Predictive Control For Trajectory Tracking Research Articles

Differential-algebraic equation-based model predictive control for trajectory tracking of spacecraft-mounted continuum manipulators

Spacecraft-mounted continuum manipulators (SMCMs) exhibit great potential for performing dexterous operations in unstructured environments due to their inherent compliance and dexterity. However, the dynamic model of the rigid-flexible coupling SMCM is highly nonlinear and typically formulated as a set of implicit differential-algebraic equations (DAEs), posing significant challenges for precise trajectory tracking control. This paper proposes a novel model predictive control (MPC) framework specifically designed for generic DAEs to achieve precise trajectory tracking of the SMCM under uncertain disturbances and input limitations. The DAE model of the SMCM is discretized into a set of nonlinear algebraic equations. By performing implicit differentiation of these equations with respect to the system state, the state transition matrix (STM) for the DAE model is derived. The optimal control action for the SMCM can be further determined based on the derived STM. Additionally, nonlinear complementary functions are introduced to address the issue of input limitations, allowing the problem of determining the optimal control sequence to be equivalently transformed into a set of nonlinear algebraic equations for solving. Numerical simulations demonstrate that the proposed approach can achieve precise trajectory tracking of the SMCM while strictly adhering to input limitations.

Set‐Membership Filtering Based Model Predictive Control for Trajectory Tracking of Automated Guided Vehicles

ABSTRACTThis article considers the trajectory tracking problem of automated guided vehicles with unknown‐but‐bounded noises and proposes a set‐membership filtering (SMF) based model predictive control strategy. A 2‐DOF vehicle model is first established with considering the unknown‐but‐bounded process and measurement noises. Then, a trajectory tracking control scheme is proposed which consists of two parts, that is, a set‐membership observer and a model predictive controller. The proposed set‐membership observer is able to deal with the unknown‐but‐bounded noises and obtain the state estimation ellipsoid for the considered automated guided vehicle. Further, the designed model predictive controller utilizes the estimated ellipsoid to compute the control sequence. Finally, simulation results demonstrate the effectiveness and superiority of the proposed method.

Hybrid Physics-Learning Model Based Predictive Control for Trajectory Tracking of Unmanned Surface Vehicles

Hybrid Physics-Learning Model Based Predictive Control for Trajectory Tracking of Unmanned Surface Vehicles

Vehicle dynamics analysis and adaptive control scheme design under coupled slope for trajectory tracking of four-wheel independent drive autonomous vehicles

Trajectory tracking is the core function of autonomous vehicle motion control. On variable road slopes, vehicles will encounter extra forces due to gravity, distinct from driving on flat roads. These forces will cause significant deviations in trajectory tracking and even instability, especially for four-wheel independent drive autonomous vehicles (4WIDAVs) with closely coupled longitudinal-lateral dynamics. To address above issues, first, a modified dynamics model considering coupled slopes is built, and the slope effect on vehicle motion and stability is analysed. Secondly, treating coupled slopes as augmented states, a square root cubature Kalman filter (SRCKF) is designed for real-time slope estimation. Then, a slope-adaptive model separate predictive trajectory tracking control strategy is proposed, decoupling the slope-adaptive tracking system into longitudinal and lateral subsystems. Predictive control is performed separately to ensure real-time performance, and prediction information is exchanged between subsystems to preserve dynamics coupled characteristics, enhancing tracking accuracy. Finally, the effectiveness of the proposed scheme is validated by simulations and real-vehicle experiment under the varying slope condition. Results show that the proposed scheme improves tracking accuracy by 17.59% and reduces computation time by 55.02% compared to existing scheme in simulations, and in experiment, the tracking error is reduced by 17.89%.

Research on model predictive trajectory tracking control based on particle swarm optimization

Abstract This paper focuses on the challenges of low trajectory tracking accuracy in intelligent vehicles caused by different road adhesion coefficients and vehicle speed conditions, aiming to address these challenges with an improved Model Predictive Control (MPC) algorithm. Firstly, a model encompassing vehicle dynamics featuring two degrees of freedom has been formulated, along with a corresponding model to track trajectory errors. Secondly, a Model Predictive Control (MPC) algorithm is designed with trajectory tracking accuracy and control increment as the objective functions. Additionally, incorporating a Particle Swarm Optimization (PSO) algorithm with MPC allows for the dynamic computation of the optimal prediction horizon size. Afterward, a unified simulation model is assembled, employing both CarSim and MATLAB/Simulink, to conduct dual-lane trajectory tracking simulation analysis under diverse road adhesion and vehicle speed conditions. The efficacy of the proposed control algorithms is validated through this process.

