In this paper, an adaptive trajectory tracking control algorithm for underactuated unmanned surface vessels (USVs) with guaranteed transient performance is proposed. To meet the realistic dynamical model of USV, we consider that the mass and damping matrices are not diagonal and the input saturation problem. Neural Networks (NNs) are employed to approximate the unknown external disturbances and uncertain hydrodynamics of USVs. Moreover, both full state feedback control and output feedback control are presented, and the unmeasurable velocities of the output feedback controller are estimated via a high-gain observer. Unlike the conventional control methods, we employ the error transformation function to guarantee the transient tracking performance. Both simulation and experimental results are carried out to validate the superior performance via comparing with traditional potential integral (PI) control approaches.

This note studies the self-triggered MPC problem for constrained discrete-time nonlinear systems. A novel self-triggered MPC algorithm enabling the codesign of triggering and control is proposed, in which the communication cost is explicitly considered in the total cost function, and both the control and triggering intervals can be optimally designed. For constrained nonlinear systems, the theoretical conditions on ensuring feasibility and closed-loop stability are developed, and the achieved control and communication performance is proved to be guaranteed. In particular, the parameter design approaches for satisfying feasibility and stability conditions are further presented in the case of quadratic cost functions. Furthermore, stronger results are provided for linear case. Finally, the effectiveness and advantages are verified via simulation and comparison studies.

This paper studies the precedence-constrained task assignment problem for a team of heterogeneous vehicles to deliver packages to a set of dispersed customers subject to precedence constraints that specify which customers need to be visited before which other customers. A truck and a micro drone with complementary capabilities are employed where the truck is restricted to travel in a street network and the micro drone, restricted by its loading capacity and operation range, can fly from the truck to perform the last-mile package deliveries. The objective is to minimize the time to serve all the customers respecting every precedence constraint. The problem is shown to be NP-hard, and a lower bound on the optimal time to serve all the customers is constructed by using tools from graph theory. Then, integrating with a topological sorting technique, several heuristic task assignment algorithms are proposed to solve the task assignment problem. Numerical simulations show the superior performances of the proposed algorithms compared with popular genetic algorithms.

This note is concerned with the distributed optimal control of multi-agent systems (MASs) with general linear dynamics. The cooperative tracking model consisting of multiple followers and one leader is constructed. Distributed optimal controllers are presented using the linear quadratic regulator, which guarantee the leader-following consensus of MASs and the optimization of global performance index. The global information of topology is unneeded for solutions of optimization equations. The topology graph is a general digraph, and the only requirement is that the digraph should contain a directed spanning tree. Numerical examples are finally reported to demonstrate the efficiency of presented approach.

This paper proposes a novel adaptive control methodology based on the admittance model for multiple manipulators transporting a rigid object cooperatively along a predefined desired trajectory. First, an admittance model is creatively applied to generate reference trajectory online for each manipulator according to the desired path of the rigid object, which is the reference input of the controller. Then, an innovative integral barrier Lyapunov function is utilized to tackle the constraints due to the physical and environmental limits. Adaptive neural networks (NNs) are also employed to approximate the uncertainties of the manipulator dynamics. Different from the conventional NN approximation method, which is usually semiglobally uniformly ultimately bounded, a switching function is presented to guarantee the global stability of the closed loop. Finally, the simulation studies are conducted on planar two-link robot manipulators to validate the efficacy of the proposed approach.