Adaptive neural finite-time trajectory tracking control of hydraulic excavators

This paper presents an online recorded data-based composite neural finite-time control scheme for underactuated marine surface vessels (MSVs) subject to uncertain dynamics and time-varying external disturbances. The underactuation problem of the MSVs was solved by introducing the line-of-sight (LOS) method. The uncertain dynamics of MSVs are approximated by the composite neural networks (NNs). A modified prediction error signal is designed by virtue of online recorded data. The weight updating law of NN is driven by both tracking error and prediction error, introducing additional correction information to the weights of NN, thus improving the learning ability of the NN. Furthermore, disturbance observers can be devised to estimate the compound disturbances consisting of the approximation errors of NNs and external disturbances. Moreover, the smooth function is inserted into the design of the control scheme, and the finite-time composite neural trajectory tracking control of MSVs is achieved. The stability of the MSVs trajectory tracking closed-loop control system is guaranteed rigorously by the Lyapunov approach, and the tracking error will converge to the set of residuals around zero within a finite time. The simulation tests on an MSV verify the effectiveness of the proposed control scheme.

Trajectory tracking control is indispensable for a wheeled mobile robot to achieve successful navigation. The classical tracking control systems that are used in wheeled mobile robots do not compensate for the parameter uncertainties and external disturbances. For control strategy, this paper presents a novel hybrid approach, combining a neural network-based kinematic controller and a model reference adaptive control. The controller parameters are adaptively determined online using neural networks. The adaptively tuned kinematic controller ensures a fast convergence to the desired trajectory. The model reference adaptive controller retains the desired tracking performance when parameter and model uncertainties occur. The Lyapunov stability method is used to obtain the adaptive gains which guarantee the asymptotic stability of the error dynamics, where the error is the difference between the outputs of the reference model and the actual plant. The performance of the proposed controller is compared with that of the PID controller, kinematic controller, and adaptive dynamic controller using different performance analysis indices such as integral absolute error, integral squared error, and mean absolute error. Simulation studies demonstrate that the proposed controller achieves high tracking accuracy and fast convergence as compared to the PID, kinematic, and adaptive dynamic controllers considering parameter uncertainties and slip disturbances. The outcomes of the simulation studies also illustrate that the proposed controller achieves the best transient performance. Experiments using real-world tests based on a two-wheeled differential drive robot architecture have elucidated the feasibility of the developed controller regarding tracking accuracy, total control effort, and robustness against uncertainties.

Adaptive neural network-based trajectory tracking outer loop control for a quadrotor

This paper presents an adaptive neural tracking control approach for uncertain stochastic nonlinear time‐delay systems with input and output constraints. Firstly, the dynamic surface control (DSC) technique is incorporated into adaptive neural control framework to overcome the problem of ‘explosion of complexity’ in the control design. By employing a continuous differentiable asymmetric saturation model, the input constraint problem is solved. Secondly, the appropriate Lyapunov‐Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown time‐delay terms, RBF neural network is utilized to identify the unknown systems functions, and barrier Lyapunov functions (BLFs) are designed to avoid the violation of the output constraint. Finally, based on adaptive backstepping technique, an adaptive neural control method is proposed, and it decreases the number of learning parameters. Using Lyapunov stability theory, it is proved that the designed controller can ensure that all the signals in the closed‐loop system are 4‐Moment (or 2 Moment) semi‐globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two simulation examples are provided to further illustrate the effectiveness of the proposed approach.

In this note, adaptive neural control is presented for a class of strict-feedback nonlinear systems with unknown time delays. Using appropriate Lyapunov-Krasovskii functionals, the uncertainties of unknown time delays are compensated for such that iterative backstepping design can be carried out. In addition, controller singularity problems are solved by using the integral Lyapunov function and employing practical robust neural network control. The feasibility of neural network approximation of unknown system functions is guaranteed over practical compact sets. It is proved that the proposed systematic backstepping design method is able to guarantee semiglobally uniformly ultimate boundedness of all the signals in the closed-loop system and the tracking error is proven to converge to a small neighborhood of the origin.

In the braking control of single rope winding hoisting systems (SRWHS), reasonable braking torque is of great significance in reducing the probability of significant hidden dangers such as overwinding, overspeeding, rope breaking, vibration, conveyance crash and so on. However, dynamic nonlinearity, time-varying disturbances, mechanical dynamic uncertainties and measurement noise severely affect the practical braking control performance. To address those problems, a neural adaptive braking torque controller with disturbance compensation is developed to enhance the braking performance for a single rope winding hoisting system in this paper. Firstly, considering the elastic of steel wire rope, the nonlinear braking model of the SRWHS is established using Lagrange equations and an extended state observer (ESO) is introduced to estimate the system’s unmeasured states and modelling error. Next, a neural adaptive controller is developed to estimate and compensate the mechanical dynamical disturbances resulted from friction forces and random external disturbances. Then, a neural adaptive network controller combined with the ESO (ESONAC) is designed to solve the modelling nonlinearity, time-varying disturbances and system’s unmeasurable states. Finally, the advantages of the ESONAC in improving the braking control performance of winding hoisting systems is verified by comparative experiments.

