Water Surface and Ground Control of a Small Cross-Domain Robot Based on Fast Line-of-Sight Algorithm and Adaptive Sliding Mode Integral Barrier Control  
[PERSON]  
School of Computer Science and Engineering, Nanjing University of Science and Technology,  
Nanjing 210094, China; [EMAIL_ADDRESS] (K.W. ); [EMAIL_ADDRESS] (C.H. ); [EMAIL_ADDRESS] (P.C.) [PERSON] [PERSON]  
School of Computer Science and Engineering, Nanjing University of Science and Technology,  
Nanjing 210094, China; [EMAIL_ADDRESS] (K.W. ); [EMAIL_ADDRESS] (C.H. ); [EMAIL_ADDRESS] (P.C.) [PERSON]  
control [17,18]. Those methods have been applied to WMR or USV. In addition, in [19], model predictive control is used to design the yaw angle controller, and fuzzy rules are used to optimize the steering controller of WMR. In [20], the backstepping-based fuzzy adaptive rate is used to achieve cooperation control of multiple USVs. In addition, there are more intelligent methods, such as the reinforcement learning, deep reinforcement learning, etc. [21,22]. It is also an effective method to use observers dealing with uncertain model parameters as well as external disturbances. An extended state observer (ESO) is used to obtain the uncertainty of the WMR dynamics model [23]. In [24], external disturbances are observed by a linear ESO with data-driven structural improvement. A radial basis function neural network is designed to approximate the system model uncertainty [25]. An extended disturbance observer is used to compensate the output of adaptive TSMC [26]. In [27], a fast finite-time convergence extend state observer (FFESO) is designed to observe the uncertainties in the dynamics model of an autonomous underwater vehicle (AUV). When designing controllers for USVs or WMRs, only the performance index requirements in a single environment are considered. However, the performances of a CDR on the water surface and ground are different, so the state constraints of CDR in different environments should be considered. Barrier control is an effective method for state constraints [28,29]. In [30], based on tan-type barrier Lyapunov function (BLF), a barrier controller (BC) is applied to constrain the full state of USV. The log-type barrier Lyapunov function is used to design the controller to constrain the velocity and position of the robotic arm [31]. Integral barrier Lyapunov function (iBLF) is used to design controllers that directly constrain the system states. In [32], a time-varying bound line of iBLF is considered for a strict-feedback system. Based on [32], a novel iBLF is proposed to design a controller that can constrain the velocity and angular velocity states of the hoverraft [33]. A line of sight (LOS) navigation algorithm can adjust the yaw angle in advance and be applied in a variety of unmanned systems. However, a traditional LOS algorithm cannot eliminate the trajectory tracking error in the presence of lateral velocity. Therefore, in [34], a reduced-order ESO is used to observe the side-slip angle caused by lateral velocity. In [35], a finite-time LOS method is used to get the side-slip angle by designing a finite-time convergence observer. In [36], another kind of finite-time convergence observer is used to observe the side-slip angle. It is also a brilliant way to use adaptive law to get the side-slip angle [37]. Through the discussion of the control methods above and inspired by references [16,32], an adaptive sliding model integral barrier control (ASMIBC) algorithm is proposed in this paper to constrain the yaw angular velocity and velocity of the robot on the ground and water surface. Adaptive law is designed for unknown and time-varying lumped disturbances. To eliminate the influence of lateral velocity on the trajectory tracking of CDR, a finite-time fast line of sight (FLOS) is proposed, and the side-slip angle is obtained by a finite-time extended state observer (FESO) [38] without small angle assumption. Several scenarios are designed for simulation and comparative analysis. The main contributions of this paper are as follows. 1.