Adaptive robust anti-disturbance control for pure feedback nonlinear systems with multiple constraints

This paper investigates a novel robust adaptive dynamic surface control scheme based on the barrier Lyapunov function (BLF), online composite learning, disturbance observer, and improved saturation function. It is mainly designed for a class of skid-to-turn (STT) interceptor integrated guidance and control (IGC) design problems under multi-source uncertainties, state constraints, and input saturation. The serial-parallel estimation model used in this study estimates the system states and provides “critic” information for the neural network and disturbance observer; then, these three are combined to realize online composite learning of the multiple uncertainties of the system and improve the interception accuracy. In addition, the state and input constraints are resolved by adopting the BLF and the improved saturation function, while the design of the auxiliary system ensures stability. Finally, a series of simulation results show that the proposed IGC scheme with a direct-hit intercept strategy achieves a satisfactory effect, demonstrating the validity and robustness of the scheme.

Robust fixed-time H∞ tracking control of UUVs with partial and full state constraints and prescribed performance under input saturation

A low-complexity adaptive tracking control strategy for a class of pure feedback nonlinear systems is developed in the presence of completely unknown non-affine nonlinearities, prescribed tracking performance constraints, full states constraints, and input saturation constraint. To handle the input saturation nonlinearity and the constrained states without pre-calculating the bound of virtual controllers, a new coordinate transformation approach is presented with an arc-tangent function and an auxiliary first-order dynamics. Subsequently, to deal with the completely unknown non-affine nonlinearities and the problem of prescribed performance, an inverse hyperbolic tangent function and a Nussbaum function are introduced at each step of back-stepping design, which can guarantee the control errors to vary with prescribed performance functions in time and avoid the “explosion of complexity” issue simultaneously. Then, an adaptive controller is derived from the recursive design procedure without the use of any approximators. The semi-globally uniformly ultimate boundedness of all signals in the closed-loop system and the satisfaction of all constraints are rigorously proved through Lyapunov stability analysis. Finally, the simulation studies on a hypersonic flight vehicle system are worked out to demonstrate the effectiveness and the applicability of the investigated approach.

In the large-area multidevice linkage-based laser processing, using the traditional control method cannot guarantee the processing accuracy of a permanent magnet linear motor (PMLM) system for a high-speed subject, due to unknown model uncertainties, backlash-like hysteresis, states constraints, and external disturbance. To address this shortcoming, this article develops a disturbance observe-based adaptive neural control for the PMLM system. First, aiming at the problem of unknown and uncertain system parameters, this study uses a radial basis neural network to estimate unknown functions of the system. Then, an adaptive variable is introduced to compensate for the effect of unknown backlash-like hysteresis, and the barrier Lyapunov function is used to restrict the motor from operating in a specified area. In addition, a nonlinear disturbance observer is constructed to adapt to the actual processing to reduce the influence of the external disturbance and the system load change. The proposed control scheme is verified experimentally, and the results indicate that when the proposed method is used, the PMLM system is consistently bounded by using the Lyapunov theorem. Finally, the simulation and experimental results verify the effectiveness of the proposed control strategy. The proposed method could be applied to laser-processing equipment.

This paper proposes a new and original course keeping control strategy for an unmanned surface vehicle in the presence of modeling error, external disturbance and input saturation. The trajectory linearization control method is used as the basic algorithm to design the course keeping strategy, and the radial basis function neural network and disturbance observer are used to compensate modeling error and external disturbance respectively to enhance the robustness of the control system. Moreover, a robust term is used to compensate various compensation errors to further improve the robustness of the system. In addition, hyperbolic tangent function and Nussbaum function are hired to deal with the potential input saturation problem, and the neural shunting model is adopted to avoid the computational explosion caused by the derivation of virtual control law. Taking the above facts into account will help to further realize engineering practice. Finally, the control strategy proposed in this paper is compared with the classical proportional–integral–derivative control strategy. The simulation results show that the course control results of the proposed control strategy are more robust than proportional–integral–derivative control, regardless of whether the external disturbance is weak or strong.

Terminal sliding mode tracking control for a class of SISO uncertain nonlinear systems

The control performance of quadrotor unmanned aerial vehicles (UAVs) in complex environments can be affected by external disturbances and other factors. In this paper, an adaptive neural network backstepping controller based on the barrier Lyapunov function (BLF) is designed for a quadrotor UAV with internal uncertainties, input–output constraints and external disturbances. Radial basis function neural networks are used to approximate the uncertainties in the dynamic model of the UAV, while the minimum parameter learning method is combined to accelerate the adjustment speed of neural network weights. A robust term is designed to balance the total system disturbance and improve the anti-interference performance. The BLF is used to handle the output constraint so that the constrained parameters cannot break the predefined constraints. An auxiliary system is introduced to solve input saturation and avoid the dependence of tracking error on the input amplitude in the method of approximating input saturation using the smoothing function. The stability of the control system is demonstrated by the Lyapunov method. The simulation results show that the proposed method has high tracking accuracy compared with the backstepping dynamic surface control method, and the input and output are in the predefined range.

