Safety-Preserving Lyapunov-Based Model Predictive Rendezvous Control for Heterogeneous Marine Vehicles Subject to External Disturbances.

This article investigates the cooperative rendezvous control problem for perturbed heterogeneous marine systems composed of an autonomous underwater vehicle (AUV) and an autonomous surface vehicle (ASV). A novel Lyapunov-based model predictive control (LMPC) framework is presented to accomplish safe and precise rendezvous under input limitations and external disturbances. First, by incorporating the prescribed performance control (PPC) technique into the LMPC framework, we transform the original ascending state of the AUV into a self-constrained state, which serves as the decision variable of the model predictive control (MPC) optimization problem. Then, PPC-aided auxiliary control laws based on disturbance observers (DOBs) are designed to establish a robust contractive constraint to provide stability margins. Combining the LMPC with the PPC technique makes the original state-constrained problem an equivalent state-constraint-free problem. By addressing the MPC problem for the equivalent unconstrained system, the proposed method preserves the rendezvous safety. With the robust contractive constraint, the proposed safety-preserving LMPC (SP-LMPC) controller can inherit robustness and stability from the robust auxiliary control laws. Furthermore, theoretical analyses are conducted to assess recursive feasibility and closed-loop stability. With comprehensive theoretical support, the proposed method provides a new framework to simultaneously address state constraints and disturbances for highly nonlinear marine systems. Finally, simulations and comparisons are conducted to demonstrate the effectiveness and advantages of the proposed algorithm.

Preparation and characterization of Y-doped microarc oxidation coating on AZ31 magnesium alloys.

The rapid degradation characteristics of magnesium alloys limit its application in the field of orthopedic fracture fixation and cardiovascular stents. This study aimed to improve the corrosion resistance and biocompatibility of AZ31 magnesium alloys and prepare degradable implant materials. Micro-arc oxidation (MAO) was used to change the concentration of yttrium acetate in the electrolyte to prepare coatings with different yttrium content on the surface of AZ31 magnesium alloy. Through characterization, it is proved that the yttrium in the coating mainly exists in the form of Y3+. The polarization potential experiment shows that the micro-arc oxidation coating significantly improves the corrosion resistance of magnesium alloys. With the increase of yttrium acetate concentration in the electrolyte, the corrosion resistance of the coating first increases and then weakens. When the concentration is 0.0035 mol/L, the coating has the highest corrosion resistance. The results of CCK-8 cytotoxicity experiment and cell morphology observation also proved that the cell viability in each group was greater than 140%, and the yttrium-doped coating on the surface of AZ31 magnesium alloy has no cytotoxicity, can promote cell growth, and has good biocompatibility.

Trajectory Tracking Control of Autonomous Ground Vehicles Using Adaptive Learning MPC.

In this work, an adaptive learning model predictive control (ALMPC) scheme is proposed for the trajectory tracking of perturbed autonomous ground vehicles (AGVs) subject to input constraints. In order to estimate the unknown system parameter, we propose a set-membership-based parameter estimator based on the recursive least-squares (RLS) technique with the ensured nonincreasing estimation error. Then, the estimated system parameter is employed in MPC to improve the prediction accuracy. In the proposed ALMPC scheme, a robustness constraint is introduced into the MPC optimization to handle parametric and additive uncertainties. For the designed robustness constraint, its shape is decided off-line based on the invariant set, whereas its shrinkage rate is updated online according to the estimated upper bound of the estimation error, leading to further reduced conservatism and slightly increased computational complexity compared with the robust MPC methods. Furthermore, it is theoretically shown that the proposed ALMPC algorithm is recursively feasible under some derived conditions, and the closed-loop system is input-to-state stable (ISS). Finally, a numerical example and comparison study are conducted to illustrate the efficacy of the proposed method.

Event-Based Rendezvous Control for a Group of Robots With Asynchronous Periodic Detection and Communication Time Delays.

In this paper, we propose an event-triggered rendezvous control method for multiple two-wheeled mobile robots (2WMRs) subject to time-varying communication delays. By checking the integral-type event-triggering conditions asynchronously and periodically, each 2WMR determines whether or not to sample and broadcast its states. When the information used in an agent's controller is updated, the 2WMR calculates its x (or y ) control input, and then a Rotate&Compensate&Run Rendezvous Scheme is provided for 2WMRs to update their states. We present a sufficient condition for 2WMRs to asymptotically reach rendezvous, and the convergence analysis is conducted using the Lyapunov functional approach. Experiments are further presented to validate the effectiveness of the proposed control method.