Jianjun Ma

Adaptive Dynamic Surface Control of a Class of Nonlinear Systems With Unknown Direction Control Gains and Input Saturation

In this paper, adaptive neural network based dynamic surface control (DSC) is developed for a class of nonlinear strict-feedback systems with unknown direction control gains and input saturation. A Gaussian error function based saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Based on backstepping combined with DSC, adaptive radial basis function neural network control is developed to guarantee that all the signals in the closed-loop system are globally bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design parameters. Simulation results demonstrate the effectiveness of the proposed approach and the good performance is guaranteed even though both the saturation constraints and the wrong control direction are occurred.

Adaptive NN Control of a Class of Nonlinear Systems With Asymmetric Saturation Actuators

In this note, adaptive neural network (NN) control is investigated for a class of uncertain nonlinear systems with asymmetric saturation actuators and external disturbances. To handle the effect of nonsmooth asymmetric saturation nonlinearity, a Gaussian error function-based continuous differentiable asymmetric saturation model is employed such that the backstepping technique can be used in the control design. The explosion of complexity in traditional backstepping design is avoided using dynamic surface control. Using radial basis function NN, adaptive control is developed to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of origin by appropriately choosing design constants. The effectiveness of the proposed control is demonstrated in the simulation study.

Robust adaptive sliding mode control for uncertain nonlinear MIMO system with guaranteed steady state tracking error bounds

Abstract In this paper, a robust adaptive sliding mode controller is proposed for a class of uncertain nonlinear multi-input multi-output (MIMO) systems. The upper bounds of the uncertainties are not needed in the procedure of the controller design, and the controller is continuous, which guarantees that the tracking error can converge to a small residual set. Furthermore, explicit formulas are given that allow for calculating the size of the residual set, and the bounds of the tracking errors at steady state can be specified a priori and guaranteed by choosing certain design parameters. Finally, a simulation study based on a two-link rigid robotic manipulator model is used to illustrate the effectiveness of the proposed controller.

Fast nonsingular integral terminal sliding mode control for nonlinear dynamical systems

This paper presents a fast nonsingular integral terminal sliding mode control (FNITSMC) for a class of nonlinear systems. First, a novel fast nonsingular integral terminal sliding mode surface (FNITSMS) is designed by introducing power integral terms which contain a boundary-like structure. Next, by employing FNITSMS and the equivalent control technique, the FNITSMC is proposed to achieve the finite-time convergence of the system states. Compared with many existing works on terminal sliding mode control (TSMC), the proposed FNITSMC exhibits three attractive features: (i) can avoid the singularity problem without any constraint, and provide faster responses by tuning the parameters in FNITSMS; (ii) for constant or eventually constant disturbances, zero steady-state error can be achieved when a boundary layer is used to alleviate chattering; (iii) the switching gain is only required to be designed greater than the bound of the disturbance. Finally, Simulation results of both the numerical and application examples show the effectiveness of the proposed control method.

Disturbance-observer-based fixed-time second-order sliding mode control of an air-breathing hypersonic vehicle with actuator faults

This paper presents a disturbance-observer-based fixed-time second-order sliding mode approach for fault-tolerant control of an air-breathing hypersonic vehicle, where both partial loss of effectiveness faults and bias faults in actuators are considered. Firstly, a fixed-time second-order sliding mode control law is designed to guarantee the reaching time, independent of initial conditions. Then, by using a robust uniformly convergent differentiator technique, a fixed-time convergent disturbance observer is established to quickly estimate the lumped uncertainty, which consists of actuator faults and system uncertainties. Therefore, any information of actuator faults is not required by this design. Finally, simulation results of a generic air-breathing hypersonic vehicle are presented to demonstrate the effectiveness of the proposed controller.

Robust adaptive multivariable higher-order sliding mode flight control for air-breathing hypersonic vehicle with actuator failures

This article proposes an adaptive multivariable higher-order sliding mode control for the longitudinal model of an air-breathing vehicle under system uncertainties and actuator failures. Firstly, a fast finite-time control law is designed for a chain of integrators. Secondly, based on the input/output feedback linearization technique, the system uncertainty and external disturbances are modeled as additive certainty and the actuator failures are modeled as multiplicative uncertainty. By using the proposed fast finite-time control law, a robust multivariable higher-order sliding mode control is designed for the air-breathing hypersonic vehicle with actuator failures. Finally, adaptive laws are proposed for the adaptation of the parameters in the robust multivariable higher-order sliding mode control. Thus, the bounds of the uncertainties are not needed in the control system design. Simulation results show the effectiveness of the proposed robust adaptive multivariable higher-order sliding mode control.

Adaptive finite-time tracking control for a robotic manipulator with unknown deadzone

This paper is concerned with the adaptive finite-time control for a robotic manipulator preceded by unknown non-symmetric deadzone. Radial basis function neural networks (RBFNNs) are employed to approximate the unknown dynamics and the deadzone effect of actuators. Adaptive finite-time tracking controller is then proposed based on the finite-time stability theorem in combination with backstepping technique. Consequently, tracking control of a robotic manipulator with finite-time convergent property is achieved even in the presence of unknown uncertainties and deadzone nonlinearity. Stability of the closed-loop system is analyzed via Lyapunov direct method. Simulation studies on a two-joint rigid manipulator are conducted to examine the effectiveness of the proposed control.

Adaptive finite-time attitude tracking control of an uncertain spacecraft with input saturation

This paper presents an adaptive finite time tracking control design for a spacecraft with uncertainties and input saturation. Radial basis function neural networks (RBFNNs) are used to approximate the unknown uncertainties and external disturbances. The input saturation effect is compensated by adding an auxiliary signal. State feedback finite time tracking controller is then proposed based on the finite time control principle and backstepping technique. Consequently, attitude tracking of an uncertain spacecraft with the finite time convergent property is achieved even in the presence of input saturation. Stability of the closed-loop system is analyzed via Lyapunov direct method. Simulation studies are conducted to examine the effectiveness of the proposed control.

Constrained control allocation based on nonlinear compensation with application to flight control

In flight control, control allocation is used to distribute the total control command to each actuator when the number of actuators exceeds the number of controlled variables. A new optimal control allocation method based on nonlinear compensation was proposed in this paper. The quadratic term and mutual-interference effects were introduced into control effectiveness matrix. The linear combination of control objective and error objective was selected as optimal target. Then the optimal control allocation problem of redundancy system was solved by transformed to nonlinear programming formulation. Comparison with other allocation method and control system simulation results show the proposed method has timing properties similar to the redistributed pseudo-inverse method and achieves the objective accurately and optimally.

Performance comparison of control allocation for aircraft with control effectiveness uncertainties

The task of control allocation is to distribute the total control command to each actuator when the number of actuators exceeds the number of controlled variables. In this paper, the effects of uncertainty of flight control system on control allocation algorithms are examined. The uncertainties of control allocation algorithm result from model uncertainties are analyzed firstly. Then three control allocation methods including direct allocation method, cascaded generalized inverse method and fixed-point method are compared using aircraft model with control effectiveness uncertainties. The simulation results demonstrate the influences of uncertainties to the control allocation results.