Tracking Control Of Vehicles Research Articles

Simple adaptive trajectory tracking control of underactuated autonomous underwater vehicles under LOS range and angle constraints

In this study, the authors propose a simple adaptive trajectory tracking control scheme for underactuated autonomous underwater vehicles (AUVs) subject to unknown dynamic parameters and disturbances under asymmetric time-varying line-of-sight (LOS) range and angle constraints. A simple error mapping function is constructed to transform the LOS range and angle constraint problems of AUVs trajectory tracking into the problem of the boundedness of transform variable, such that any constraint boundaries of LOS range and angle can be handled. The compounded uncertain item caused by the unknown dynamic parameters and disturbances is transformed into a linear parametric form with only three unknown parameters called virtual parameters. Based on the above, a simple adaptive trajectory tracking control law is developed using dynamic surface control technique, where the adaptive laws online provide the estimations of virtual parameters. As a result, the proposed trajectory tracking control scheme is simple to compute and easy to implement in engineering applications. Strict stability analysis indicates that the designed control law makes AUVs track the desired trajectory within the LOS range and angle constraints and guarantees the boundedness of all signals of the closed-loop control system. Simulation results and comparison verify the effectiveness of the designed control scheme.

Three-dimensional trajectory tracking control of underactuated autonomous underwater vehicles

Three-dimensional trajectory tracking control of underactuated autonomous underwater vehicles

Three-dimensional trajectory tracking control of underactuated autonomous underwater vehicles

This study proposes a three-dimensional (3D) trajectory-tracking control scheme for an underactuated autonomous underwater vehicle (AUV). Given the 3D reference trajectory, the reference velocities, angles, angular velocities, force, and torques were planned first. These reference variables were used to obtain the error dynamics. The backstepping technique was used to design the trajectory-tracking controller for tracking the AUVs reference trajectory. According to the Lyapunov stability theory, the trajectory-tracking system was stable and bounded, and the tracking errors converged close to a small neighbourhood of zero. Finally, the effectiveness of the developed control method was demonstrated using simulations.

3-D Flight Path Tracking Control for Unmanned Aerial Vehicles Under Wind Environments

This paper provides a new design framework for 3D flight path tracking control of unmanned aerial vehicles (UAVs) under wind environments. The new design framework simultaneously achieves the following three points: (i) 3D path tracking error system representation using the Serret-Frenet frame under wind environments, (ii) guaranteed cost control, (iii) simultaneous stabilization by a single controller for different 3D paths with a common interval parameter setting in the Serret-Frenet frame. To realize the three points, a path tracking error system based on the 3D kinematic model of UAVs under wind environments is constructed in the Serret-Frenet frame. The path tracking error system is exactly represented with the Takagi-Sugeno (T-S) fuzzy model in considered operation domains. As an advantage of the T-S fuzzy model construction, this paper considers a guaranteed cost controller design that minimizes the upper bound of a given performance function. The guaranteed cost controller design problem is cast in terms of linear matrix inequalities (LMIs). Thus, the designed controller guarantees not only the path stabilization but also the guaranteed cost control even in path tracking control for a given desired 3D flight path under wind environments. In addition, a simultaneous stabilization problem is also considered in terms of finding a common solution in a set of the LMIs. The simulation results show the utility of the proposed 3D flight path tracking control under wind environments.

Trajectory Tracking Control for Autonomous Underwater Vehicles Based on Fuzzy Re-Planning of a Local Desired Trajectory

This paper investigates the trajectory tracking control problem for autonomous underwater vehicles whose initial starting position differs substantially from that specified by the desired trajectory. This scenario is very likely to cause serious chattering in the control output, especially in the early stages, when the large tracking error is the input to the controller. A novel trajectory tracking control strategy is developed based on fuzzy re-planning of a local desired trajectory. At each time instant, a local desired trajectory is reconstructed based on the AUV's current position and that specified by the original desired trajectory at a future time. Also the control effort is computed based on the local desired trajectory, rather than the original one. Moreover, the interval between each time instant is determined by a new single-input fuzzy model, where the input is determined by the distance between AUV's current and desired trajectories and the change in the distance. Finally, the effectiveness of the new control strategy is verified by simulation-based case studies using an actual vehicle model as a necessary step prior to experimental validation.

