Path Planning and Tracking Control for Unmanned Vehicle Using Multi-dimensional Taylor Network

With the growing demand for safe obstacle avoidance and precise trajectory tracking in the autonomous navigation of unmanned surface vessels (USVs), this paper investigates an adaptive differential evolution approach for integrated path planning and tracking control. In the path planning stage, an elite archive mechanism is first incorporated into the mutation process, and the scaling factor F and crossover rate CR are adaptively adjusted to enhance population diversity and global search capability. Then, the International Regulations for Preventing Collisions at Sea (COLREGs) are embedded into the algorithmic framework to reinforce collision avoidance performance in complex encounter scenarios. A multi-objective fitness function combining six performance criteria is subsequently constructed to evaluate individual path points, thereby identifying high-quality solutions that ensure both safe navigation and route efficiency. In the tracking control stage, the optimally generated reference trajectory is then employed as the input command for the vessel’s motion control subsystem. A fuzzy logic system is introduced to approximate unknown nonlinear dynamics, and an adaptive fuzzy logic controller is designed to guarantee accurate tracking of the planned path. Finally, simulation tests are used to show the algorithm’s efficiency and usefulness.

In this paper, we present an integrated dynamic path planning and trajectory tracking control strategy for electric vertical take-off and landing (eVTOL) unmanned aerial vehicles (UAVs) by using a receding horizon optimization (RHO) approach. Considering that the effective sensing range of onboard sensors is practically short, we formulate the path planning into minimum curvature-based RHO problems with a polynomial path template. By adding the contraction constraint derived from the backstepping controller, the proposed nonlinear model predictive control (NMPC), as an online optimization scheme, guarantees the closed-loop stability. It also achieves the optimal control performance by minimizing the tracking error and power consumption. The experimental results of the dynamic path planning and trajectory tracking demonstrate the effectiveness of the proposed control algorithm.

Unmanned vehicles are currently facing many difficulties and challenges in improving safety performance when running in complex urban road traffic environments, such as low intelligence and poor comfort performance in the driving process. The real-time performance of vehicles and the comfort requirements of passengers in path planning and tracking control of unmanned vehicles have attracted more and more attentions. In this paper, in order to improve the real-time performance of the autonomous vehicle planning module and the comfort requirements of passengers that a local granular-based path planning method and tracking control based on multi-segment Bezier curve splicing and model predictive control theory are proposed. Especially, the maximum trajectory curvature satisfying ride comfort is regarded as an important constraint condition, and the corresponding curvature threshold is utilized to calculate the control points of Bezier curve. By using low-order interpolation curve splicing, the planning computation is reduced, and the real-time performance of planning is improved, compared with one-segment curve fitting method. Furthermore, the comfort performance of the planned path is reflected intuitively by the curvature information of the path. Finally, the effectiveness of the proposed control method is verified by the co-simulation platform built by MATLAB/Simulink and Carsim. The simulation results show that the path tracking effect of multi-segment Bezier curve fitting is better than that of high-order curve planning in terms of real-time performance and comfort.

Integrated path planning and control through proximal policy optimization for a marine current turbine

MTN optimal control of MIMO non-affine nonlinear time-varying discrete systems for tracking only by output feedback

In this study, the problem of adaptive multi‐dimensional Taylor network (MTN) control for single‐input single‐output (SISO) uncertain stochastic non‐linear systems is investigated. How to minimise the influence of randomness and uncertain non‐linearity for less complex computation, and how to improve the real‐time performance of the controller are of great significance. To this end, a control approach based on MTN is proposed for tracking control of stochastic non‐linear systems. MTNs are used to approximate the non‐linearities, and the backstepping technique is employed to construct the MTN controller (MTNC). MTNC involves only addition and multiplication, featuring desirable simplicity and real‐time performance. Stability of the system is guaranteed via Lyapunov approach, and it is proved that the proposed controller can guarantee that all signals of the closed‐loop system remain bounded in probability, and the tracking error converges to an arbitrarily small neighbourhood around the origin. Finally, a numerical example is given to illustrate the effectiveness of the proposed design approach, and simulation results demonstrate that the method presented in this study has good real‐time performance and control quality.

Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

Obstacle avoidance problem for the autonomous vehicle has received more and more attention these years, especially for the autonomous heavy vehicle, which has the high gravity center, large carrying capacity and promising commercial prospect. However, the vehicle dynamic properties have been rarely considered for path planning, which is a crucial factor for driving safety. An integrated collision avoidance framework that consists of a new path planner and a tracking controller considering multiple dynamic constraints is proposed for an autonomous multi-axis distributed vehicle. In the upper level, a new optimal path generator based on the B-splines is designed for safety and stability purposes, and the collision avoidance path is optimized considering multiple dynamic constraints, including the tire nonlinear dynamic, lateral stability limits and the anti-slip constraints. Meanwhile, a safety assessment evaluator according to the time to collision calculation is designed for risk level recognition and path planning weight determination. In the lower level, a path tracking controller based on the nonlinear model predictive control (NMPC) is designed to improve the control accuracy, and a high-fidelity nonlinear dynamic predictive vehicle model with 16 degrees of freedom is established to depict the vehicle dynamic properties comprehensively. To solve the nonlinear optimization problem and get the optimal control steering angle output, an adaptive weight adjustment strategy and a varying predictive duration method are formulated. The optimization constraints related to steering actuator ability, plane stability limits, rollover prevention thresholds and tire adhesion requirements are constructed and converted as the bounds of the vehicle dynamic states. To verify the proposed framework, the simulation and HIL test platform are built and different driving scenarios are validated. The test results show the satisfactory performance of path planning and path tracking considering the dynamic constraints for safety and stability