Lyapunov-based model predictive control for trajectory tracking of hovercraft with actuator constraints and external disturbances

In this study, a Lyapunov-based model predictive control (LMPC) method is developed to address the trajectory tracking problem of autonomous hovercrafts subjected to unknown complex disturbances from ocean environments and practical constraints of marine surface vessels, such as actuator increment limitations and saturation. Initially, the novel LMPC algorithm is applied to enhance tracking performance through rolling optimization and online optimization. Subsequently, a nonlinear disturbance observer is designed to estimate external disturbances caused by wind and dynamics uncertainties. Furthermore, to theoretically ensure control stability, contraction constraints are established using the nonlinear backstepping control method. This approach provides the essential conditions for the system's robustness and stability. Finally, the superiority and robustness of the designed LMPC trajectory tracking method are demonstrated through numerical simulations conducted in a marine environment.

Nonlinear Model Predictive Control for Trajectory Tracking and Obstacle Avoidance of Free-Floating Satellite Manipulator

Nonlinear Model Predictive Control for Trajectory Tracking and Obstacle Avoidance of Free-Floating Satellite Manipulator

Model Predictive Trajectory Tracking Control of an Underactuated Bionic Underwater Vehicle

Model Predictive Trajectory Tracking Control of an Underactuated Bionic Underwater Vehicle

Model-free Predictive Trajectory Tracking Control and Obstacle Avoidance for Unmanned Surface Vehicle With Uncertainty and Unknown Disturbances via Model-free Extended State Observer

Model-free Predictive Trajectory Tracking Control and Obstacle Avoidance for Unmanned Surface Vehicle With Uncertainty and Unknown Disturbances via Model-free Extended State Observer

Predictive trajectory tracking control for the USV in networked environments with communication constraints

The trajectory tracking control problem of under-actuated unmanned surface vessels (USVs) suffering from communication constraints (network delay, packet loss, packet disorder) as well as external environment disturbance is investigated in this paper. To solve the aforementioned trajectory control problem, a nonlinear networked predictive control strategy based on the discrete sliding mode framework is proposed. Initially, using the forward Euler discretization method, a novel discrete virtual velocity control law is designed to transform trajectory tracking control into virtual velocity tracking control. Then, a sliding mode controller is suggested to suppress external disturbance of the USV. In addition, a networked sliding mode predictive control (NSMPC) scheme is proffered to guarantee active compensation of the communication constraints and stability of the trajectory tracking system simultaneously. Afterward, a theoretical analysis is given to show that the closed-loop system is uniformly bounded stable after the introduction of the digital network. Finally, extensive comparative simulation examples are provided to demonstrate the effectiveness of the proposed control strategy.

Variable Tube-Based Model Predictive Trajectory Tracking Control Strategy with Adaptive Load for Automatic Truck

Variable Tube-Based Model Predictive Trajectory Tracking Control Strategy with Adaptive Load for Automatic Truck

Robust Laguerre‐based model predictive control for trajectory tracking of a mobile robot using an linear matrix inequality (LMI)‐based approach

AbstractThis paper studies the trajectory tracking of a constrained mobile robot under slippery conditions. The goal is to propose a controller for real‐time operations of time‐varying dynamics with insignificant execution time. Therefore, a Laguerre‐based model predictive control (LMPC) is designed, and robustness is provided with an linear matrix inequality (LMI)‐feedback controller. Moreover, a recursive least square (RLS) algorithm with a forgetting factor is utilized to identify the required parameters of the LMI‐based controller. LMPC and LMI‐based controller stability is achieved with suboptimal theory and Lyapunov function, respectively. This algorithm separates the nominal system and introduces new dynamics containing uncertainties. Furthermore, LMPC is removed from the online calculation, which dramatically reduces computation burden and time; consequently, online computation is dedicated to determining the LMI feedback. Simulations are provided to compare the proposed robust controller to its not robust counterpart. Finally, it is demonstrated that this controller diminishes the execution time considerably, making it incomparable to previous robust MPC strategies.

