In this article, we address the bearing-only formation control problem of 3-D networked robotic systems with parametric uncertainties. The contributions of this article are two-fold: 1) the bearing-rigid theory is extended to solve the nonlinear robotic systems with the Euler-Lagrange-like model and 2) a novel almost global stable distributed bearing-only formation control law is proposed for the nonlinear robotic systems. Specifically, the robotic systems subject to nonholonomic constraints and dynamics are first transformed into a Euler-Lagrange-like model. By exploring the bearing-rigid graph theory, a backstepping approach is used to design the distributed formation controller. Simulations for 3-D robotics are given to demonstrate the effectiveness of the proposed control law. Compared to the distance-rigid formation control approach, the bearing-rigid approach guarantees almost global stability while naturally excluding flip ambiguities.

Cooperative control of multi-agent systems (MAS) on communication networks has received a great deal of attention, mostly for the case of homogeneous agents which all have the same dynamics. An advantage of cooperative synchronization mechanisms is their local distributed nature, which makes them scalable to large networks. However, most existing design mechanisms require some global information, such as the leader's dynamics or global graph information, so that the control protocols are technically not fully distributed. Moreover, the distributed nature of the control protocols makes them susceptible to faults or uncertainties. In this paper, we study heterogeneous MAS, where all agents may have different dynamics. We provide adaptive resilience mechanisms for rejecting actuator faults, and guarantee exponential convergence of synchronization errors, whereas most existing results on actuator faults guarantee only boundedness of errors. Finally, we provide algorithms that are fully distributed, requiring no knowledge of either the leader's dynamics or of graph properties.

In this article, we consider the resilience problem in the presence of communication faults encountered in distributed secondary voltage and frequency control of an islanded alternating current microgrid. Such faults include the partial failure of communication links and some classes of data manipulation attacks. This practical and important yet challenging issue has been taken into limited consideration by existing approaches, which commonly assume that the measurement or communication between the distributed generations (DGs) is ideal or satisfies some restrictive assumptions. To achieve communication resilience, a novel adaptive observer is first proposed for each individual DG to estimate the desired reference voltage and frequency under unknown communication faults. Then, to guarantee the stability of the closed-loop system, voltage and frequency restoration, and accurate power sharing regardless of unknown communication faults, sufficient conditions are derived. Some simulation results are presented to verify the effectiveness of the proposed secondary control approach.

In this article, we investigate the optimal output tracking problem for linear discrete-time systems with unknown dynamics using reinforcement learning (RL) and robust output regulation theory. This output tracking problem only allows to utilize the outputs of the reference system and the controlled system, rather than their states, and differs from most existing works that depend on the state of the system. The optimal tracking problem is formulated into a linear quadratic regulation problem by proposing a family of dynamic discrete-time controllers. Then, it is shown that solving the output tracking problem is equivalent to solving output regulation equations, whose solution, however, requires the knowledge of the complete and accurate system dynamics. To remove such a requirement, an off-policy RL algorithm is proposed using only the measured output data along the trajectory of the system and the reference output. By introducing reexpression error and analyzing the rank condition of the parameterization matrix, we ensure the uniqueness of the proposed RL-based optimal control via output feedback.