Barrier Lyapunov functions for Nussbaum gain adaptive control of full state constrained nonlinear systems

This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there is no need to get the prior knowledge about the uncertainty and a sufficient amount of observed data. Also, impedance learning is introduced to tackle the interaction between the robot and its environment, so that the robot follows a desired destination generated by impedance learning. A barrier Lyapunov function is used to address the effect of state constraints. With the proposed control, the stability of the closed-loop system is achieved via Lyapunov’s stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty. Some simulation studies are carried out to illustrate the effectiveness of the proposed scheme.

Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning

This paper presents a design method for output-feedback path-following control of under-actuated autonomous underwater vehicles moving in a vertical plane without using surge, heave, and pitch velocities. Specifically, an extended state observer (ESO) is developed to recover the unmeasured velocities as well as to estimate total uncertainty induced by internal model uncertainty and external disturbance. At the kinematic level, a commanded guidance law is developed based on a vertical line-of-sight guidance scheme and the observed velocities. To optimize guidance signals, optimization-based reference governors are formulated as bound-constrained quadratic programming problems for computing optimal reference signals. Two globally convergent recurrent neural networks called projection neural networks are used to solve the optimization problems in real-time. Based on the optimal reference signals and ESO, a kinetic control law with disturbance rejection capability is constructed at the kinetic level. It is proved that all error signals in the closed-loop system are uniformly and ultimately bounded. Simulation results substantiate the efficacy of the proposed method for output-feedback path-following of under-actuated autonomous underwater vehicles.

Output-Feedback Path-Following Control of Autonomous Underwater Vehicles Based on an Extended State Observer and Projection Neural Networks

Fuzzy logic control, due to its simple control structure, easy and cost-effective design, has been successfully employed to the application of guidance and control in robotic fields. This paper aims to review fuzzy-logic-based guidance and control in an important branch of robots—marine robotic vehicles. First, guidance and motion forms including the maneuvering, path following, trajectory tracking, and position stabilization are described. Subsequently, the application of three major classes of fuzzy logic control, including the conventional fuzzy control (Mamdani fuzzy control and Takagi–Sugeno–Kang fuzzy control), adaptive fuzzy control (self-tuning fuzzy control and direct/indirect adaptive fuzzy control), and hybrid fuzzy control (fuzzy PID control, fuzzy sliding mode control, and neuro-fuzzy control) are presented. In particular, we summarize the design and analysis process of direct/indirect adaptive fuzzy control and fuzzy PID control in marine robotic fields. In addition, two comparative results between hybrid fuzzy control and the corresponding single control are provided to illustrate the superiority of hybrid fuzzy control. Finally, trends of the fuzzy future in marine robotic vehicles are concluded based on its state of the art.

Survey on Fuzzy-Logic-Based Guidance and Control of Marine Surface Vehicles and Underwater Vehicles

In this paper, a neurodynamics-based output feedback scheme is proposed for distributed containment maneuvering of marine vessels guided by multiple parameterized paths without using velocity measurements. Each vessel is subject to internal model uncertainties and external disturbances induced by wind, waves, and ocean currents. In order to recover unmeasured velocity information as well as to identify unknown vessel dynamics, an echo state network (ESN) based observer using recorded input–output data is proposed for each vessel. Based on the observed velocity information of neighboring vessels, distributed containment maneuvering laws are developed at the kinematic level. Next, in order to shape the transient motion profile for vessel kinetics to follow, finite-time nonlinear tracking differentiators are employed to generate smooth reference signals as well as to extract the time derivatives of kinematic control laws. Finally, ESN-based dynamic control laws are constructed at the kinetic level. The stability of the closed-loop system is analyzed via input-to-state stability and cascade theory. Simulation results are provided to illustrate the efficacy of the proposed neurodynamics-based output feedback approach.

Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback

Adaptive sliding-mode attitude control for autonomous underwater vehicles with input nonlinearities

Decentralized formation control for multiple autonomous underwater vehicles (AUVs) with input saturation is discussed in this paper. In the assumed group communication, each AUV can only obtain information from its adjacent neighbors in the specified communication topology, and only a subset of the AUVs can access the desired trajectory. A robust integral of the sign of the error (RISE) feedback based distributed controller with an anti-windup compensator is proposed so that all group members ultimately synchronize and achieve the desired formation. Based on Lyapunov theory, it is proven that the formation error of each AUV converges to zero. Finally, a simulation example demonstrates the effectiveness of the algorithm.

Decentralized Formation Control of Multiple Autonomous Underwater Vehicles with Input Saturation Using RISE Feedback Method

In this paper, we focus on the cooperative tracking problem of multi-agent systems with nonlinear dynamics and communication time delays. Only a portion of agents can access the information of the desired trajectory and there are communication delays among the agents. Through designing an adaptive neural network based control law and constructing an appropriate Lyapunov-Krasovskii functional, it is proved that the tracking error of each agent converges to a neighborhood of zero. Simulation results are provided to show the effectiveness of the proposed algorithm.

Dong Cui

Papers

Adaptive sliding-mode attitude control for autonomous underwater vehicles with input nonlinearities

Decentralized Formation Control of Multiple Autonomous Underwater Vehicles with Input Saturation Using RISE Feedback Method

Cooperative tracking of multiple agents with uncertain nonlinear dynamics and fixed time delays