Mou Chen

Extended State Observer-Based Integral Sliding Mode Control for an Underwater Robot With Unknown Disturbances and Uncertain Nonlinearities

This paper develops a novel integral sliding mode controller (ISMC) for a general type of underwater robots based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). The difficulties associated with the unmeasured velocities, unknown disturbances, and uncertain hydrodynamics of the robot have been successfully solved in the control design. An adaptive MIMO-ESO is designed not only to estimate the unmeasurable linear and angular velocities, but also to estimate the unknown external disturbances. An ISMC is then designed using Lyapunov synthesis, and an adaptive gain update algorithm is introduced to estimate the upper bound of the uncertainties. Rigorous theoretical analysis is performed to show that the proposed control method is able to achieve asymptotical tracking performance for the underwater robot. Experimental studies are also carried out to validate the effectiveness of the proposed control, and to show that the proposed approach performs better than a conventional potential difference (PD) control approach.

Robust tracking control for uncertain MIMO nonlinear systems with input saturation using RWNNDO

In this paper, the robust tracking control scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with input saturation and unknown external disturbance based on the recurrent wavelet neural network disturbance observer (RWNNDO) and the backstepping technique. And then, the developed robust tracking control scheme is applied to an unmanned aerial vehicle (UAV) system. To handle the input saturation, a hyperbolic tangent function and a Nussbaum function are employed, and the dynamic surface method is applied to solve the problem of explosion of complexity in backstepping control. It is proved that the proposed control scheme can guarantee that all signals of the closed-loop system are bounded through the Lyapunov analysis. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme for uncertain MIMO nonlinear systems.

Adaptive neural prescribed performance tracking control for near space vehicles with input nonlinearity

In this paper, an adaptive neural tracking controller with prescribed performance is developed for near space vehicles (NSVs) with unknown parametric uncertainties, external disturbances and input nonlinearities including input saturation and dead-zone. By placing the right inverse of the dead-zone before the input nonlinearity, the serial input nonlinearity consisting of input saturation and dead-zone can be regarded as an equivalent input saturation which is solved by a common constrained control method. To guarantee the prescribed performance of the closed-loop system including the transient and steady-state performance, the constrained prescribed performance is changed into unconstrained transformation error using error transformed technology. Meanwhile, the radial basis function neural networks (RBFNNs) are adopted to tackle the system uncertainties. Then, using the auxiliary system and backstepping technology, an adaptive tracking control scheme is proposed, and all the closed-loop system signals are proved to be bounded. Finally, extensive simulations are given for the attitude motion of the NSV to illustrate the effectiveness of the developed adaptive neural control scheme.

Fault-tolerant control for a class of non-linear systems with dead-zone

In this paper, a fault-tolerant control scheme is proposed for a class of single-input and single-output non-linear systems with the unknown time-varying system fault and the dead-zone. The non-linear state observer is designed for the non-linear system using differential mean value theorem, and the non-linear fault estimator that estimates the unknown time-varying system fault is developed. On the basis of the designed fault estimator, the observer-based fault-tolerant tracking control is then developed using the backstepping technique for non-linear systems with the dead-zone. The stability of the whole closed-loop system is rigorously proved via Lyapunov analysis and the satisfactory tracking control performance is guaranteed in the presence of the unknown time-varying system fault and the dead-zone. Numerical simulation results are presented to illustrate the effectiveness of the proposed backstepping fault-tolerant control scheme for non-linear systems.

Adaptive control and constrained control allocation for overactuated ocean surface vessels

In this article, the constrained control allocation is proposed for overactuated ocean surface vessels with parametric uncertainties and unknown external disturbances. The constrained control allocation is transformed into a convex quadratic programming problem and a recurrent neural network is employed to solve it. To complete the control allocation, the control command is derived via the backstepping method. Adaptive tracking control is proposed for the full-state feedback case using the backstepping technique and the Lyapunov synthesis. It is proved that the proposed adaptive tracking control is able to guarantee semi-global uniform ultimate boundedness of all signals in the closed-loop system. Then, the obtained control command is distributed to each actuator of overactuated ocean vessels. Finally, simulation studies are presented to illustrate the effectiveness of the proposed adaptive tracking control and the constrained control allocation scheme.