An adaptive neural network control scheme is developed for a class of nonlinear systems in the strict-feedback form. Compared with the existing approaches, the main advantage is that the developed scheme can be implemented by utilizing only one neural network approximator. Thus, the designed controller structure is simplified. In addition, less neural network can reduce the running cost in practical application. The developed neural network control scheme can achieve that all the signals of the closed-loop system are uniformly bounded and the tracking errors converge to an arbitrary small neighborhood around zero by selecting suitably design parameters.

Neural network-based adaptive fault-tolerant control for nonlinear systems with unknown backlash-like hysteresis and unmodeled dynamics

This paper introduces a novel control strategy under the slow down navigation circumstance for the underactuated surface vehicle (USV)-unmanned aerial vehicle (UAV) cooperative system. Within the guidance planning structure, a time-variable slow down function is presented to become the desired velocity signal of the virtual vehicles. In order to accomplish the aim of the path following, the neural adaptive controller different from position and attitude is designed. For the sake of the control system stability, combining the backstepping and the sliding mode theory, multiple parameter update laws are designed to tackle the model uncertainty and the external interferences. The stability of the closed-loop control system is proved by Lyapunov theory. Finally, the simulation experiment is conducted to testify the effectiveness of the proposed algorithm.

Event-triggered adaptive neural network controller for uncertain nonlinear system

This paper deals with an adaptive robust tracking control using a multilayer neural network (NN) for a class of nonlinear dynamic systems with unknown time varying state delays. Typical adaptive NN backstepping controllers for uncertain nonlinear systems with time-delay give rise to computation complexity caused by the the repeated derivatives of virtual controllers and nonlinear functions. Moreover, the combined techniques usually result in only uniformly ultimately bounded (UUB) stability caused by the inherent NN approximation error. This paper presents a control scheme that uses an integral sliding mode control as a feedback term and an adaptive neural controller as a feedforward term based on the desired compensation adaptive law (DCAL) technique. First, we develop a new DCAL formulation which avoids the explosion of complexity caused by the general NN backstepping scheme to compensate for nonlinear system uncertainties, bounded system disturbances, and unknown state time delays. Then, using a Lyapunov-Krasovskii (LK) functional, it is shown that the proposed controller renders the class of uncertain nonlinear time-delay systems asymptotically stable.

This work devises a neural adaptive backstepping control of the chaotic permanent magnet synchronous motor system with output constraint. In the specific backstepping steps, to resolve the restraint of output tracking error, the Lyapunov function of the tangent barrier is introduced. Chebyshev neural networks and Nussbaum-type function are utilized to deal with uncertain nonlinearities and uncertain gain. The tracking differentiator is exploited to eliminate the "explosion of complexity" of virtual controller. Then, the boundedness of all the closed-loop signals is proved without bypassing the output constraint by Lyapunov synthesis. Eventually, simulation results verify the validities of the presented scheme.

A robust adaptive neural dynamic surface control (DSC) approach is presented for the longitudinal dynamics of a morphing aircraft in the presence of unknown dynamics and input constraints. For the altitude subsystem, neural systems are utilized to approximate the unknown nonlinear functions with smooth robust compensations to counteract the lumped approximation errors. By combining dynamic surface control and minimal learning parameter techniques, a robust adaptive neural control scheme is proposed and a simple adaptive algorithm is constructed. Meanwhile, an auxiliary system is incorporated into the control scheme to overcome the problem of input saturation. The highlight is that the proposed neural controller not only owns less updated neural parameters, but also has the ability of handling input constraints. It is proved that all the signals in the closed-loop system are bounded. Simulation results demonstrate the effectiveness of the proposed control scheme.

Perching on vertical surfaces include impact of aerial vehicle with target surface. The impact may cause damageto the installed payloads. Smooth landing on vertical surface is recommended for such aerial vehicles. This chapter proposes a novel insect copter design to perform smooth landing on vertical surface. The insect copter design is explained with its functionalities. Kinematics and dynamic equation governing the flight and perching is discussed. The trajectory for soft landing on vertical surface is formulated using neural network approximation. An adaptive neural controller is designed to follow the desired trajectory. Further to validate its behaviour simulations are performed in multi-body dynamic software MSCAdams and MATLAB. Simulation Results have shown effective controlled perching on vertical surfaces.

This paper presents an indirect adaptive neural network control for a class of uncertain non-affine nonlinear switched systems with an unknown control direction. In this method, the unknown non-affine function is firstly converted into an affine-like form using the mean value theorem. Then, an RBF neural network is utilized to approximate the unknown functions. In addition, Nussbaum-gain technique is employed to deal with the issue of not having a priori knowledge about the gain sign. Furthermore, by adding a robustifying term to the control signal, the proposed approach can handle the approximation error in neural network approximation and external disturbance so that the system robustness can be improved. The proposed controller is designed based on Lyapunov stability theory to ensure the asymptotic stability of the closed-loop system. Finally two numerical simulations are given to show the effectiveness of the proposed scheme.