Trajectory tracking for autonomous vehicles on varying road surfaces by friction-adaptive nonlinear model predictive control

ABSTRACTWe propose an adaptive nonlinear model predictive control (NMPC) for vehicle tracking control. The controller learns in real time a tyre force model to adapt to a varying road surface that is only indirectly observed from the effects of the tyre forces determining the vehicle dynamics. Learning the entire tyre model from data would require driving in the unstable region of the vehicle dynamics with a prediction model that has not yet converged. Instead, our approach combines NMPC with a noise-adaptive particle filter for vehicle state and tyre stiffness estimation and a pre-determined library of tyre models. The stiffness estimator determines the linear component of the tyre model during normal vehicle driving, and the control strategy exploits a relation between the tyre stiffness and the nonlinear part of the tyre force to select the appropriate full tyre model from the library, which is then used in the NMPC prediction model. We validate the approach in simulation using real vehicle parameters, demonstrate the real-time feasibility in automotive-grade processors using a rapid prototyping unit, and report preliminary results of experimental validation on a snow-covered test track.

FPGA-Based Mechatronic Design and Real-Time Fuzzy Control with Computational Intelligence Optimization for Omni-Mecanum-Wheeled Autonomous Vehicles

This study presents a field-programmable gate array (FPGA)-based mechatronic design and real-time fuzzy control method with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles. With the advantages of cuckoo search (CS), an evolutionary CS-based fuzzy system is proposed, called CS-fuzzy. The CS’s computational intelligence was employed to optimize the structure of fuzzy systems. The proposed CS-fuzzy computing scheme was then applied to design an optimal real-time control method for omni-Mecanum-wheeled autonomous vehicles with four wheels. Both vehicle model and CS-fuzzy optimization are considered to achieve intelligent tracking control of Mecanum mobile vehicles. The control parameters of the Mecanum fuzzy controller are online-adjusted to provide real-time capability. This methodology outperforms the traditional offline-tuned controllers without computational intelligences in terms of real-time control, performance, intelligent control and evolutionary optimization. The mechatronic design of the experimental CS-fuzzy based autonomous mobile vehicle was developed using FPGA realization. Some experimental results and comparative analysis are discussed to examine the effectiveness, performance, and merit of the proposed methods against other existing approaches.

Trajectory Tracking Control of AUVs via Adaptive Fast Nonsingular Integral Terminal Sliding Mode Control

This article aims to develop an effective control method that can improve the convergence rate over the existing adaptive nonsingular integral terminal sliding mode control (ANITSMC) method for the trajectory tracking control of autonomous underwater vehicles (AUVs). To achieve this goal, an adaptive fast nonsingular integral terminal sliding mode control (AFNITSMC) method is proposed. First, considering that the existing nonsingular integral terminal sliding mode (NITSM) has slow convergence rate in the region far from the equilibrium point, a fast NITSM (FNITSM) is proposed, which guarantees fast transient convergence both at a distance from and at a close range of the equilibrium point, and therefore increases the convergence rate over the existing NITSM. Then, using this FNITSM and adaptive technique, an AFNITSMC method is designed for AUVs. It yields local finite-time convergence of the velocity tracking errors to zero and then local exponential convergence of the position tracking errors to zero, without requiring any a priori knowledge of the upper bounds of the uncertainties and disturbances. Compared with the existing ANITSMC method, the salient feature of the proposed AFNITSMC method is that it provides AUV dynamics a faster convergence rate. Finally, simulation results demonstrate the efficiency of the proposed AFNITSMC method and its superiority over the existing ANITSMC method.

Distributed adaptive fractional‐order fault‐tolerant cooperative control of networked unmanned aerial vehicles via fuzzy neural networks

This study presents a distributed fault-tolerant cooperative control (FTCC) strategy to achieve the attitude synchronisation tracking control of networked unmanned aerial vehicles (UAVs) in the presence of actuator faults and model uncertainties. By utilising the fuzzy neural networks (FNNs), the unknown non-linear terms induced by actuator faults and model uncertainties are estimated as lumped uncertainties. A set of distributed sliding-mode estimators (DSMEs) is then employed to estimate the leader UAV's attitudes for the follower UAVs via a distributed communication network. Based on the estimated knowledge from FNNs and DSMEs, a group of distributed FTCC laws is developed for all follower UAVs by using the fractional-order calculus. It is proven that with the proposed control scheme, all follower UAVs can track the attitudes of the leader UAV and the tracking errors are uniformly ultimately bounded even when a portion of networked UAVs encounters multiple actuator faults. Comparative simulation results are presented to demonstrate the effectiveness of the proposed approach.

Tracking control of multiple unmanned aerial vehicles incorporating disturbance observer and model predictive approach

This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure.