Differential-steering based path tracking control and energy-saving torque distribution strategy of 6WID unmanned ground vehicle

Path planning and tracking control are important modules of mobile robots, which have outstanding advantages in improving the flexibility of mobile robots. However, in the existing applications, these two modules are usually processed separately, which makes the obstacle avoidance process of the mobile robot uncontrollable and reduces the adaptability of the application scenario. In order to realize collision-free high-precision trajectory tracking control of mobile robots in dynamic scenes, this paper proposes an adaptive MPC control method based on curvature constraints, which can improve the safety of operation and reduce the chattering caused by unreasonable paths in the process of operation. Firstly, according to the kinematic constraints of general mobile robots, the constraint model of mobile robot path planning is constructed. At the same time, the adaptive parameter adjustment rules are adopted to realize the balance between the calculation efficiency and the control cycle. Finally, a smooth safe path is obtained by re-fitting the planned path and reprocessing the nodes with excessive curvature. Compared with traditional methods, the proposed method has better advantages.

Aiming to address the tracking accuracy and anti-rollover problem of the unmanned mining truck path tracking process under the complex unstructured road conditions in mining areas, a coordinated control strategy for path tracking and anti-rollover based on topology theory is proposed. Moreover, optimal equilibrium weights are assigned to path tracking control and anti-rollover control to ensure that the path tracking accuracy of the mining vehicle can be effectively improved in a safe and stable driving state. Regarding the path tracking problem, a lateral preview error model is established, and a path tracking controller is designed using LQR (linear quadratic regulator) control theory. In the design of the anti-rollover controller, the effects of understeer and trip-type rollover on the stability of the vehicle are taken into account, and the ideal transverse swing angular velocity and trip-type rollover evaluation index are introduced for controller design, which reduce the effects of the curves and roadway excitation on the mining truck and improve the rollover motion. Based on a joint simulation using Trucksim and Simulink and the construction of a hardware-in-the-loop simulation platform for verification, the single control strategy and coordinated control strategy are compared and analyzed. The final simulation results show that the tracking error, yaw velocity, and center of mass side deviation angle are optimized by 45%, 32.5%, and 20%, respectively. Therefore, the Extension theory-based coordinated controller satisfies the complex road conditions in the mining area and improves the tracking accuracy to the maximum extent while ensuring the safety and smoothness of vehicle driving and exhibiting good adaptability and robustness.

For switched nonlinear systems with output hysteresis, by working on multi-dimensional Taylor network (MTN), an adaptive control strategy is put forward, which can effectively solve the tracking control problem. Firstly, the modified Bouc–Wen hysteresis model is employed to cope with the nonlinear problem caused by output hysteresis. Secondly, the Nussbaum function is introduced into the control design process, and MTN is used to approach nonlinear structures. Thirdly, a novel network adaptive control strategy is constructed with the help of a common Lyapunov function, which can realize the tracking control and stability of the system simultaneously. It is worth noting that as a new type of neural network, MTN is first used for control of switched systems with output hysteresis. Finally, two simulation examples show that the proposed control strategy is effective.

In order to realize the path following control of the robot in the complex agricultural environment, an intelligent path following control method based on fuzzy PD (proportional derivative) is proposed. Firstly, in order to realize the task of path tracking and automatic steering control, the automatic navigation controller and the automatic steering method based on fuzzy PD control are constructed. The method of driving the robot hardware platform based on plant protection unmanned, developed automatic navigation control system based on laser radar, angle sensor, then according to the structure and control principle of automatic navigation control system, the robot kinematics model, two wheel vehicle control model, pure path tracking control method, finally, has carried on the the design of the tracking control principle and fuzzy PD control method is verified by MATLAB/simulink simulation platform. The results show that the maximum tracking error is only 5cm, and the average tracking error is 1.2cm when the robot tracks with 1.5m/s. The simulation and experimental results show that the control system can effectively control the robot walking on a predetermined path.

In this paper, a method of tank gun stability control system based on multi-dimensional Taylor network optimal control(MTN) is proposed. As the fact that tank itself has a strong nonlinear characteristic, which will lead to the traditional effects such as PID control is not satisfactory. Multi-dimensional Taylor network controller, which only relies on the output of the system, it combines the new controller structure with the classical feedback system architecture. It is not only suitable for analyzing the stability of closed-loop system, but also for control of the nonlinear systems with the mechanism known or unknown models. The virtual prototype of tank is used as the controlled plant to test the actual control effect of the MTN. And simulation results have shown the effectiveness of the proposed method.

<abstract> <bold>Abstract. </bold>This paper focuses on path tracking and vibration reducing velocity control inside strawberry field furrows. A path tracking controller is developed which maintains the robot chassis in the center of the furrow and parallel to the furrow walls by minimizing the heading error between the robot’s velocity vector and a look ahead point placed at a constant distance ahead of the robot in the center of the furrow. This type of control is based on a method known as pure pursuit. The velocity controller developed implements a finite state machine which controls the velocity based on the vibration energy. When the vibration is higher or lower than different vibration thresholds, the velocity will be changed accordingly. The results of the experiments demonstrate that by reducing velocity when the robot enters an uneven terrain, the vibration energy of the robot stays within the range that it was in when the robot was on the even terrain. Furthermore, the results indicate that the path tracking controller was able to maintain a lateral error of less than 2 centimeters from the center of a 50 centimeter wide furrow and heading error of less than 3 degrees. Future experiments will include determining the threshold a fragile crop can experience without bruising damage so that a maximum velocity that will maintain the integrity of the crop can be determined.