[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Droplet based microfluidics is a rapidly growing interdisciplinary field of research combining soft matter physics, biochemistry and microsystems engineering. Its applications range from fast analytical systems or the synthesis of advanced materials to protein crystallization and biological assays for living cells. Precise control of droplet volumes and reliable manipulation of individual droplets such as coalescence, mixing of their contents, and sorting in combination with fast analysis tools allow us to perform chemical reactions inside the droplets under defined conditions. In this paper, we will review available drop generation and manipulation techniques. The main focus of this review is not to be comprehensive and explain all techniques in great detail but to identify and shed light on similarities and underlying physical principles. Since geometry and wetting properties of the microfluidic channels are crucial factors for droplet generation, we also briefly describe typical device fabrication methods in droplet based microfluidics. Examples of applications and reaction schemes which rely on the discussed manipulation techniques are also presented, such as the fabrication of special materials and biophysical experiments.

https://iopscience.iop.org/article/10.1088/0034-4885/75/1/016601/pdf

Droplet based microfluidics

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society

Feedback systems: Input-output properties

In this paper, a feedback linearization (FL)-based control law made implementable using an extended state observer (ESO) is proposed for the trajectory tracking control of a flexible-joint robotic system. The FL-based controller cannot be implemented unless the full transformed state vector is available. The design also requires exact knowledge of the system model making the controller performance sensitive to uncertainties. To address these issues, an ESO is designed, which estimates the state vector, as well as the uncertainties in an integrated manner. The FL controller uses the states estimated by ESO, and the effect of uncertainties is compensated by augmenting the FL controller with the ESO-estimated uncertainties. The closed-loop stability of the system under the proposed observer-controller structure is established. The effectiveness of the ESO in the estimation of the states and uncertainties and the effectiveness of the FL + ESO controller in tracking are demonstrated through simulations. Lastly, the efficacy of the proposed approach is validated through experimentation on Quanser's flexible-joint module.

Extended-State-Observer-Based Control of Flexible-Joint System With Experimental Validation

The authors propose an adaptive robust control approach for the levitation control of non-linear maglev vehicle with state constraint. The system contains non-linear and (possibly) time-varying uncertainty, which is supposed to be bounded. In order to prevent the undesirable collision, the airgap between suspended chassis and guideway should be restrained in a specified range for safety concerns. Furthermore, the maglev vehicle does not satisfy the (global) matching condition. The authors propose a three-step state transformation approach to transform the maglev vehicle to an interconnected uncertain system. After that, the robust control is proposed based on the transformed system, and the adaptive law is constructed to emulate the total system uncertainty. The adaptive robust control is able to ensure the system performance (uniform boundedness and the uniform ultimate boundedness) of uncertain maglev vehicle. In addition, the airgap can be confined within the specified range.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cta.2017.1348

Adaptive robust constrained state control for non-linear maglev vehicle with guaranteed bounded airgap

The design of robust control for an uncertain dynamical system is first considered. The only information on the uncertainty, which is needed for the design, is its possible bound. The (non-adaptive) robust control design based on this information may sometimes not be feasible or practical. Thus the adaptive version of the non-adaptive robust control is desirable to estimate the bound. Two general routines are constructed such that the corresponding adaptive versions of all the non-adaptive controls can be constructed. The adaptive control is applicable to mismatched uncertainty, measurement noise and the model-following problem. The theory is illustrated via a mechanical manipulator.

Robust control system design: non-adaptive versus adaptive

This paper investigates the robust control for dynamical systems subject to uncertainty. The uncertainty is assumed to be (possibly fast) time varying and bounded. The bound is unknown but lies within a prescribed fuzzy set (hence the fuzzy dynamical system). We propose an approach for the robust control design which is implemented in two steps. First, a class of robust controls is proposed based on tunable parameters. The proposed controls are deterministic and are not conventionally IF-THEN rules based. By the Lyapunov minimax approach, we prove that the proposed controls are able to guarantee deterministic system performance, namely, uniform boundedness and ultimate uniform boundedness. Second, optima seeking from the proposed controls is considered to improve fuzzy system performance. We formulate the optima-seeking problem as a two-player (one leader and one follower) Stackelberg game by developing two cost functions, each of which is in charge of one tunable parameter (i.e., the player). Each cost function consists of an average fuzzy system performance index and the associated player’s control effort. We show that the solution of the optimal design problem (i.e., the optima of the tunable parameters), which is called the Stackelberg strategy, always exists and how to obtain the backwards-induction outcome is provided. Simulation results on the walking control of a biped robot model are presented for demonstration.

Stackelberg-Theoretic Approach for Performance Improvement in Fuzzy Systems

The problem of stabilization of a class of nonlinear systems with time-varying uncertain parameters is considered. The uncertainty is unknown but lies within a prescribed set. The nominal system is assumed to be feedback-equivalent to a controllable-like canonical form. A Lyapunov function based robust controller is incorporated with a feedback linearization technique to guarantee practical stability. >

Robust control of nonlinear uncertain systems: a feedback linearization approach

Controlling underactuated systems is a challenging problem in control engineering. This paper presents a novel constraint-following approach for control design of an underactuated two-wheeled mobile robot (2 WMR), which has two degrees-of-freedom (DOF) to be controlled but only one actuator. The control goal is to drive the 2 WMR to follow a set of constraints, which may be holonomic or nonholonomic constraints. The constraint is considered in a more general form than the previous studies on constraint-following control (hence including a wider range of constraints). No auxiliary variables or pseudo variables are required for the control design. The proposed control only uses physical variables. We show that the proposed control is able to deal with both holonomic and nonholonomic constraints by forcing the constraint-following error to converge to zero, even if the system is not initially on the constraint manifold. Using this control design, we investigate two cases regarding different constraints on the 2 WMR motion, one for a holonomic constraint and the other for a nonholonomic constraint. Simulation results show that the proposed control is able to drive the 2 WMR to follow the constraints in both cases. Furthermore, the standard linear quadratic regulator (LQR) control is applied as a comparison in the simulations, which reflects the advantage of the proposed control.

Ye-Hwa Chen

Papers

Adaptive robust constrained state control for non-linear maglev vehicle with guaranteed bounded airgap

Robust control system design: non-adaptive versus adaptive

Stackelberg-Theoretic Approach for Performance Improvement in Fuzzy Systems

Robust control of nonlinear uncertain systems: a feedback linearization approach

Controlling an Underactuated Two-Wheeled Mobile Robot: A Constraint-Following Approach