Envelope-constraint-based tracking control of air-breathing hypersonic vehicles

In this paper, a novel envelope-constraint-based neural controller is addressed for air-breathing hypersonic vehicles with unknown non-affine formulations. The vehicle dynamics is decomposed into velocity subsystem and altitude subsystem that are further described as unknown non-affine expressions. Neural networks are employed to approximate the unknown terms of each subsystem based on Mean Value Theorem, which rejects system uncertainties and non-affine characteristics. New learning algorithms are developed for adaptive parameters to stabilize the closed-loop system and reduce online parameters, yielding a low computational load design. The remarkable advantages are that complicated design procedure of back-stepping is avoided, and desired transient performance is guaranteed for velocity and altitude tracking errors via envelope constraint. The stability of closed-loop control system is proved utilizing Barrier Lyapunov functions. Finally, numerical simulation results are presented to validate the effectiveness of the proposed control approach.

Human-Centered Trajectory Tracking Control for Autonomous Vehicles With Driver Cut-In Behavior Prediction

Trajectory tracking control in the cut-in scenarios is challenging, since the autonomous vehicles have to follow the reference trajectory and cooperate with the cut-in vehicles. This paper proposes a human-centered trajectory tracking control strategy integrating driver behavior prediction for the cut-in scenarios and their transient processes. A recurrent neural network (RNN) with long short-term memory (LSTM) cells is used to predict the driver behaviors of the cut-in vehicle. Then, a model predictive control (MPC) approach considering the driver behaviors of the cut-in vehicle is designed to track the reference trajectory. The transient processes of the cut-in scenarios are considered for different cut-in behaviors. Moreover, the moving horizon estimator (MHE) is used to estimate the vehicle lateral velocity that is used in the controller. Human driver tests on a driving simulator show that the drivers’ intention of the cut-in vehicle can be predicted by the RNN with LSTM cells. CarSim® simulation studies show the human-centered trajectory tracking controller can track the reference trajectory using the estimated vehicle lateral velocity. The autonomous vehicle can cooperate with the cut-in vehicle in different driving situations and obtain smooth transient processes of the cut-in scenarios.

Research on Path Tracking Algorithm of Autopilot Vehicle Based on Image Processing

Smart cars are the result of the combination of the latest technological achievements in the fields of artificial intelligence, sensors, control science, computer, and network technology with the modern automobile industry. Intelligent cars usually have functions, such as automatic shifting, automatic driving, and automatic road condition recognition. The research of intelligent car technology involves many disciplines. This thesis focuses on the field of smart car visual navigation, focusing on image denoising, image information recognition, extraction, and pattern recognition control algorithms. The traditional trajectory tracking algorithm is mainly used in industrial computer or high-performance computer. The computational complexity leads to poor real-time control, and it is easily interfered by external complex terrain environment and internal disordered electromagnetic environment during vehicle driving. In general, on a regular basis, by the image analysis of the driver or the driver information, the image information is proposed using way trace processing technology, vehicle tracking control method and automatic driving rules. The simulation and experimental results show that the proposed control methods and rules used to carry out automatic driving vehicle are feasible. The algorithm reduces the complexity of the algorithm, improves the real-time and stability of the control and finally achieves a good trajectory tracking effect of the car on high-speed automatic driving.

Adaptive heading tracking control of surface vehicles with unknown control directions and full state constraints

This paper investigates the heading tracking control of surface vehicles in the presence of unknown control directions and full state constraints. To achieve the control objective, the neural networks and command filter-based backstepping technique are utilized to construct an adaptive controller. The Nussbaum gain technique is employed to handle the problem of unknown control directions, and all states of the system are guaranteed to remain within their constraints based on the Barrier Lyapunov functions (BLFs). The proposed control protocol can guarantee that all signals of the closed-loop system are bounded and all states of the system are ensured to remain in the predefined compact sets. The simulation results are provided to illustrate the effectiveness of the proposed control scheme.

Fuzzy Disturbance Observer-Based Adaptive Sliding Mode Control for Reusable Launch Vehicles With Aeroservoelastic Characteristic

This paper investigates the problem of attitude tracking control for reusable launch vehicles (RLVs) with aeroservoelastic characteristic and disturbances in reentry phase. An aeroservoelastic model for reentry RLV is first established, where the synthetic disturbances are considered as well. Then a novel disturbance observer combined with fuzzy logic system is constructed by using both fuzzy approximation error and observation error to provide the disturbance estimation. The fuzzy disturbance observer (FDO) can be employed to the control scheme to counteract the perturbations and guarantee the estimation errors for converging to a small neighborhood around origin. Incorporated with the designed FDOs, the adaptive sliding mode technique is proposed for developing attitude angle and angular rate subsystem controllers, respectively, to ensure the reentry attitude tracking performance. Rigorous proof shows that the uniform stability of the closed-loop system under the proposed control law can be guaranteed by using Lyapunov technique. Finally, numerical simulations are conducted in this paper to illustrate the effectiveness of the developed technique for the RLV.

