Nonlinear Trajectory Tracking Research Articles

Distributed robust tracking control for multiple unmanned aerial vehicles: theory and experimental verification

The distributed trajectory tracking problem for multiple UAVs under unknown external disturbance is investigated. A new nonlinear robust trajectory tracking strategy for multiple UAVs is proposed. The dynamic model for the UAVs' formation is illustrated in the Tangent frame. For the trajectory tracking problem, a Robust formation tracking control strategy based on robust integral of the signum of error (RISE) is designed to compensate for the effects of unknown external disturbances, which improves the robustness of the UAVs' formation system. Based on the Lyapunov stability analysis, it is proved that the global asymptotic convergence of the coordination errors and the semi-global asymptotic convergence of the UAVs' position tracking errors are achieved. The proposed formation control strategy is validated via real-time experiments that are performed on the self-build UAVs' formation flight control testbed. Flights without wind disturbance and flights with disturbance are all performed. The performance comparison experiment with the conventional sliding mode control algorithm is performed. The experimental results show that the designed control strategy can achieve good coordinated trajectory tracking of multiple UAVs, and has better control performance compared with normal sliding mode control law.

Precision improvement of robotic bioprinting via vision-based tool path compensation

Robotic 3D bioprinting is a rapidly advancing technology with applications in organ fabrication, tissue restoration, and pharmaceutical testing. While the stepwise generation of organs characterizes bioprinting, challenges such as non-linear material behavior, layer shifting, and trajectory tracking are common in freeform reversible embedding of suspended hydrogels (FRESH) bioprinting, leading to imperfections in complex organ construction. To overcome these limitations, we propose a computer vision-based strategy to identify discrepancies between printed filaments and the reference robot path. Employing error compensation techniques, we generate an adjusted reference path, enhancing robotic 3D bioprinting by adapting the robot path based on vision system data. Experimental assessments confirm the reliability and agility of our vision-based robotic 3D bioprinting approach, showcasing precision in fabricating human blood vessel segments through case studies. Significantly, it minimizes the printing layer width disparity to just 0.15 mm compared to the 0.6 mm in traditional methods, and it decreases the average error for curved filaments to 7.0 mm2 from the previous 12.7 mm2 in conventional printing. While these results underscore the significant potential of our innovation in creating precise biomimetic constructs, further investigation is necessary to tackle challenges such as accurately distinguishing closely stacked layers using a vision system, especially under varying lighting conditions. These limitations, coupled with issues of computational complexity and scalability in larger-scale bioprinting, emphasize the importance of enhancing the reliability of the vision-based approach across various conditions. Nonetheless, our innovation demonstrates substantial promise in creating precise biomimetic constructs and paves the way for future advancements in vision-guided robotic bioprinting, including the integration of multi-material printing techniques to enhance versatility.

Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model

This paper addresses the issue of trajectory tracking control for an autonomous underwater vehicle in the presence of parameter perturbations and disturbances in three-dimensional space. The control scheme is based on a combination of the backstepping method, the adaptive integral sliding mode control scheme, and velocity transformation resulting from the decomposition of the inertia matrix, which is symmetric. In addition, adaptive laws were applied to eliminate the effects of parameter perturbations and external disturbances. The main feature of the proposed approach is that the vehicle model is not fully symmetric but contains quantities due to the shift of the center of mass. Another important feature of the control scheme is the ability to detect some of the consequences caused by reducing the vehicle model by neglecting dynamic couplings. Numerical results on the five degrees of freedom (DOF) vehicle model show the efficiency, effectiveness, and robustness of the developed controller.

Nonlinear robust motion trajectory tracking control based on backstepping method for a novel pneumatic actuator

Nonlinear robust motion trajectory tracking control based on backstepping method for a novel pneumatic actuator

Robust Adaptive Control Based on Incremental Nonlinear Dynamic Inversion for a Quadrotor in Presence of Partial Actuator Fault

This paper presents a novel nonlinear robust adaptive trajectory tracking control architecture for stabilizing and controlling a quadrotor in the presence of actuator partial faults. The proposed control strategy utilizes an Incremental Nonlinear Dynamic Inversion (INDI) algorithm as the baseline controller in the inner loop and augments a nonlinear model reference adaptive controller in the outer loop to ensure robustness against unmodeled faults. Additionally, a modified PID controller is introduced in the most outer-loop to track the desired path. The effects of actuator faults are modeled by considering sudden variations in motor thrust and torques. To enhance the control algorithm's robustness, a projection operator is employed in the robust adaptive structure. Comparative performance evaluations with a previous successful algorithm implemented on a quadrotor model demonstrate that the proposed controller achieves full controllability of the faulty quadrotor in pitch, roll, and yaw channels in the presence of actuator partial faults up to 50%.

