An adaptive cascade trajectory tracking control for over-actuated autonomous electric vehicles with input saturation

In this paper, we investigate the trajectory tracking control problem of surface ships subject to unknown parameters, unknown time-varying disturbances, and input saturation. The input saturation is approximated using a Gaussian error function. The compounded uncertain term caused by the unknown parameters and disturbances is transformed into a linear parametric form with a single parameter called virtual parameter. Then, a novel robust adaptive ship trajectory tracking control law is designed using the adaptive vector-backstepping design method, where the adaptive law online provides the estimation of the virtual parameter. Further, a finite-time control scheme is developed injecting a power function vector of the position and velocity errors into the proposed control scheme such that the control performance is improved. Both our control schemes are simple to compute and easy to implement in engineering applications. Theoretical analysis indicates that the designed control laws can force the surface ship to track the desired trajectory while guaranteeing the global uniform ultimate boundedness of all the signals in the closed-loop trajectory tracking control system of surface ships. Simulation results and comparison verify the effectiveness of our novel robust adaptive trajectory tracking control schemes.

In this paper, we investigate an adaptive output feedback trajectory tracking controller for an underactuated unmanned surface vehicle (USV) with position signals. Using Lyapunov stability theorem and backstepping strategy, a Neural Network (NN)-based adaptive trajectory tracking control scheme is proposed without the velocity informations. The NN is employed to approximate the unknown external disturbances and model parametric uncertainties. Also, a high-gain observer is applied to estimate the USV’ unmeasured velocities, and the NN-based output feedback tracking control is designed. Simulation results are carried out to verify the proposed controller.

In this paper, an adaptive neural network (NN) trajectory tracking control scheme is developed for underactuated autonomous underwater vehicles (AUVs) subject to unknown dynamic parameters and unknown disturbances. A novel additional control based on Nussbaum function is proposed to handle the underactuation problem of AUVs. The radial basis function NNs with minimal learning parameter (MLP) are employed to online approximate the compounded uncertain item due to unknown dynamic parameters and unknown disturbances. On the basics of the above, an adaptive NN trajectory tracking control law is proposed using command filtered vector-backstepping design tool. As a result, the computational burden of the developed trajectory tracking control scheme is significantly reduced, Theoretical analysis indicates that the proposed control law can force the AUV track the desired trajectory and guarantee that all signals in the trajectory tracking closed-loop control system are bounded. Simulation results on an AUV verify the effectiveness of our developed control scheme.

Adaptive trajectory tracking control for underwater vehicle based on hierarchical compensation

The safety and optimality of underactuated autonomous underwater vehicles (AUVs) during operations are essential factors to consider. In this context, a three‐dimensional robust adaptive optimal trajectory tracking control method under position and velocity constraints, unknown dynamics, and environmental disturbances is proposed. The main features of the method are: (1) The outputs of an underactuated AUV system are redefined to handle the underactuation problem. (2) The system with position and velocity constraints is transformed into an unconstrained system by a nonlinear state‐dependent transformation. (3) A critic‐identifier architecture is constructed using adaptive dynamic programming and neural networks in a backstepping framework. Specifically, critic networks and weight update laws without requiring initial stability control are designed to solve Hamilton‐Jacobi‐Bellman equations in kinematic and dynamic subsystems, and optimal virtual and actual control laws are obtained. (4) A neural network identifier is developed to estimate unknown dynamics. Disturbances are overcome by improving the cost function and solving for optimal control of the nominal dynamic subsystem. By stability analysis, tracking errors in the AUV closed‐loop system can converge to an arbitrarily small compact set of the origin, and the other signals are uniformly ultimately bounded. Simulation comparisons demonstrate the effectiveness and superiority of the proposed method.

A quadrotor unmanned aerial vehicle (UAV) must achieve desired flight missions despite internal uncertainties and external disturbances. This paper proposes an adaptive trajectory tracking control method that attenuates unknown uncertainties and disturbances. Although the quadrotor is underactuated, a fully actuated controller is designed using backstepping control. To avoid repeated derivatives of control inputs, a dynamic surface method introduces a filter and auxiliary controller. Lyapunov criteria guide adaptive laws for tuning controller gain and filters. A low-power observer is integrated for state estimation. Additionally, a disturbance observer is developed and combined with the control scheme to handle unknown disturbances. Simulations on a DJI F450 quadrotor demonstrate that the proposed control algorithm offers strong trajectory-tracking performance and system stability under multiple uncertainties and external disturbances during flight.

