Trajectory Tracking Based on Adaptive Sliding Mode Control for Agricultural Tractor

Adaptive sliding mode control with pre-specified performance settings for AUV’s trajectory tracking

<div class="section abstract"><div class="htmlview paragraph">Trajectory tracking control is a critical component of the autopilot system, essential for achieving high-performance autonomous driving. This paper presents the design of a stable, reliable, accurate, fast, and robust trajectory tracking controller. Specifically, a lateral and longitudinal trajectory tracking controller based on a linear parameter time-varying model predictive control (LPV-MPC) framework is designed. Firstly, a three-degree-of-freedom vehicle dynamics model and a tracking error model are established. Secondly, a multi-objective function and constraints considering tracking accuracy and lateral stability are formulated, and the quadratic programming (QP) method is employed to solve the optimization problem. Finally, PID speed tracking control is introduced in the longitudinal control scheme for comparison with the proposed MPC longitudinal speed control. A step velocity tracking test validates the effectiveness of the MPC speed tracking controller. In the lateral control scheme, double lane change and circumferential tests are designed to assess the comprehensive performance of the controller under medium-low adhesion road conditions. Additionally, the trajectory tracking performance of the proposed MPC controller is evaluated by comparing it with traditional Sliding Mode Control (SMC) and Linear Quadratic Regulator (LQR) trajectory tracking controllers. The simulation results indicate that the maximum heading error of the MPC controller is less than 0.09 rad, the heading error remains within 0.05 rad, and the front wheel angle stabilizes within 7.5 degrees under both operating conditions. Furthermore, the MPC controller ensures tracking accuracy and smoothness without any obvious jitter while minimizing control inputs to achieve optimal control, thereby significantly enhancing the accuracy and stability of the vehicle's trajectory tracking.</div></div>

Differential‐drive wheeled mobile robot (DDWMR) offers excellent maneuverability, making it increasingly valuable to researchers in recent years, but the control poses major challenges due to the nonlinearity nature of the system. This study proposes an integrated scheme consisting of a backstepping controller and an adaptive sliding‐mode controller (ASMC) to address these challenges including the DDWMR being subject to external disturbances and uncertainties. The presented DDWMR design is lightweight, structurally robust, and mechanically simple with a total wight of about 10 kg. The new control scheme can achieve accurate trajectory tracking while ensuring dynamic stability of the DDWMR. Performance comparison of trajectory tracking was made on the approach with that of proportional‐integral‐derivative (PID) controller and sliding mode controller (SMC). Compared to a SMC + backstepping controller, simulation results revealed that performance improvements in trajectory tracking reached root‐mean‐square errors (RMSE) reduction by 3.7% in the X‐axis, and by 23.1% in the Y‐axis, respectively. Finally, experimental results verified that the proposed controller outperformed both traditional controllers by achieving a reduction of RMSE by 12.1% in the X‐axis and by 19.1% in the Y‐axis, respectively in comparison with the SMC + backstepping controller in a real‐world application.

This paper explores lane changing trajectory planning and tracking control for intelligent vehicle on curved road. A novel arcs trajectory is planned for the desired lane changing trajectory. A kinematic controller and a dynamics controller are designed to implement the trajectory tracking control. Firstly, the kinematic model and dynamics model of intelligent vehicle with non-holonomic constraint are established. Secondly, two constraints of lane changing on curved road in practice (LCCP) are proposed. Thirdly, two arcs with same curvature are constructed for the desired lane changing trajectory. According to the geometrical characteristics of arcs trajectory, equations of desired state can be calculated. Finally, the backstepping method is employed to design a kinematic trajectory tracking controller. Then the sliding-mode dynamics controller is designed to ensure that the motion of the intelligent vehicle can follow the desired velocity generated by kinematic controller. The stability of control system is proved by Lyapunov theory. Computer simulation demonstrates that the desired arcs trajectory and state curves with B-spline optimization can meet the requirements of LCCP constraints and the proposed control schemes can make tracking errors to converge uniformly.

