics. Due to the recent success of reinforcement learning algorithms for mobile robot control, the Deep Deterministic Policy Gradients (DDPG) was successfully implemented and an algorithm was designed for continuous control problems. The complex dynamics of the vehicle model were dealt with and the advantages of deep neural networks were leveraged for their generalizability. Reinforcement learning was used to gather information and train the agent in an unsupervised manner. The performance of the trained policy on the six degrees of freedom dynamic model simulation was demonstrated with ground force interactions. The system met the requirement to stay within the distance of half the vehicle width from reference paths.

Dynamic speed trajectory generation and tracking control for autonomous driving of intelligent high-speed trains combining with deep learning and backstepping control methods

The development of autonomous transportation systems has received increasing attention over the last decades. Different from existing automatic train control systems, the decision-making capability in the autonomous driving system enables a train to adapt to the complicated and dynamic circumstances. This paper in particular focuses on the decision-making problem for the autonomous driving of intelligent high-speed trains, and proposes a novel decision-making framework by combining the deep learning and backstepping control methods. By exploiting the deep learning methods, a speed trajectory generator is trained with the actual driving data, and dynamically calculates the reference speed trajectory according to the real-time driving condition. Then, a backstepping controller is designed to track the reference speed trajectory such that the separation, cohesion and alignment requirements for the autonomous driving of high-speed trains are achieved. Simulation experiments are implemented to illustrate the effectiveness of our methods.