Trajectory tracking problem of autonomous vehicle under lane change is considered in this paper. To improve trajectory tracking performance in real scene, an adaptive tracking controller with finite time convergence performance is proposed to deal with the uncertain factors in the lane change scenario. The effectiveness and advantages of the proposed adaptive finite time control strategy (AFTCS) are illustrated by the simulation comparison results with the conventional PID tracking controller.

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

This paper presents an adaptive fuzzy trajectory tracking control schemes for a wheeled inverted pendulum mobile robot (WIP) in presence of parametric uncertainties and external disturbances. First a virtual output is defined for overcoming underactuated property of the system. Then feedback linearization technique is applied to dynamic of system. A damping term is designed through LQG method to damp oscillation which caused by definition of virtual output and is added to control law. After that disturbances and parametric uncertainties are compensated by use of fuzzy disturbance observer. The computer simulations illustrate the performance of proposed controller.

In this article, a practical fixed-time adaptive neural network (NN) trajectory tracking control scheme for underactuated autonomous underwater vehicles (AUVs) subject to uncertain dynamics, unknown time-varying disturbances, an unknown dead zone, actuator faults and input quantization is developed for the first time. Here, a hysteresis quantizer is introduced to decrease the oscillation in the signal quantization process. Then, the radial basis function NN is employed to compensate the uncertainty term in the AUVs trajectory tracking control system. By incorporating the bounded estimate, smoothing functions and parameter adaptive technique, the problem of unknown dead zone, actuator fault and input quantization are addressed. The restrictive conditions of boundedness for the disturbance-like item in conventional sector bounded quantizer is resolved. Subsequently, a practical fixed-time adaptive NN trajectory tracking control law is designed does not require any parameter information of the quantizer under the backstepping design framework. The theoretical analysis further confirms that all signals in the AUV trajectory tracking closed-loop control system remain bounded, and the developed control scheme is shown to be effective through simulation results.

For the problems of difficult position control and load swing reduction caused by external disturbances in the system of quadrotor UAV with suspended load, the approximation ability of radial basis function neural network (RBFNN) algorithm to the unknown function is used to compensate the external disturbances. A new robust adaptive neural network (RANN) trajectory tracking control scheme is designed. The stability of closed-loop system is analyzed through Lyapunov stability theory. Finally, the performance of the proposed control scheme is verified via numerical simulations.

This study presents the state-feedback and output-feedback adaptive shared trajectory tracking control laws for nonlinear Euler–Lagrange systems subject to parametric uncertainties and output constraints framed within linear inequalities. The logarithm-driven coordinate transformation is used to ensure that system outputs are consistently bounded by defined regions, while model-based adaptive laws are used in the machine controller to estimate and cancel parametric uncertainties and the human controller can be given arbitrarily. The stability of the whole controlled system is proved by Lyapunov stability theory, and simulation examples are used to illustrate the performance of the proposed shared control laws.

The adaptive trajectory and force tracking control is considered for a new mobile manipulator under both holonomic and affine constraints with the presence of uncertainties and disturbances in this paper. A new state transformation is proposed to deal with the affine constraints, and then based on a suitable reduced dynamic model, adaptive controller is presented to ensure that the states of closed-loop system asymptotically track to desired trajectories while the constraint force remains bounded by tuning design parameters. Detailed simulation results confirm the effectiveness of the control strategy.

Robust adaptive asymptotic trajectory tracking control for underactuated surface vessels subject to unknown dynamics and input saturation

This paper studies the trajectory tracking control problem of an AGV under the influence of input saturation, where an improved adaptive sliding mode control method based on a barrier function is proposed. The barrier function is used to ensure that the tracking error converges to a predefined range, and system chattering is reduced. An auxiliary system is designed to compensate for the vehicle system's input saturation, allowing the vehicle to maintain a satisfactory trajectory tracking performance when the control torque is limited. Finally, simulation results shows the effectiveness of the method proposed.