Adaptive model-parameter-free nonsingular fixed-time sliding mode control for underwater cleaning vehicle

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

In this paper, we propose a Takagi-Sugeno-Kang (TSK) fuzzy model for a four-Mecanum-wheel omni-directional mobile free-base (4MW-ODMFB) with system uncertainties and external disturbances based on the model given in [12], [13], and then design an adaptive sliding-mode control (ASMC) system for trajectory tracking of 4MW-ODMFB, which combines the techniques of adaptive control and sliding-mode control. By using Lyapunov method we prove that the ASMC system is global asymptotic stable. We also conduct simulation experiments in MATLAB environment to test our TSK fuzzy model and the ASMC method. All the simulation results demonstrate the effectiveness of the proposed TSK fuzzy model and the stability and robustness of ASMC system.

PurposeThe purpose of this paper is to enhance the quadrotor’s capability of short-distance delivery to satisfy the large demand for quadrotor, which is used for goods distribution in huge warehouses, under time-varying payload and external wind disturbance.Design/methodology/approachA trajectory tracking controller design based on the combination of an adaptive sliding mode control (ASMC) method and the active disturbance rejection control (ADRC) technique is proposed. Besides, an inner–outer loop control system structure is adopted.FindingsSimulation results of different trajectory tracking verify the effectiveness and robustness of the proposed tracking control method under various conditions, including parameter uncertainty and external wind disturbance. The proposed control strategy ensures that quadrotor UAV is capable of tracking linear and spiral trajectory well whether it loads or unloads goods in the presence of the external wind disturbance.Originality/valueThe proposed method of designing a trajectory tracking controller is based on an integral ADRC and ASMC scheme so as to deal with the trajectory tracking problem for a quadrotor with payload variation.

In this chapter an adaptive sliding mode controller for the Furuta pendulum is proposed. The Furuta pendulum is a class of underactuated mechanical systems commonly used in many control systems laboratories due to its complex stabilization which allows the analysis and design of different nonlinear and multivariable controllers that are useful in some fields such as aerospace and robotics. Sliding mode control has been extensively used in the control of mechanical systems as an alternative to other nonlinear control strategies such as backstepping, passivity based control etc. The design and implementation of an adaptive sliding mode controller for this kind of system is explained in this chapter, along with other sliding mode controller variations such as second order sliding mode (SOSMC) and PD plus sliding mode control (PD \(+\) SMC) in order to compare their performance under different system conditions. These control techniques are developed using the Lyapunov stability theorem and the variable structure design procedure to obtain asymptotically stable system trajectories. In this chapter the adaptive sliding mode consist of a sliding mode control law with an adaptive gain that makes the controller more flexible and reliable than other sliding mode control (SMC) algorithms and nonlinear control strategies. The adaptive sliding mode control (ASMC) of the Furuta pendulum, and the other SMC strategies shown in this chapter, are derived according to the Furuta’s pendulum dynamic equations making the sliding variables, position errors and velocity errors reach the zero value in a specified reaching time. The main reason of deriving two well known sliding mode control strategy apart from the proposed control strategy of this chapter (adaptive sliding mode control) is for comparison purposes and to evince the advantages and disadvantages of adaptive sliding mode control over other sliding mode control strategies for the stabilization of the Furuta pendulum. A chattering analysis of the three SMC variations is done, to examine the response of the system, and to test the performance of the ASMC in comparison with the other control strategies explained in this chapter.

In most extant flight trajectory tracking control schemes, such as sliding mode control, gain parameter selection is time-consuming owing to the lack of explicit methods or rationales for the selection. Therefore, we herein propose a simple and efficient parameter selection method for sliding mode control. To demonstrate the effectiveness of the proposed method, we designed several types of trajectory tracking control laws for a rotorcraft. We derived the relation between gain parameters and desired damping ratio/desired natural frequency by utilizing modal analysis to transform trajectory tracking error as a second order system with respect to position and heading angle. Thus, we rationally determined gain parameters based on the expected system response, which corresponded to the classical response expected in a second order system. After implementing our method to the trajectory tracking control law for a Bo-105 rotorcraft in simulations, we determined that tracking performances can be upgraded or degraded based on damping ratio and natural frequency. In our controller design, as the desired damping ratio increased, although no significant differences exist at high damping ratio, undesired oscillatory responses disappeared. Further, the effects of variation in desired natural frequencies are not as significantly evident as those due to variation in damping ratio. However, the effects of desired natural frequency can be observed in the heading angle of the current trajectory tracking control design; as the natural frequency increased, the tracking accuracy increased. Therefore, the proposed method can serve as a guideline for tuning many parameters involved in the design of an advanced control law.KeywordsRotorcraftTrajectory tracking controlSliding mode controlGain selection