Disturbance Attenuation Tracking Control for Wheeled Mobile Robots With Skidding and Slipping

In this paper, a robust tracking control scheme is proposed for wheeled mobile robots with skidding, slipping, and input disturbance. Considering the existing skidding and slipping, a desired disturbance-observer-based virtual velocity control law is first designed. Then, the robust tracking control scheme is developed by considering the prescribed tracking performance requirement and using the disturbance observer. In the tracking control scheme design, the prescribed performance function method is employed to guarantee the desired tracking performance. To handle the skidding, slipping, and input disturbance, the disturbance observer is developed in the control scheme design. Experiment results demonstrate the effectiveness of the proposed tracking control scheme for wheeled mobile robots with skidding, slipping, and input disturbance.

Adaptive neural tracking control for uncertain nonlinear systems with input and output constraints using disturbance observer

This paper investigates the tracking control problem for a class of strict-feedback nonlinear uncertain systems in the presence of unknown disturbance, input and output constraints. By using backstepping approach, an adaptive tracking controller is developed on the basis of neural network and disturbance observer. The Nussbaum function is introduced to tackle the problem of the nonlinear term arising from the input saturation, and the barrier Lyapunov function is employed to prevent the outputs from violating the constraints. The disturbance observer is developed to estimate unknown external disturbances. The proposed control scheme can guarantee that all signals of the closed-loop system are bounded by using the Lyapunov analysis method. Finally, the simulation results for three degrees of freedom (3-DOF) model helicopter are given to illustrate the effectiveness of the developed control scheme.

Discrete-time Optimal Adaptive RBFNN Control for Robot Manipulators with Uncertain Dynamics

Abstract In this paper, a novel optimal adaptive radial basis function neural network (RBFNN) control has been investigated for a class of multiple-input-multiple-output (MIMO) nonlinear robot manipulators with uncertain dynamics in discrete time. To facilitate digital implementations of the robot controller, a robot model in discrete time has been employed. A high order uncertain robot model is able to be transformed to a predictor form, and a feedback control system has been then developed without noncausal problem in discrete time. The controller has been designed by an adaptive neural network (NN) based on the feedback system. The adaptive RBFNN robot control system has been investigated by a critic RBFNN and an actor RBFNN to approximate a desired control and a strategic utility function, respectively. The rigorous Lyapunov analysis is used to establish uniformly ultimate boundedness (UUB) of closed-loop signals, and the high-quality dynamic performance against uncertainties and disturbances is obtained by appropriately selecting the controller parameters. Simulation studies validate that the proposed control scheme has performed better than other available methods currently, for robot manipulators.

ℓ 1 -induced state-bounding observer design for positive Takagi–Sugeno fuzzy systems

Abstract In this paper, the design problem of the l1-induced positive observer is studied for positive Takagi–Sugeno (T–S) fuzzy systems. First, a l1-induced performance index is presented for positive systems and the performance indicator matches well to the linear Lyapunov functions. Then, we explore a novel description of the l1-induced performance for positive T–S fuzzy systems. Moreover, the design problem of the l1-induced positive observer is solved for positive fuzzy systems under a proposed linear programming method. To estimate the state of positive fuzzy systems, a couple of state-bounding positive observers are constructed. In the end, a numerical example is given to show the effectiveness of the proposed approach.

Constrained adaptive neural control for a class of nonstrict-feedback nonlinear systems with disturbances

Abstract This paper focuses on the tracking problem for a class of nonstrict-feedback nonlinear systems with mismatched unknown nonlinear functions and external disturbances. First, a disturbance observer is developed to estimate the disturbance generated by an exogenous system. Then, based on the output of the disturbance observer, a constrained adaptive neural controller is developed for the nonstrict-feedback nonlinear system. In the control scheme design, the modified variable separation approach is applied to establish the relationship between the bounded function of nonstrict-feedback nonlinear function and the error variable. Furthermore, the barrier Lyapunov function is applied to guarantee that full state constraints are not violated. As a result, all the signals of the closed-loop system are semi-global uniformly ultimately bounded. Finally, two simulation examples are used to demonstrate the effectiveness of the developed constrained adaptive neural control law.