Robust adaptive trajectory tracking control of underactuated autonomous underwater vehicles with prescribed performance

SummaryIn this paper, a novel robust adaptive trajectory tracking control scheme with prescribed performance is developed for underactuated autonomous underwater vehicles (AUVs) subject to unknown dynamic parameters and disturbances. A simple error mapping function is proposed in order to guarantee that the trajectory tracking error satisfies the prescribed performance. A novel additional control based on Nussbaum function is proposed to handle the underactuation of AUVs. The compounded uncertain item caused by the unknown dynamic parameters and disturbances is transformed into a linear parametric form with only single unknown parameter called virtual parameter. On the basis of the above, a novel robust adaptive trajectory tracking control law is developed using dynamic surface control technique, where the adaptive law online provides the estimation of the virtual parameter. Strict stability analysis indicates that the designed control law ensures uniform ultimate boundedness of the AUV trajectory tracking closed‐loop control system with prescribed tracking performance. Simulation results on an AUV in two different disturbance cases with dynamic parameter perturbation verify the effectiveness of our adaptive trajectory tracking control scheme.

Nussbaum-based robust tracking control of flexible air-breathing hypersonic vehicles with actuator dynamics

This paper proposes an effective control scheme to address the robust tracking control problem of the flexible air-breathing hypersonic vehicle subject to actuator dynamics. The whole vehicle dynamics is decomposed into the velocity subsystem and altitude subsystem, and the nonlinear disturbance observer based dynamic surface control is employed in the controller design within both subsystems. In order to tackle the input constraints, different hyperbolic tangent functions are carefully designed to approximate the non-smooth saturation functions. The compensators are further introduced for saturation approximations with the Nussbaum function technique. Through Lyapunov stability analysis, the closed-loop system is guaranteed to be semi-globally uniformly ultimately bounded. Moreover, the tracking and estimation errors converge to an arbitrary small neighborhood around zero. Extensive simulation and evaluations verify the superiority of the proposed scheme.

Robust vision-based tracking control of VTOL unmanned aerial vehicles

This study addresses the problem of robust tracking control of vertical take-off and landing (VTOL) unmanned aerial vehicles through a vision-based control technique. The main objective is to design a new theoretical result for vision-based tracking of a moving target in the presence of external disturbances. In particular, the proposed control framework is able to simultaneously guarantee a saturated thrust input, and measurable and bounded desired angular velocity and acceleration while dealing with time-varying disturbances in the dynamics of the system. Considering different sets of visual features, two control schemes are proposed, where the features are defined based on perspective image moments. Simulation studies are conducted in perturbed conditions to verify the effectiveness of the proposed controllers.

Command‐filtered fixed‐time trajectory tracking control of surface vehicles based on a disturbance observer

SummaryIn this paper, we investigate trajectory tracking control of surface vehicles with model parameter uncertainties and external disturbances. The disturbances due to the wind, waves, and ocean currents are combined with the model parameter uncertainties as a compound disturbance. Then, a disturbance observer (DO) is introduced to estimate the compound disturbance, which can be achieved within a finite time independent of the initial estimation error. Based on the DO and, in the context of command filtered control, a novel fixed‐time backstepping control scheme is developed, by which the vehicle can track the desired trajectory with all the states globally stabilized in a given fixed time. The effectiveness and performance of the method are demonstrated by numerical simulations.

Robust tracking control of hypersonic flight vehicles: A continuous model-free control approach

It is extremely difficult to establish precise motion model for hypersonic flight vehicles (HFVs). However, the majority of the existing control techniques focus on model-based methodologies. This paper investigates a novel model-free controller for a continuous non-affine model of HFV. Firstly, the nonaffine vehicle dynamics is transformed into a pure feedback formulation consisting of velocity subsystem and altitude subsystem. Then, a model-free tracking control law is exploited for velocity subsystem. Furthermore, the developed model-independent control technique is expanded into the altitude subsystem that is a high-order dynamic system. The proposed model-free controller contains a Hurwitz stable term and a filtering term. In addition, compensated filters are devised to further strength robustness. The special novelty is that the addressed controller doesn't require accurate affine model, exhibiting fine universal property and strong robustness. Finally, theoretical conclusions are validated and the effectiveness of the design is verified by simulation results.