Prescribed performance dynamic surface control for trajectory-tracking of unmanned surface vessel with input saturation

An adaptive neural network (NN) event-triggered control scheme is proposed for nonlinear nonstrict-feedback multiagent systems (MASs) against input saturation, unknown disturbance, and sensor faults. Mean-value theorem and Nussbaum-type function are invoked to transform the structure of the input saturation and overcome the difficulty of unknown control directions, respectively. On the basis of the universal approximation property of NNs, a nonlinear disturbance observer is designed to estimate the unknown compounded disturbance composed of external disturbance and the residual term of input saturation. According to the measurement error defined by control signal, an event-triggered mechanism is developed to save network transmission resource and reduce the number of controller update. Then, an adaptive NN compensation control approach is proposed to tackle the problem of sensor faults via the dynamic surface control (DSC) technique. It is proved that all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results demonstrate the effectiveness of the presented control strategy.

Unmanned surface vessels (USVs) are supposed to be able to adapt unstructured environments by means of multi-sensor active perception without any human interference, and high-accuracy path following is achieved for USVs by effective control strategies and intelligent devices of e-navigation. This paper proposes a finite-time predictor line-of-sight (LOS)-based integral sliding-mode adaptive neural (FPISAN) scheme for the path following of USVs in the presence of unknown dynamics and external disturbances, which copies with the problem of merging with the kinematic level and the kinetic level of USVs. From the point of view of USVs’ practical engineering, the inertia matrix of USVs maintains nonzero off-diagonal. In order to ensure that USVs can converge to and follow a defined path, a novel LOS-based guidance law that can acquire sideslip angles by error predictors within a finite time is presented, called finite-time predictor-based LOS (FPLOS). Then, the path-following control laws are designed by combining the neural network (NN) technique with the integral sliding-mode method, where radial basis function NN (RBFNN) is applied to approximate lumped unknown dynamics induced by nonparametric uncertainties and external disturbances. The theoretical analysis verifies that the path-following guidance-control system of USVs is semiglobally uniformly ultimately bounded (SGUUB) with the aid of Lyapunov stability theory. The effectiveness and performance of this presented scheme are illustrated by simulation experiments with the comparison. Note to Practitioners —The design of heading guidance laws and path-following control laws for path following of USVs subject to unknown dynamics and external disturbances is a critical problem, which affects the development of USVs. This problem associated with practical engineering of USVs due to the actual navigation environment that is complex, diversified, and highly unstructured. This paper presents a wholly tight strategy to compensate for unknown sideslip angles and approximate lumped unknown dynamics. Hence, an effective scheme being denoted FPISAN mentioned above is developed for path following of USVs.

In this paper, robust adaptive control problem is discussed for a class of output feedback nonlinear switched systems with unmodeled dynamics and time-varying constraints. Based on the common Lyapunov function (CLF), an adaptive output feedback dynamic surface control (DSC) scheme is developed. Radial basis function neural networks are utilized to approximate unknown continuous functions, and K-filters and a dynamic signal are designed to estimate the unmeasured states and deal with the dynamic uncertainty. An asymmetric barrier Lyapunov function (ABLF) is introduced to handle output constraint. It is proved that the designed controller can guarantee the semi- global uniform ultimate boundedness (SGUUB) for all the signals in the closed-loop system under arbitrary switchings. The simulation results are provided to demonstrate the effectiveness of the proposed method.

Observer-based adaptive prescribed performance tracking control for nonlinear systems with unknown control direction and input saturation

This paper proposes a resilient cooperative control framework for unmanned surface vehicles (USVs) operating under unknown disturbances, input saturation, and communication link faults. A novel resilient distributed observer is developed to simultaneously reconstruct both the state and system matrix of the leader using only local neighbor information without requiring global knowledge of the leader’s dynamics or communication topology as in existing methods. Furthermore, a disturbance-rejection synchronization controller is designed based on the internal model principle and a saturation-compensating auxiliary system. In contrast to conventional strategies that rely on constant-bound disturbance assumptions and lack explicit saturation compensation, the proposed controller achieves online disturbance cancellation without prior knowledge of disturbance characteristics such as frequency, amplitude, and phase, while explicitly handling input saturation. The proposed control strategy could significantly improve tracking accuracy, robustness, and practicality in marine environments. Theoretical analysis and simulations validate the merits and performance of the proposed approach.

Observer-based Adaptive Controller Design for Nonlinear Saturation Systems With Output Constraints

Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

Hysteresis is a complex nonlinear effect in smart materials-based actuators, which degrades the positioning performance of the actuator, especially when the hysteresis shows asymmetric characteristics. In order to mitigate the asymmetric hysteresis effect, an adaptive neural digital dynamic surface control (DSC) scheme with the implicit inverse compensator is developed in this article. The implicit inverse compensator for the purpose of compensating for the hysteresis effect is applied to find the compensation signal by searching the optimal control laws from the hysteresis output, which avoids the construction of the inverse hysteresis model. The adaptive neural digital controller is achieved by using a discrete-time neural network controller to realize the discretization of time and quantizing the control signal to realize the discretization of the amplitude. The adaptive neural digital controller ensures the semiglobally uniformly ultimately bounded (SUUB) of all signals in the closed-loop control system. The effectiveness of the proposed approach is validated via the magnetostrictive-actuated system.

Appointed-time prescribed performance control for 6-DOF spacecraft rendezvous and docking operations under input saturation

This paper presents a neural adaptive controller for a class of nonlinear system in the presence of input saturation and arbitrary unknown information by integrating the design framework of neural networks (NNs) with the dynamic surface control (DSC) and backstepping technique. NNs are directly used to deal with unknown nonlinear terms. By introducing a well-defined smooth function and a Nussbaum function, a novel robust adaptive control scheme is developed to guarantee the states of system to be semi-globally uniformly ultimately bounded (SGUUB). Owing to the introduction of DSC, the problem of ‘explosion of complexity’ inherent in the backstepping design is effectively eliminated. Simulation results demonstrate the effectiveness and simplicity of the proposed method.

This article considers the trajectory tracking control for unmanned surface vessels with unknown time-variant external disturbances and input saturation. The strategy mainly consists of event-triggered reset sub-controller and nonlinear disturbance observer–based compensation sub-controller. To reduce network transmissions, and in the meanwhile, guarantee the desirable closed-loop behavior, the event-triggered reset control is proposed where the reset law and the event-triggered mechanism are designed separately. Both of static and dynamic event-triggered reset controllers are designed. Their corresponding stability is demonstrated using Lyapunov stability theory. Finally, numerical simulation results are presented to demonstrate the effectiveness and robustness of the proposed trajectory tracking control strategy.

Fixed-time adaptive neural tracking control for high-order nonlinear switched systems with input saturation and dead-zone

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

In this brief, finite-time backstepping sliding mode control technique is investigated to handle with the yaw control of non-affine nonlinear unmanned aerial vehicle (UAV) helicopters with actuator faults and unknown disturbances. Using a taylor series expansion technique, the control input in non-affine nonlinear system is presented as an explicit expression, which is convenient for controller design. Based on it, nonlinear disturbance observer is designed to estimate unknown compound disturbance. Furthermore, finite-time convergence backstepping sliding mode controller is designed to force tracking errors to small bounded sets in finite time, in which dynamic surface control avoids explosion of complexity and the sliding mode variable enhances system robustness. Then, the Lyapunov function is employed to verify the stability of the control system. Finally, the validity of proposed scheme adopted in yaw-channel dynamic model of UAV helicopters under actuator faults is demonstrated via simulation results.

Incremental sliding-mode control and allocation for morphing-wing aircraft fast manoeuvring