A Nonlinear Trajectory Tracking Control Strategy for Quadrotor With Suspended Payload Based on Force Sensor

A Nonlinear Trajectory Tracking Control Strategy for Quadrotor With Suspended Payload Based on Force Sensor

Trajectory Planning and Control Design for Aerial Autonomous Recovery of a Quadrotor

One of the most essential approaches to expanding the capabilities of autonomous systems is through collaborative operation. A separated lift and thrust vertical takeoff and landing mother unmanned aerial vehicle (UAV) and a quadrotor child UAV are used in this study for an autonomous recovery mission in an aerial child–mother unmanned system. We investigate the model predictive control (MPC) trajectory generator and the nonlinear trajectory tracking controller to solve the landing trajectory planning and high-speed trajectory tracking control problems of the child UAV in autonomous recovery missions. On this basis, the estimation of the mother UAV movement state is introduced and the autonomous recovery control framework is formed. The suggested control system framework in this research is validated using software-in-the-loop simulation. The simulation results show that the framework can not only direct the child UAV to complete the autonomous recovery while the mother UAV is hovering but also keep the child UAV tracking the recovery platform at a speed of at least 11 m/s while also guiding the child UAV to a safe landing.

Research on Practical Path Tracking Control of Autonomous Underwater Vehicle Based on Constructive Dynamic Gain Controller

In this study, the stochastic nonlinear system-based trajectory tracking control problem of an autonomous underwater vehicle (AUV) is studied. We investigate the time-varying gain adaptive control method to find possible approaches to reduce the excessive computational burden. Enhanced adaptive algorithms are devised by considering the dynamic characteristics of AUV motion. By transforming the original controller design problems into parameter selection problems and subsequently solving them using the functional time-varying observer technical theorem, we can achieve optimal control performance. The control system is shown to constrain system state error due to stochastic disturbances within arbitrarily small domains. A coordinate transformation is proposed for all system states to meet boundedness conditions. We show that the closed-loop stability is confirmed, the system is asymptotically probabilistically stable, and contraction limits given in the stability analysis may be used to certify the convergence of the AUV trajectory errors. A large number of simulation studies using an underwater vehicle model have proved the effectiveness and robustness of the proposed approach. A real-time, time-varying gain constructive control strategy is further developed for the hardware-in-the-loop simulation; the effectiveness of the controller design is confirmed by introducing the controller into the AUV actuator model.

Trajectory Tracking Design for Unmanned Surface Vessels: Robust Control Approach

We propose a robust nonlinear trajectory tracking design by integrating a nonlinear model transformation, robust disturbance eliminator, and trajectory generator for unmanned surface vessels influenced by modeling uncertainties and ocean environmental disturbances. We designed this nonlinear control law to help control unmanned surface vessels following any designated sail trajectory constructed by the trajectory generator. With cubic spline interpolation, this trajectory generator can generate arbitrary smooth trajectories. Simulation results show that the proposed nonlinear robust control law has precise trajectory tracking performance and a robustness property for unmanned surface vessels under harsh ocean environmental disturbances and modeling uncertainties.

A Comparative Study of Various Intelligent Algorithms Based Nonlinear PID Neural Trajectory Tracking Controller for the Differential Wheeled Mobile Robot Model

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSOlearning algorithm is more effective and robust than genetic learning algorithm; this is demonstrated by the minimized tracking error and obtained smoothness of the velocity control signal, especially when external disturbances are applied.

Improved line-of-sight nonlinear trajectory tracking control of autonomous underwater vehicle exposed to high variable speed ocean currents

In this paper, a new controller is proposed to help AUV (Autonomous Underwater Vehicle) track ideal trajectory in the high speed and variable speed ocean current environment. The kinematic and dynamic model of AUV under current interference is derived in the defined coordinate system. For the observation of variable speed current, the proposed controller proposes an improved extended state observer based on ADCP (Acoustic Doppler Current Profiler) sensor and fuzzy control. In order to resist the interference of high speed current, the proposed controller introduces an auxiliary lateral thruster to improve the anti-current capability of AUV. In order to coordinate the path tracking performance and current interference resistance of AUV, the proposed controller proposes an improved line of sight method, where observer gain is selected through fuzzy control. The improved line of sight method compensates for the prediction error of the state observer and the error in the AUV speed regulation process through the switching symbol function term in the sliding film control. The AUV's speed gradually reaches the ideal speed calculated by the navigation law through the dynamic controller, which is designed based on the backstepping method. Finally, the proposed controller is theoretically proved by the Lyapunov stability criterion and tested by comparison simulation in various working environments and controllers.

Adaptive PSO-tuned trajectory tracking controller for quadrotor aircraft based on Lyapunov approach

This research presents a nonlinear adaptive trajectory tracking controller for quadrotor unmanned aircraft. When external disturbances and model uncertainty are present, traditional control algorithms may not be able to provide enough control accuracy. An adaptive nonlinear controller is a remedy for this unsatisfactory performance caused by disturbances and parameter fluctuations. This work proposes a nonlinear adaptive controller based on the Lyapunov method. The technique used here for achieving the adaptive nonlinear control is to adaptively determine the optimal controller gain values. To fine-tune the controller gains, the proposed method suggests the use of either trial and error or the adaptive particle swarm optimization. Simulations with the MATLAB/Simulink software show that the proposed method works superior as compared to traditional control algorithms such as proportional–integral–derivative (PID) and sliding mode controllers even under the influence of parameter variations, external disturbances and communication noises.

