Optimization-based adaptive trajectory tracking controller design of self-balanced vehicle with asymptotic prescribed performance

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.

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.

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 reports the adaptive asymptotic tracking control problem for flexible-joint (FJ) robot systems, the output tracking error can be kept within the prescribed range in the initial stage of system operation, as time approaches infinity, the asymptotic tracking result can be obtained. The prescribed performance function and the positive integrable time-varying function are introduced simultaneously in the control design of FJ robot systems for the first time. The control scheme is designed under the frame of the adaptive backstepping method and command filtered technique, which successfully avoids the problem of complexity explosion. The radial basis function neural networks are used to deal with unknown uncertainties and the adaptive laws are designed to approximate the norms of weight vectors and approximation errors. Finally, the feasibility of the proposed scheme is proved by the simulation and the experiment of the 2-link FJ robot on the Quanser platform.

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.

This paper presented a novel adaptive cascade nonlinear trajectory tracking control scheme of over-actuated autonomous electric vehicles involving input saturation. First, a nonlinear vehicle dynamic model with input saturation is established, which can accurately describe the features of uncertainties and coupling of autonomous electric vehicles, and the hyperbolic tangent function is designed to estimate the saturation function for dealing with the input saturation problem. Then, a novel adaptive cascade trajectory tracking control scheme is designed. An adaptive neural network-based terminal sliding control law is proposed for producing the generalized force/moment in real-time, the asymptotic stability of this adaptive control system is proven by Lyapunov theory, and a quasi-newton distribution law is designed to determine the optimum tire forces that guarantee the actual generalized forces/moment are close to the desired values. Finally, simulation results demonstrate the effectiveness of the proposed control scheme.

SUMMARYThis paper presents an adaptive trajectory tracking controller for a non-holonomic wheeled mobile robot (WMR) in the presence of parametric uncertainty in the kinematic and dynamic models of the WMR and actuator dynamics. The adaptive non-linear control law is designed based on input–output feedback linearization technique to get asymptotically exact cancellation for the uncertainty in the given system parameters. In order to evaluate the performance of the proposed controller, a non-adaptive controller is compared with the adaptive controller via computer simulation results. The results show satisfactory trajectory tracking performance by virtue of SPR-Lyapunov design approach. In order to verify the simulation results, a set of experiments have been carried out on a commercial mobile robot. The experimental results also show the effectiveness of the proposed controller.

A force/position hybrid adaptive control method based on radial basis function(RBF) neural network is proposed to solve the problem of difficulties to precisely describe the compliance and friction for robot terminal during trajectory tracking process.RBF neural network is an efficient feed-forward neural network with non-linear approximation and global optimization characteristics,which is not provided by other feed-forward networks,which is simple in network structure,and rapid in training speed.An adaptive controller is designed that relies on the nonlinear approximation ability of the RBF neural network to estimate the uncertainty factors in the models,the update rules for the weights of the controller neural network is provided and its finally uniform boundedness of the errors of the controller output force and position is proved.The controller is applied to a duct cleaning robot for simulation experiments.Simulation results shows that the adaptive controller demonstrates superior tracking precision and robustness compared with traditional adaptive controller.

Adaptive trajectory tracking control for underwater vehicle based on hierarchical compensation

This article focuses on an adaptive dynamic surface tracking control issue of nonlinear multiagent systems (MASs) with unmodeled dynamics and input quantization under predefined accuracy. Radial basis function neural networks (RBFNNs) are employed to estimate unknown nonlinear items. A dynamic signal is established to handle the trouble introduced by the unmodeled dynamics. Moreover, the predefined precision control is realized with the aid of two key functions. Unlike the existing works on nonlinear MASs with unmodeled dynamics, to avoid the issue of "explosion of complexity," the dynamic surface control (DSC) method is applied with the nonlinear filter. By using the designed controller, the consensus errors can gather to a precision assigned a priori. Finally, the simulation results are given to demonstrate the effectiveness of the proposed strategy.

In this paper, the problem of prescribed performance adaptive neural tracking control design is investigated for strict-feedback stochastic nonlinear systems. The unknown nonlinear functions are tackled using the neural networks. Combining with prescribed performance function and backstepping technique, the desired adaptive controller is developed. It is proved that all the signals of the close-loop system are bounded in probability and the tracking error remains within a predefined arbitrarily small residual set with the prescribed performance bounds. Simulation results are provided to show the effectiveness of the proposed control method.

This paper develops a novel adaptive trajectory tracking control strategy to enhance the tracking performance for surface vessels with unmodeled dynamics and unknown time-varying disturbances. A high robustness and precision trajectory tracking controller is presented by using trajectory linearization control (TLC) technology, neural network, extended state observer (ESO), nonlinear tracking differentiator, and auxiliary dynamic system. First, the greatest advantage of this paper is that the TLC technology is first introduced into the field of surface vessels motion control, which provides a new direction for TLC technology research. Then, to further enhance the control performance and robustness of the system, the neural network with minimum learning parameter is used to replace the classical radial basis function neural network to approximate unmodeled dynamics, which can reduce the burden of computing. A novel reduced-order ESO is constructed to estimate unknown time-varying disturbances to achieve real-time compensation. Meanwhile, nonlinear tracking differentiator is employed to realize the derivative of virtual control command, as well as to provide command filtering. In addition, an auxiliary dynamic system is designed to reduce the risk of actuator saturation. The stability of the closed-loop system is guaranteed based on the Lyapunov criteria. Lastly, the comparison results demonstrate the superior performance of the proposed approach.

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.

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.

In this paper, an novel adaptive tracking control is developed for a class of completely non-affine pure-feedback nonlinear systems using radial basis function neural networks (RBFNNs). Combining the dynamic surface control (DSC) technique and backstepping method, the explosion of complexity in the traditional backstepping design is avoided. Using mean value theorem and Young's inequality, only one learning parameter need to be tuned online in the whole controller design, and the computational burden is effectively alleviated. It is proved that the proposed design method is able to guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system. Simulation results verify the effectiveness of the proposed approach.