Non-linear Model-based Predictive Control for Trajectory Tracking and Control Effort Minimization in a Smartphone-based Quadrotor

In this paper, the design and implementation of a non-linear model-based predictive controller (NMPC) for predefined trajectory tracking and to minimize the control effort of a smartphone-based quadrotor is carried out. The optimal control actions are calculated in each iteration by means of an optimal control algorithm based on the non-linear model of the quadrotor, considering some aerodynamic effects. Control algorithm implementation and simulation tests are executed on a smartphone using the CasADi framework. In addition, a technique for estimating the energy consumed based on control signals is presented. NMPC controller performance is compared to an H-infinity controller and an LQI controller, using three predefined trajectories, where the NMPC average tracking error was around 50\% lower, and average estimated power and energy consumption slightly higher, with respect to the H-infinity and LQI controllers.

Research on the Model Predictive Trajectory Tracking Control of Unmanned Ground Tracked Vehicles

This article summarizes the research significance and the development status of the unmanned ground tracked vehicles (UGTVs). According to the speed and steering principle of the UGTVs in plane motion, the kinematic state space equation of the vehicle is established. Based on the model predictive control (MPC), the UGTVs trajectory tracking controller is also established. After introducing the overall control system solution, based on the vehicle model, the track speed on both sides is used as the control input to solve the predicted output of the system. After constraining the control amount, the model prediction controller is established by using S function. Several simulations with different preset speeds are performed under linear conditions, and the numerical simulation with different prediction time domains and control time domains are performed under continuous curves. The test validates the effectiveness of the controller, and the effects of speed and time domain parameters on the deviation are analyzed based on the results. Based on satellite positioning technology, trajectory tracking tests of the UGTVs are carried out. After analyzing the positioning principle and common errors, the real-time kinematic technology is used to improve the positioning accuracy of the vehicle prototype. The hardware of the test platform includes a data acquisition system, a control execution system and a walking execution system, and the software part includes data processing and optimization control. The trajectory tracking experiments under different driving conditions are conducted, and the results show that the navigation tracking system can achieve good tracking performance.

Model Predictive Trajectory Tracking Control of 2 DoFs SCARA Robot under External Force Acting to the Tip along the Trajectory

The robot arms often follow a certain trajectory depending on the type of end effector with functions of spray-painting, arc welding, bonding or machining etc. Therefore, trajectory-tracking control is a very important issue in robot arm applications. Also, the robot must be able to follow the determined trajectory stably under the influence of external forces or machining forces it encounters in its operations. In this study, a Model Predictive Control (MPC) for trajectory tracking control of a 2 Degrees of Freedom (DoFs) Selective Compliant Assembly Robot Arm (SCARA) under an external force acting to the tip of the robot along the trajectory was performed. The effectiveness of the MPC method used has been demonstrated by simulation applications. According to simulation studies, successful results were obtained.

Collision-Free Model Predictive Trajectory Tracking Control for UAVs in Obstacle Environment

In this paper, we propose a collision-free model predictive trajectory tracking control algorithm for unmanned aerial vehicles (UAVs) in environments with both static obstacles and dynamic obstacles. Collision avoidance is ensured by obtaining outer polyhedral approximations of each interval of the dynamic obstacles trajectories based on MINVO basis, and then optimizing a plane to separate the polyhedra and the trajectory of the UAV. By incorporating the resulting computationally efficient collisionfree constraints and divers physical constraints, a model predictive control (MPC) optimization problem is formulated with a tailored terminal constraint set, which can be solved by a standard nonlinear programming solver. Moreover, the control theoretic properties are established, including recursive feasibility, the guarantee of collision avoidance, as well as closed-loop stability. Finally, the efficacy of the proposed algorithm is successfully evaluated by a simulation in a multi-obstacle environment.