Adaptive disturbance rejection neural output feedback control of hydraulic manipulator systems

Adaptive neural tracking control of pure-feedback nonlinear systems with unknown gain signs and unmodeled dynamics

Hydraulic system is widely used in industry where large actuation forces are needed such as electro-hydraulic positioning system, active suspension control, and so on. One of the main problem in hydraulic system is the nontriviality, for instance, the deadbands in valve operation, frictions of links, saturations, pressure dynamics, etc. both system unknown nonlinearities and external disturbances should be considered in controller design. For underwater hydraulic manipulator, because of the environment of sea and the large nonlinearities of the hydraulic manipulator system, both system unknown nonlinearities and external disturbances should be considered in controller design. A lot of control methods have been proposed to solve the problem properly, such as sliding mode control, adaptive control, fuzzy adaptive control, adaptive robust control, ect. However, due to limited knowledge about hydraulic manipulator system, it is hard to precisely estimate the nonlinearities for a possible higher performance. In this paper, a neural network adaptive robust control method is proposed to help control an underwater hydraulic manipulator. In order to achieve better performance using adaptive robust control strategy, unknown nonlinearities in the system are approximated by the outputs of radial basis function neural network (RBFNNs). A projection law has been used to bound the NN weights when they are tuning on-line to avoid possible adaptive divergence, and the tracking transient performance is guaranteed by robust control law. Simulation results illustrate that manipulator has a good tracking performance.

This paper proposed a new fixed-time trajectory tracking control strategy based on the disturbance observer (DOB) for a tilt trirotor unmanned aerial vehicle (TTUAV) which is subject to unknown external disturbances. To facilitate the TTUAV's trajectory tracking controller design, the dynamic model of the TTUAV is divided into two subsystems, the position subsystem (outer-loop) and the attitude subsystem (inner-loop). For the dynamics of the TTUAV's outer-loop, the unknown external disturbances consist of the unknown wind disturbances, the unknown aerodynamic frictions forces, the lateral force generated by the rear servo and the states coupling between the outer-loop and the inner-loop. Then, a nonlinear DOB is designed to estimate the unknown external disturbances, and a nonsingular terminal sliding-mode controller (NTSMC) is further developed to drive the TTUAV's position tracking errors to zero in a fixed time. For the dynamics of the TTUAV's inner-loop, the unknown external disturbances are mainly introduced by the aerodynamic frictions torques. Then, a modified sliding mode controller is designed to reject the unknown external disturbances, and to ensure that the attitude tracking errors converge to zero exponentially. In addition, a composite stability analysis based on Lyapunov stability theory is provided to guarantee the fixed-time convergence of the closed-loop dynamics of the TTUAV. Finally, the results of the real-time experiments are presented to verify the effectiveness of the proposed control strategy.

In this paper, considering the simultaneous influence of multi-source disturbances, system modeling uncertainties and input–output constraints, an adaptive robust attitude tracking control scheme is proposed for near space vehicles (NSVs) which is expressed as a stochastic nonlinear system. A multi-dimensional Taylor polynomial network (MTPN) is utilized to handle the system uncertainties, and the nonlinear disturbance observer (NDO) based on MTPN is designed to estimate the external disturbances. Furthermore, by constructing the auxiliary system to tackle the input saturation and introducing the Tan-type barrier Lyapunov function (TBLF) to solve the output constraint, the constrained control strategy can be obtained. Combining with backstepping control (BC) technique and stochastic control method, an adaptive robust stochastic control scheme is developed based on NDO, MTPN, and auxiliary system, and the closed-loop system stability in the sense of probability is analyzed based on stochastic Lyapunov stability theory. Finally, numerical simulations further demonstrate the feasibility of the proposed tracking control scheme.

This paper presents a Radial Basis Function neural network based higher-order Sliding Mode Control for robust control of a dynamical system. A conventional sliding mode controller is suffering from a chattering problem, and the Super Twisting Algorithm is a special kind of higher-order Sliding Mode Control that has the capability of minimizing the chattering problem. There are also unknown model parameters and external disturbances that exert a negative influence on the control performance. To address the issues of model uncertainties and chattering, a Radial Basis Function (RBF) neural network based Super Twisting Algorithm is designed. The RBF neural network evaluates the model parameters and uncertainties, while the Super Twisting approach mitigates chattering, hence improving the controller’s overall performance. Lyapunov stability based adaptive laws are derived for online updating of the parameters of the neural network. The proposed control algorithm was tested on a 2-degree-of-freedom serial flexible joint robotic arm to investigate its efficacy. The controller has a lower control chattering amplitude, lower control energy consumption, and a good tracking response, when compared to the RBF based conventional Sliding Mode controller and simple STA controller, as shown by the results.

In this study, the fault-tolerant control problem is investigated for a class of non-affine output-constrained multi-input multiple-output non-linear systems with actuator faults. The controlled systems contain unknown non-linear functions, unknown external disturbance and immeasurable states. The fuzzy logic systems and a robust compensation function are employed to deal with the unknown non-linear functions and non-affine non-linear actuator fault problems. Then, the state observer and the non-linear disturbance observer are developed for estimating the immeasurable states and unknown compounded disturbance, respectively. By using the barrier Lyapunov function method and in the unified framework of adaptive backstepping output feedback control design, a novel adaptive fuzzy fault-tolerant controller design scheme is developed. It is shown that all the variables of the closed-loop system are semi-globally uniformly bounded. Moreover, the system outputs cannot violate their predefined bounds in the presence of the non-affine non-linear faults. A simulation is given to validate the effectiveness of the proposed approach.

Adaptive vibration control for flexible satellite with output constraint and unknown control direction