[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

This paper addresses a problem of optimal resource allocation for technology innovation ecosystem. Two layers goal are realized. First, an uncertain dynamical system is constructed with the Lotka–Volterra model for the description of the dynamics of the concerned innovation ecosystem, in which the growth rate and the symbiosis coefficient are specially treated as possibly (fast) time-varying uncertain parameters. Second, an adaptive robust resource allocation strategy is designed to drive the errors between the populations and the desired populations to be uniformly bounded and uniformly ultimately bounded, such that the first layer (basic) goal of resource allocation is achieved. Third, a two-player cooperative game is formulated for the seeking of the optimal control parameters, in which two (static) cost functions and a comprehensive performance index are constructed by performance analysis, and then the optimal control parameters (i.e., the Pareto-optimality) are obtained by minimizing the comprehensive performance index, such that the second layer (further) goal of optimal balance between the control inputs and the system performance is achieved. This work should be the first effort that addresses the optimal resource allocation and control for technology innovation ecosystem through a creative combination of dynamic modeling, control theory and game theory. 

Optimal resource allocation and adaptive robust control of technology innovation ecosystems based on cooperative game theory

The constraint following control problem of the mechanical systems is investigated in this paper. For the practical mechanical system, there always exists the time-varying uncertainty, which may be due to the system parameter perturbation, the modeling error, and the external disturbance. The uncertainty will result in the constraint following performance degradation. To solve this issue, a new adaptive robust control is proposed to force the system to satisfy the constraint. The control scheme consists of two parts: the nominal part and the adaptive robust part. The nominal part is proposed based on the Udwadia-Kalaba theory, which can guarantee the constraint following performance of the nominal system. The adaptive robust part is proposed to compensate for the uncertainty. In addition, the adaptive law is proposed to estimate the uncertainty bound of the system. The resulting adaptive robust control can guarantee the uniform boundedness and uniform ultimate boundedness of the constraint following system in the presence of various uncertainties, which is verified by a two-link Revolute-Prismatic Mechanical Manipulator (RPMM) system.

Adaptive Robust Constraint Following Control for Mechanical Systems

Uncertainties exist in control fields which have a significant impact both on system performance and control cost. A fuzzy approach is applied to describe uncertainties of a collaborative robot based on the membership function. Then a high-order robust control is designed to compensate for uncertainties. The performances of uniform boundedness and uniform ultimate boundedness are obtained. To increase system performance and decrease control cost, the optimal control parameters are solved based on game theory to balance performance and cost. In the end, the control effects are verified based on simulation results by comparing with other classical control methods.

High-Order Robust Control with Optimized Parameters for Collaborative Robots

Newton's third law of motion has two co-existing forms: the weak form and the strong form. The weak form asserts that the internals forces are equal (in magnitude) and opposite (in direction), while the strong form asserts the additional central (or collinear) condition. It has always been puzzling to the engineering and science community as to exactly which one is correct. This paper endeavors to resolve this discrepancy via control theory. It considers a problem of particle dynamics as a planar affine nonlinear system. The forces sustain particles motion, which consist of internal and external forces, may be viewed as functions of time and particles' motions. To verify if the internal forces are able to sustain the motion, a nonlinear controllability theorem is applied. We are able to deduce a definitive conclusion on which form of the Newton's third law is valid.

Controllability verifications of Newton's third law

The robust bounded control problem for the permanent magnet linear motors with inequality constraints is studied. Firstly, the dynamical model of the system is built, through the state transformation is used to satisfy the inequality constraint of the position output. Thus, the controller after the state transformation can ensure that the control output of the linear motor stays the desired range. Selecting the appropriate boundary function, and then the upper and lower bounds of the control output can be set according to our control requirements. The control scheme can guarantee uniform boundedness and uniform ultimate boundedness. The results of experiments and simulation show that the proposed algorithm can assure the control output within the desired range regardless of the uncertainty.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09864602.pdf

Ye-Hwa Chen

Papers

Optimal resource allocation and adaptive robust control of technology innovation ecosystems based on cooperative game theory

Adaptive Robust Constraint Following Control for Mechanical Systems

High-Order Robust Control with Optimized Parameters for Collaborative Robots

Controllability verifications of Newton's third law

Robust Bounded Control Design and Experimental Verification for Permanent Magnet Linear Motor With Inequality Constraints