Anti-Disturbance Lyapunov-Based Model Predictive Control for Trajectory Tracking of Dynamically Positioned Ships

Trajectory tracking is a fundamental task of the dynamic positioning (DP) system. This paper studies the problem of trajectory tracking of DP ships constrained by control inputs under environmental disturbances. To solve this problem, we develop a novel anti-disturbance Lyapunov-based model predictive control (ADLMPC) scheme. Firstly, an extended state observer (ESO) is designed to estimate environmental disturbances. By combining the ESO with Lyapunov-based model predictive control, the ADLMPC scheme is devised. Secondly, a virtual controller which satisfies input constraints is developed by backstepping and the auxiliary dynamic system, and it is integrated into the Lyapunov contraction constraint in ADLMPC. We show that if the parameters for the virtual controller are appropriately determined, the recursive feasibility of ADLMPC is theoretically guaranteed, and the uniform ultimate boundedness of all signals in the trajectory tracking control system is achieved. Finally, the simulation results display the efficacy and superiorities of the ADLMPC scheme.

Koopman operator based model predictive control for trajectory tracking of an omnidirectional mobile manipulator

Omnidirectional mobile manipulators (OMMs) have been widely used due to their high mobility and operating flexibility. However, since OMMs are complex nonlinear systems with uncertainties, the dynamic modeling and control are always challenging problems. Koopman operator theory provides a data-driven modeling method to construct explicit linear dynamic models for the original nonlinear systems, using only input-output data. It then allows to design control system based on well-established model-based linear control methods. This paper designs a Koopman operator based model predictive control (MPC) scheme for trajectory tracking control of an OMM. Firstly, using Koopman operator and extended dynamic mode decomposition method, an approximate high-dimensional linear dynamic explicit expression for the OMM system is obtained. Then MPC is employed to achieve tracking control based on the derived linear Koopman model. Finally, to show modeling accuracy for the OMM, the Koopman model is evaluated via both simulation and experimental tests. The control performances of the Koopman operator based MPC design are also verified in the simulation and experimental results.

Event-based adaptive horizon nonlinear model predictive control for trajectory tracking of marine surface vessel

This study investigates the trajectory tracking problem of fully actuated marine surface vessels (MSVs). First, an optimal problem for trajectory tracking of MSV is established. Model uncertainty, time-varying environmental disturbances, and actuator saturation are considered in this problem. For the control algorithm, considering that the traditional nonlinear model predictive control (NMPC) algorithm requires large amounts of computational resources, it is not easy to transmit the control signal to the system in real-time in practice. Meanwhile, due to the limited bandwidth of the system, the large amount of data transmission may lead to congestion and loss of signal transmission. Therefore, a novel event-based adaptive horizon nonlinear model predictive control (EAHNMPC) scheme is proposed to relieve the computation burden and reduce the signal transmission frequency for the MSV's trajectory tracking. Moreover, stability proof of adaptive predictive horizon control is given, and simulation experiments under different cases are performed. The results show that the proposed scheme has better control performance and computational performance than other approaches.

Event-triggered-based nonlinear model predictive control for trajectory tracking of underactuated ship with multi-obstacle avoidance

Trajectory tracking and automatic obstacle avoidance are both cutting-edge problems in controlling underactuated ship; however, they have been generally studied separately due to its challenge in multiple control objectives with fewer actuators. Nevertheless, the obstacles are frequently in the way of trajectory tracking at sea; thus, a novel event-triggered-based nonlinear model predictive control (ENMPC) solution is, for the first time, presented for trajectory tracking of underactuated ship with multi-obstacle avoidance herein. Taking advantage of solving multi-objective control problem of nonlinear model predictive control, the trajectory tracking problem of underactuated ship is devised as an optimization problem. Then, the mechanism for automatic obstacle avoidance is established through designing the output constraints elaborately for the resultant optimization problem; considering the input saturation of ship, the problem of trajectory tracking of underactuated ship with multi-obstacle avoidance is naturally transformed into the optimization problem with input and output constraints herein. In order to improve the efficiency of nonlinear model predictive control, an event-triggered control mechanism is designed, i.e., the optimization problem is only solved when the triggering condition is satisfied; to this end, the computational burden is reduced. Finally, comparative simulations are included to validate the effectiveness and advantage of the presented solution.