Non-Linear Prediction-Based Trajectory Tracking for Non-Holonomic Mobile Robots

Non-Linear Prediction-Based Trajectory Tracking for Non-Holonomic Mobile Robots

Power System Rotor Angle Stability Enhancement Using Lyapunov-Based Trajectory Tracking Controller and Model Reference Adaptive Control

It is vital that the synchronous generator rotor angle be kept stable to avoid disastrous consequences such as the loss of synchronism amongst generators within a power system network. Once it is unstable, the time that is taken to remedy this is advised to be 5–10 s for smaller power systems and 15–20 s for larger ones. The instability that is caused by poorly damped Low-Frequency Electromechanical Oscillations (LFEOs) may result in inter-area oscillations, where a group of generators in one area oscillates against those in another area, thus affecting the stability of the entire network. This paper explores two control architectures, namely, a nonlinear Lyapunov-based trajectory tracking controller and a Model Reference Adaptive Controller (MRAC) as options to enhance the stability of the rotor angle. The performance of each of these controllers was assessed under steady-state conditions, and then, the synchronous generator was subjected to Gaussian noise and an impulse. While the first one is aimed at emulating small variations in the system loads that are responsible for inter-area oscillations, the latter one is an attempt to explore their performance for transient stability.

Three-dimensional nonlinear trajectory tracking control based on adaptive sliding mode

A novel three-dimensional adaptive sliding mode controller (ASMC) is proposed for the nonlinear three-dimensional trajectory tracking of missiles. Firstly, the three-dimensional trajectory tracking problem is decomposed into the pitch channel and the yaw channel, and the trajectory tracking control model of each channel is established, respectively. Secondly, in the design process of the controller, a fast non-singular terminal sliding mode with finite-time convergence characteristics that can avoid singularities and a novel adaptive reaching law that can avoid chattering and accelerate convergence are introduced into the two channels. Thirdly, the Lyapunov stability analysis shows that the adaptive sliding mode tracking controller proposed in this paper can guarantee the rapid convergence and stability of the closed-loop system in the presence of modeling errors. Finally, simulation results show that the controller can effectively complete trajectory tracking under the influence of the wind disturbance and mismatched disturbances.

Autonomous Locomotion Trajectory Shaping and Nonlinear Control for Lower Limb Exoskeletons

This article presents a strategy for autonomous locomotion trajectory planning for high-level control of lower limb exoskeletons by defining a novel set of adaptive central pattern generators (ACPGs) to facilitate safe and compliant interaction with the human. A time-varying bounded-gain adaptive disturbance observer is designed for estimating the human–robot interaction (HRI) needed for online central pattern generator (CPG)-based trajectory shaping and low-level nonlinear trajectory tracking control. The proposed ACPG dynamics for each exoskeleton joint updates the motion frequency and amplitude based on the observed HRI torque, which is also coupled with adjacent joints’ CPGs to regulate their phase differences in real time. An integrated Lyapunov analysis is conducted to ensure the closed-loop system’s stability and uniformly ultimately boundedness of both the tracking error and the torque estimation error in the controlled exoskeleton. Experimental studies are performed with an able-bodied human wearer by applying arbitrary torques on the exoskeleton’s joints in order to evaluate the proposed autonomous control strategy in online adjustment and personalization of the locomotion.

Prediction of Angular Acceleration for Practical Realization of Incremental Nonlinear Trajectory-tracking Control for Aircrafts

Prediction of Angular Acceleration for Practical Realization of Incremental Nonlinear Trajectory-tracking Control for Aircrafts

Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System.

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.

On Non-Linear Trajectory Tracking via Variety-Based Background Subtraction Models

On Non-Linear Trajectory Tracking via Variety-Based Background Subtraction Models

Optimal design of model predictive controller based on transient search optimization applied to robotic manipulators.

Due to nonlinearity and uncertainty of the robotic manipulator, the design of the robot controller has a crucial impact on its performance of motion and trajectory tracking. In this paper, the linear parameter varying (LPV) - model predictive controller (MPC) of a two-link robot manipulator is established and then the controller's optimal parameters are determined via a newly developed meta-heuristic algorithm, transient search optimization (TSO). The proposed control method is verified by set point and nonlinear trajectory tracking. In the test of set-point tracking, the LPV-MPC scheme optimized by TSO has better performance compared to the computed torque controller (CTC) schemes tuned by TSO or other metaheuristic algorithms. In addition, good performances can also be observed in the tests of nonlinear trajectory tracking via the LPV-MPC scheme by TSO. Moreover, the robustness of the method to structural uncertainty is verified by setting a large system parameter deviation. Results reveal that we achieved some improvements in the optimization of MPC of the robot manipulator by employing the proposed method.