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

Robust adaptive three-dimensional trajectory tracking control for unmanned underwater vehicles with disturbances and uncertain dynamics

The control of an orbital space robot is challenging due to the strong nonlinear dynamic coupling between the floating base spacecraft and the equipped manipulator. To address this problem effectively, this paper develops a geometric control framework by identifying and exploiting the Lie group structures of the space robot. The paper shows how to formulate the system momentum evolution equations as a set of first-order ordinary differential equations. Then, it discusses the designs of the Lie-algebra proportional-integral controller and the manifold model predictive controller to perform the three-dimensional pose trajectory tracking task. For the manifold model predictive controller, the paper presents the structure-preserving direct-collocation method to enforce the discrete dynamic constraints in a finite-horizon optimal control problem. Furthermore, it presents the performance comparisons of the above two controllers in numerical simulations, and emphasizes the significance of computational accuracy and efficiency, momentum shaping and prediction horizon selection for the manifold model predictive controller, with detailed benchmarks against the classic Euclidean model predictive controller. Finally, the paper demonstrates the trajectory tracking and object capturing experiments in a three-dimensional space via an air-bearing space robot simulator.

Adaptive fixed-time backstepping control for three-dimensional trajectory tracking of underactuated autonomous underwater vehicles

Robust adaptive three-dimensional trajectory tracking control scheme design for small fixed-wing UAVs

Four-mecanum-wheeled omnidirectional mobile robots (FMOMR) are widely used in many practical scenarios because of their high mobility and flexibility. However, the performance of trajectory tracking would be degenerated largely due to various reasons. To deal with this issue, this paper proposes a data-driven algorithm by using the T-S fuzzy quaternion-value neural network (TSFQVNN). TSFQVNN is tailored to obtain the controlled autoregressive integral moving average (CARIMA) model, and then the generalized predictive controller (GPC) is designed based on the CARIMA model. In this way, the spatial relationship between the three-dimensional pose coordinates can be preserved and training times can be reduced. Furthermore, the convergence of the proposed algorithm is verified by the Stone–Weierstrass theorem, and the convergence conditions of the algorithm are discussed. Finally, the proposed control scheme is applied to the three-dimensional (3D) trajectory tracking problem on the arc surface, and the simulation results prove the necessity and feasibility of the algorithm.

Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.

Flexibility and aeroelastic behaviors in large space structures can lead to degradation of control system stability and performance. The model reference adaptive notch filter is an effective methodology used and implemented for reducing such effects. In this approach, designing a model reference for adaptive control algorithm in a flight device such as a launch vehicle is very important. In this way, the vibrations resulting from the structure flexibility mostly affects the pitch channel, and its influences on the yaw channel are negligible. This property is used and also the symmetrical behavior of the yaw and pitch channels. In this paper, by using this property and also the symmetrical behavior of the yaw and pitch channels, a new model reference using identification on the yaw channel is proposed. This model behaves very similar to the rigid body dynamic of the pitch channel and can be used as a model reference to control the vibrational effects. Simulation results illustrated applies the proposed algorithm and considerably reduces the vibrations in the pitch channel. Moreover, the main advantage of this new method is the online tuning of the model reference against unforeseen variations in the parameters of the rigid launch vehicle, which has not been considered in the previous works. Finally, robustness of the new control system in the presence of asymmetric behavior is investigated.

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.

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.

This article investigates the three-dimensional trajectory tracking control problem for an underactuated autonomous underwater vehicle in the presence of parameter perturbations and external disturbances. An adaptive robust controller is proposed based on the velocity control strategy and adaptive integral sliding mode control algorithm. First, the desired velocities are developed using coordinate transformation and the backstepping method, which is the necessary velocities for autonomous underwater vehicle to track the time-varying desired trajectory. The bioinspired neurodynamics is used to smooth the desired velocities, which effectively avoids the jump problem of the velocity and simplifies the derivative calculation. Then, the dynamic control laws are designed based on the adaptive integral sliding mode control to drive the underactuated autonomous underwater vehicle to sail at the desired velocities. At the same time, the auxiliary control laws and the adaptive laws are introduced to eliminate the influence of parameter perturbations and external disturbances, respectively. The stability of the control system is guaranteed by the Lyapunov theorem, which shows that the system is asymptotically stable and all tracking errors are asymptotically convergent. At the end, numerical simulations are carried out to demonstrate the effectiveness and robustness of the proposed controller.

To address the search-and-docking problem in multi-stage prescribed performance switching (MPPS) scenarios, this paper presents a novel compound control method for three-dimensional (3D) underwater trajectory tracking control of unmanned underwater vehicles (UUVs) subjected to unknown disturbances. The proposed control framework can be divided into two parts: kinematics control and dynamics control. In the kinematics control loop, a novel parallel model predictive control (PMPC) law is proposed, which is composed of a soft-constrained model predictive controller (SMPC) and hard-constrained model predictive controller (HMPC), and utilizes a weight allocator to enable switching between soft and hard constraints based on task goals, thus achieving global optimal control in MPPS scenarios. In the dynamics control loop, a finite-time terminal sliding mode control (FTTSMC) method combining a finite-time radial basis function neural network adaptive disturbance observer (RBFNN-FTTSMC) is proposed to achieve disturbance estimation and fast convergence of velocity tracking errors. The simulation results demonstrate that the proposed PMPC-FTTSMC approach achieved an average improvement of 33% and 80% in the number of iterations compared with MPC with sliding mode control (MPC-SMC) and traditional MPC methods, respectively. Furthermore, the approach improved the speed of response by 35% and 44%, respectively, while accurately achieving disturbance observation and enhancing the system robustness.

In this article, a multiple structure radial basis function neural networks (MSRBFNN) model which contain a forward network and a recurrent network for USV is presented, whose parameters can be adjusted currently in the training process. Next, the nonlinear model predictive control based on MSRBFNN model is presented and the trajectory tracking with obstacle avoidance algorithms are designed. Finally, to validate the effectiveness and safety of our proposed control system, the USV dynamic model and its control system were established on the MATLAB simulation platform, and the MSRBFNN model identification experiments and MSRBFNN-based MPC trajectory tracking control simulations were conducted on this platform. These simulations verified the effectiveness of the controller and ensured the collision avoidance of the USV.

<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>

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.

Motivated by formation control of multiple non-holonomic mobile robots, this paper presents a trajectory tracking control scheme design for nonholonomic mobile robots that are equipped with low-level linear and angular velocities control systems. The design includes a nonlinear kinematic trajectory tracking control law and a tracking control gains selection method that provide a means to implement the nonlinear tracking control law systematically based on the dynamic control performance of the robot's low-level control systems. In addition, the proposed scheme, by design, enables the mobile robot to execute reference trajectories that are represented by time-parameterized waypoints. This feature provides the scheme a generic interface with higher-level trajectory planners. The trajectory tracking control scheme is validated using an iRobot Packbot's parameteric model estimated from experimental data.