Nonlinear model predictive control trajectory tracking for a fixed-wing UAV in a deep-stall and perching landing maneuver with guaranteed stability

The article studies the trajectory tracking control problem of underactuated marine vehicles via the nonlinear model predictive control (NMPC) strategy, where practical control and state constraints present. It is a well-known challenging issue that the conventional NMPC is not applicable for underactuated marine vehicles, due to the fact that there does not exist a local static continuous state-feedback controller to stabilize the underactuated dynamics. To resolve this issue, this article proposes to construct an auxiliary time-varying tracking controller to aid terminal constraint design in the NMPC framework, where the time-varying tracking controller borrows the ideas from Lyapunov's direct method and backstepping approach. Based on this, a novel NMPC algorithm is designed to ensure trajectory tracking control of underactuated marine vehicles. Furthermore, a systematic parameter design approach is developed. Under the designed parameters, we show that the tracking error system is input-to-state stable (ISS). Finally, the effectiveness of the designed algorithm is verified by thorough simulation and hardware experiments.

To enhance the performance of trajectory tracking in high-speed autonomous vehicles, this paper adopts a new technology for controlling the vehicle body to tilt toward the inside of a curve, known as “tilting technology.” It achieves this tilt through an active suspension system that inclines the vehicle body toward the inside of the curve, thereby reducing or offsetting the torque generated by gravity with the torque produced by centrifugal force. This significantly improves the vehicle’s handling stability and anti-rollover capability. Integrating this technology with active steering control, a nonlinear model predictive trajectory tracking controller has been designed. For this integrated controller, the Fiala lateral tire force model is used to establish a nonlinear vehicle model with steering-rolling dynamics, while a double-lane-change and single-lane-change tests are designed as the reference paths. To avoid the tilting angle of the vehicle body being too large to exceed the effective stroke of the suspension, a clipped ideal tilt angle is adopted as the desired tilting angle. Simulation verification is carried out to confirm the validity of the integrated trajectory tracking control. The proposed controller is compared with two other trajectory tracking controllers, the controller that takes zero rolling angle as the control target and the controller without rolling control. The results show that, compared with the latter two, the proposed trajectory tracking controller can ensure well tracking ability, meanwhile effectively improving the handling stability, anti-rollover capability, and occupant lateral ride comfort during trajectory tracking for high-speed unmanned vehicles.

Operating in the near-vicinity of marine energy devices poses significant challenges to the control of underwater vehicles, predominantly due to the presence of large magnitude wave disturbances causing hazardous state perturbations. Approaches to tackle this problem have varied, but one promising solution is to adopt predictive control methods. Given the predictable nature of ocean waves, the potential exists to incorporate disturbance estimations directly within the plant model; this requires inclusion of a wave predictor to provide online preview information. To this end, this paper presents a Nonlinear Model Predictive Controller with an integrated Deterministic Sea Wave Predictor for trajectory tracking of underwater vehicles. State information is obtained through an Extended Kalman Filter, forming a complete closed-loop strategy and facilitating online wave load estimations. The strategy is compared to a similar feed-forward disturbance mitigation scheme, showing mean performance improvements of 51% in positional error and 44.5% in attitude error. The preliminary results presented here provide strong evidence of the proposed method's high potential to effectively mitigate disturbances, facilitating accurate tracking performance even in the presence of high wave loading.

Motion control is one of the three core modules of autonomous driving, and nonlinear model predictive control (NMPC) has recently attracted widespread attention in the field of motion control. Vehicle dynamics equations, as a widely used model, have a significant impact on the solution efficiency of NMPC due to their stiffness. This paper first theoretically analyzes the limitations on the discretized time step caused by the stiffness of the vehicle dynamics model equations when using existing common numerical methods to solve NMPC, thereby revealing the reasons for the low computational efficiency of NMPC. Then, an A-stable controller based on the finite element orthogonal collocation method is proposed, which greatly expands the stable domain range of the numerical solution process of NMPC, thus achieving the purpose of relaxing the discretized time step restrictions and improving the real-time performance of NMPC. Finally, through CarSim 8.0/Simulink 2021a co-simulation, it is verified that the vehicle dynamics model equations are with great stiffness when the vehicle speed is low, and the proposed controller can enhance the real-time performance of NMPC. As the vehicle speed increases, the stiffness of the vehicle dynamics model equation decreases. In addition to the superior capability in addressing the integration stability issues arising from the stiffness nature of the vehicle dynamics equations, the proposed NMPC controller also demonstrates higher accuracy across a broad range of vehicle speeds.

It is expected that precision farming operations will increasingly rely on more complex automatic steering and navigation capabilities of agricultural vehicles. In this paper, a nonlinear model predictive tracking (NMPT) controller is presented for precision guidance in agricultural applications. The basic idea is to use a motion model for the vehicle and compute in real-time an optimal M-step-ahead control sequence, which minimizes the total M+1 step tracking error of the projected motion. Numerous simulations were performed and the NMPT consistently converged to the desired trajectories and followed them accurately, despite large initial errors and discontinuities in the desired velocities and orientations. The controller's performance was superior to pure-pursuit control and depended strongly on parameters such as the optimization horizon M, and the cost-weights assigned to the various tracking errors. The optimization horizon regulates a trade-off between tracking quality (large M) vs. consistently fast convergence (small M). The cost-weights affect tracking quality and also the shape of the path, by regulating trade-offs among position, orientation, and velocity errors. Overall, NMPT seems to offer a promising approach for advanced precision guidance applications, and deserves further investigation.

