Nonlinear Model Predictive Control for Cooperative Control and Estimation

Recent advances in computational power have made it possible to do expensive online computations for control systems. It is becoming more realistic to perform computationally intensive optimization schemes online on systems that are not intrinsically stable and/or have very small time constants. Being one of the most important optimization based control approaches, model predictive control (MPC) has attracted a lot of interest from the research community due to its natural ability to incorporate constraints into its control formulation. Linear MPC has been well researched and its stability can be guaranteed in the majority of its application scenarios. However, one issue that still remains with linear MPC is that it completely ignores the system\u27s inherent nonlinearities thus giving a sub-optimal solution. On the other hand, if achievable, nonlinear MPC, would naturally yield a globally optimal solution and take into account all the innate nonlinear characteristics. While an exact solution to a nonlinear MPC problem remains extremely computationally intensive, if not impossible, one might wonder if there is a middle ground between the two. We tried to strike a balance in this dissertation by employing a state representation technique, namely, the state dependent coefficient (SDC) representation. This new technique would render an improved performance in terms of optimality compared to linear MPC while still keeping the problem tractable. In fact, the computational power required is bounded only by a constant factor of the completely linearized MPC. The purpose of this research is to provide a theoretical framework for the design of a specific kind of nonlinear MPC controller and its extension into a general cooperative scheme. The controller is designed and implemented on quadcopter systems

Nonlinear Model Predictive Control for Unmanned Aerial Vehicles

This paper discusses the derivation and implementation of a nonlinear model predictive control law for tracking reference trajectories and constrained control of a quadrotor platform. The approach uses the state-dependent coefficient form to capture the system nonlinearities into a pseudo-linear system matrix. The state-dependent coefficient form is derived following a rigorous analysis of aerial vehicle dynamics that systematically accounts for the peculiarities of such systems. The same state-dependent coefficient form is exploited for obtaining a nonlinear equivalent of the model predictive control. The nonlinear model predictive control law is derived by first transforming the continuous system into a sampled-data form and and then using a sequential quadratic programming solver while accounting for input, output and state constraints. The boundedness of the tracking errors using the sampled-data implementation is shown explicitly. The performance of the nonlinear controller is illustrated through representative simulations showing the tracking of several aggressive reference trajectories with and without disturbances

Poly-dopamine, poly-levodopa, and poly-norepinephrine coatings: Comparison of physico-chemical and biological properties with focus on the application for blood-contacting devices

Thanks to its simplicity, versatility, and secondary reactivity, dopamine self-polymerized coatings (pDA) have been widely used in surface modification of biomaterials, but the limitation in secondary molecular grafting and the high roughness restrain their application in some special scenarios. Therefore, some other catecholamine coatings analog to pDA have attracted more and more attention, including the smoother poly-norepinephrine coating (pNE), and the poly-levodopa coating (pLD) containing additional carboxyl groups. However, the lack of a systematic comparison of the properties, especially the biological properties of the above three catecholamine coatings, makes it difficult to give a guiding opinion on the application scenarios of different coatings. Herein, we systematically studied the physical, chemical, and biological properties of the three catecholamine coatings, and explored the feasibility of their application for the modification of biomaterials, especially cardiovascular materials. Among them, the pDA coating was the roughest, with the largest amount of amino and phenolic hydroxyl groups for molecule grafting, and induced the strongest platelet adhesion and activation. The pLD coating was the thinnest and most hydrophilic but triggered the strongest inflammatory response. The pNE coating was the smoothest, with the best hemocompatibility and histocompatibility, and with the strongest cell selectivity of promoting the proliferation of endothelial cells while inhibiting the proliferation of smooth muscle cells. To sum up, the pNE coating may be a better choice for the surface modification of cardiovascular materials, especially those for vascular stents and grafts, but it is still not widely recognized

Facile immobilization of vascular endothelial growth factor on a tannic acid-functionalized plasma-polymerized allylamine coating rich in quinone groups

Biomolecules like VEGF with thiol or amine groups can easily be covalently immobilized onto a Tannic Acid functional plasma polymerized allylamine surface rich in quinone groups in a mild alkali buffer solution based on Schiff base or Michael addition reactions.</p