We propose a novel data‐driven model‐free adaptive terminal sliding mode control (SMC) for a class of discrete nonlinear systems with perturbations in which tracking error constraints are taken into account in improving tracking accuracy. The goal of this design is that the tracking error trajectory is maintained within the prescribed convergence region which is defined by the performance function, while the disturbances exist. In explaining the advantage of this proposed control scheme, at first, by theoretical calculation, it is proved theoretically that the quasi‐sliding mode can be achieved in a limited time under the specified tracking performance. Furthermore, comparative investigation results are given to validate the effectiveness of the proposed control scheme. Simulation results show that the proposed strategy is superior to model‐free adaptive control, model‐free adaptive terminal SMC, and data‐driven adaptive SMC with prescribed performance control algorithms in terms of tracking accuracy and response speed, which demonstrates the efficiency of the proposed scheme.

The robotic airship can be used as an aerostatic platform for many potential applications, for example, communication, hovering payload deliveries, data-gathering for research studies, etc. These applications require a fully autonomous perspective of an airship. One of the important aspects of airship autonomy is trajectory tracking control. An airship has complex and uncertain nonlinear dynamics which pose a major challenge for designing a precise trajectory tracking control. This paper addresses the airship trajectory tracking control problem under model uncertainties and wind disturbance. We propose a lumped model uncertainties and wind disturbance estimation approach based on an unscented Kalman filter. The estimated lumped uncertainty is used by the Sliding Mode Controller (SMC) for ultimate control of airship trajectory tracking. This comprehensive algorithm, Unscented Kalman filter-based Sliding Mode Controller (USMC), is used as a robust adaptive control solution to track the desired trajectory. The stability and convergence of the proposed method are investigated using the Lyapunov stability analysis. Simulation results show that the proposed method efficiently tracks the desired trajectory. The method solves the stability, convergence, and chattering problem of SMC without the bound constraint of model uncertainties and wind disturbance.

This article studies the trajectory tracking of an underactuated unmanned surface vehicle. The trajectory tracking controller is divided into two parts: the kinematics controller and the dynamic controller. Taking the hull coordinate system of the unmanned surface vehicle as the datum plane, the kinematics controller is designed based on the backstepping method, and the virtual control input of the speed and the heading deviation of the ship are obtained. A dynamic controller is designed through an exponential sliding surface to stabilize the error of speed and heading deviation, thereby stabilizing the position error. In the process of designing the controller, the stability of the entire closed-loop control system is analyzed using Lyapunov stability theory. Finally, the designed numerical simulation experiment is carried out. The proposed exponential sliding mode controller is compared with the global integral sliding mode controller, and it is verified that the proposed controller has better effectiveness and robustness in the trajectory tracking of the underactuated unmanned surface vehicle.

This study develops the application of adaptive sliding mode control to earthquake-excited uncertain structures. Adaptive sliding mode control is useful as it only requires the boundness feature of uncertainties and disturbances to determine control gain (and not the amplitude of bounds). Moreover, the amplitude of chattering effect, the main drawback of sliding mode control, attenuates while stability and robustness are preserved. For this purpose, two adaptive-gain control algorithms are considered. In addition, a time-varying boundary layer is considered to increase the accuracy. In order to examine the feasibility of the applied method, a three-story benchmark structure is considered. Furthermore, performance indexes are used to evaluate the effectiveness of adaptive sliding mode control in comparison to sliding mode control and fuzzy sliding mode control. Comparison of the simulation results in accordance to performance indexes demonstrates that adaptive sliding mode control not only attenuates the amplitude of control effort (i.e. chattering amplitude) but also makes the control of the maximum response of the structure applying adaptive sliding mode control more convenient. It is shown that displacement and drift performance indexes of adaptive sliding mode control are equal to or less than ones of sliding mode control, while root mean square of the control effort of adaptive sliding mode control is attenuated around 50%.

Due to the omnidirectional mobile robot's maneuverability, stability and its relatively simple control, it is used in wide range of applications. The aim of this paper is to develop a sliding mode controller that enables a three wheeled omnidirectional mobile robot to perform trajectory tracking tasks based on the robot's kinematic model. The performance of the sliding mode controller is illustrated by way of some simulation scenarios. The paper also provides a simulation software package, which is designed to allow users to investigate the trajectory tracking control problem for an omnidirectional mobile robot without the need to go through the cumbersome process of kinematic modelling and the sliding mode control design.