This paper presents road-map-assisted standoff tracking of a ground vehicle using nonlinear model predictive control. In model predictive control, since the prediction of target movement plays an important role in tracking performance, this paper focuses on utilizing road-map information to enhance the estimation accuracy. For this, a practical road approximation algorithm is first proposed using constant curvature segments, and then nonlinear road-constrained Kalman filtering is followed. To address nonlinearity from road constraints and provide good estimation performance, both an extended Kalman filter and unscented Kalman filter are implemented along with the state-vector fusion technique for cooperative unmanned aerial vehicles. Lastly, nonlinear model predictive control standoff tracking guidance is given. To verify the feasibility and benefits of the proposed approach, numerical simulations are performed using realistic car trajectory data in city traffic.

This paper presents a nonlinear model predictive path tracking control method based on tire adhesion margin. The proposed method considers a vehicle’s tire adhesion margin and introduces it into an objective function, which can effectively improve the safety of automobiles. Moreover, autonomous vehicles would have increased more maneuverability to enable them to deal with some emergencies when their tire adhesion margin is large. To confirm the feasibility and effectiveness of the controller, Simulation experiments are performed on MATLAB and CarSim/Simulink under diverse scenarios.

Nonlinear Model Predictive Trajectory Control in Tractor-Trailer System for Parallel Guidance in Agricultural Field Operations

Navigation system for agricultural machines: Nonlinear Model Predictive path tracking

To solve the problem of trajectory tracking lateral control in autonomous driving technology, a model predictive control (MPC) controller trajectory tracking lateral control method combined with a deep deterministic policy gradient algorithm (DDPG) is proposed in this paper. This method inputs the real-time state of the vehicle into DDPG to achieve real-time automatic optimization of the prediction time domain and control time domain parameters of the MPC controller, and then affects the specific performance of the MPC controller in trajectory tracking lateral control. Specifically, the state space, action space, and reward function of DDPG are defined, and the automatic driving trajectory tracking lateral controller is designed in combination with the vehicle dynamics model. To reduce the exploration space of DDPG and improve the training efficiency of the entire model, the technique of advantage-disadvantage experience separation and extraction is introduced. Finally, the proposed method was trained and verified in various scenarios, and compared with two other lateral control methods for autonomous driving. The results showed that the learning and training time of the trajectory tracking lateral control method based on DDPG-MPC was shorter than that of the DDPG-based method, and the evaluation indicators in the trajectory tracking control process were better than those of the DDPG-based method and original MPC-based method.

Lyapunov-based model predictive control trajectory tracking for an autonomous underwater vehicle with external disturbances

This article presents a tracking control approach with obstacle avoidance for a mobile robot. The control law is composed of two parts. The first is a discrete-time model predictive method-based trajectory tracking control law that is derived using an optimal quadratic algorithm. The second part is the obstacle avoidance strategies that switch according to two different designed obstacle avoidance regions. The controllability of the avoidance control law is analyzed. The simulation results validate the effectiveness of the proposed control law considering both trajectory tracking and obstacle avoidance.

In this paper, we propose an explicit nonlinear model predictive control (ENMPC) method based on a robust observer to solve the trajectory tracking problem for outdoor quadrotors. We take into consideration the external aerodynamic disturbances present in the dynamics of the Newton-Euler quadrotor model. To overcome the effects of these disturbances, a high gain observer combined with a first order sliding mode observer are proposed to estimate both the states and the unknown disturbances using the only positions and angular measurements of the quadrotor. The estimated signals are then used by the predictive controller in order to ensure the trajectory tracking objective. Despite the presence of bounded disturbances, the convergence of the composite controller (ENMPC technique with the latter observers) is guaranteed through a stability analysis. Theoretical results are validated with some numerical simulations showing the good performances of the proposed tracking control approach.

This paper employs nonlinear disturbance observer for robust Nonlinear Model Predictive Control (NMPC) of biped robots. The NMPC is used in order to imitate some properties of human walking, which is optimal and uses some basic goals and constraints, yielding safe and stable walking. Since there may be some uncertainties in the dynamics or parameters variations in the biped model, the controller robustness is also considered. However, the NMPC is a model based controller; this characteristic reduces the effectiveness of the NMPC based controlling. In order to overcome this shortcoming of the NMPC, the nonlinear disturbance observer (NDO) will be used to robustify the proposed controller against dynamic uncertainties in the biped robot and rejecting external disturbances. Simulation results reveal better performance of the nonlinear-disturbance-observer-based NMPC as compared to the previously reported NMPC controllers.

Aiming at the high-precision trajectory tracking problem of the new surface and underwater joint observation system (SUJOS) in the ocean remote sensing monitoring mission under complex sea conditions, especially at the problem of excessive tracking errors and slow convergence of actual trajectory oscillations caused by the wide range of angular changes in the motion trajectory, a real-time optimization improved model predictive control (IMPC) trajectory tracking method based on fuzzy control is proposed. Initially, the novel observation platform has been designed, and its mathematical model has been systematically established. In addition, this study optimizes the MPC trajectory tracking framework by integrating the least squares adaptive algorithm and the Extended Alternating Direction Method of Multipliers (EADMM). In addition, a fuzzy controller, optimized using a genetic algorithm, an output of real-time optimization coefficients, is employed to dynamically adjust and optimize the bias matrix within the objective function of the IMPC. Consequently, the real-time performance and accuracy of the system’s trajectory tracking are significantly enhanced. Ultimately, through comprehensive simulation and practical experimental verification, it is demonstrated that the real-time optimization IMPC algorithm exhibits commendable real-time and optimization performance, which markedly enhances the accuracy for trajectory tracking, and further validates the stability of the controller.