Effect of elastic modulus on inertial displacement of cell-like particles in microchannels.

Label-free microfluidic-based cell sorters leverage innate differences among cells (e.g., size and stiffness), to separate one cell type from another. This sorting step is crucial for many cell-based applications. Polystyrene-based microparticles (MPs) are the current gold standard for calibrating flow-based cell sorters and analyzers; however, the deformation behavior of these rigid materials is drastically different from that of living cells. Given this discrepancy in stiffness, an alternative calibration particle that better reflects cell elasticity is needed for the optimization of new and existing microfluidic devices. Here, we describe the fabrication of cell-like, mechanically tunable MPs and demonstrate their utility in quantifying differences in inertial displacement within a microfluidic constriction device as a function of particle elastic modulus, for the first time. Monodisperse, fluorescent, cell-like microparticles that replicate the size and modulus of living cells were fabricated from polyacrylamide within a microfluidic droplet generator and characterized via optical and atomic force microscopy. Trajectories of our cell-like MPs were mapped within the constriction device to predict where living cells of similar size/modulus would move. Calibration of the device with our MPs showed that inertial displacement depends on both particle size and modulus, with large/soft MPs migrating further toward the channel centerline than small/stiff MPs. The mapped trajectories also indicated that MP modulus contributed proportionally more to particle displacement than size, for the physiologically relevant ranges tested. The large shift in focusing position quantified here emphasizes the need for physiologically relevant, deformable MPs for calibrating and optimizing microfluidic separation platforms.

Scalable high-throughput acoustophoresis in arrayed plastic microchannels.

Microfluidic acoustophoresis is a label-free technique that isolates a purified product from a complex mixture of cells. This technique is well-studied but thus far has lacked the throughput and device manufacturability needed for many medical and industrial uses. Scale-up of acoustofluidic devices can be more challenging than in other microfluidic systems because the channel walls are integral to the resonant behavior and coupling to neighboring channels can inhibit performance. Additionally, the increased device area needed for parallel channels becomes less practical in the silicon or glass materials usually used for acoustofluidic devices. Here, we report an acoustic separator with 12 parallel channels made entirely from polystyrene that achieves blood cell separation at a flow rate greater than 1 ml/min. We discuss the design and optimization of the device and the electrical drive parameters and compare the separation performance using channels of two different designs. To demonstrate the utility of the device, we test its ability to purify lymphocytes from apheresis product, a process that is critical to new immunotherapies used to treat blood cancers. We process a leukapheresis sample with a volume greater than 100 ml in less than 2 h in a single pass without interruption, achieving greater than 90% purity of lymphocytes, without any prepurification steps. These advances suggest that acoustophoresis could in the future aid in cell therapy bioprocessing and that further scale-up is possible.

Rapid prototyping and parametric optimization of plastic acoustofluidic devices for blood-bacteria separation.

Acoustic manipulation has emerged as a versatile method for microfluidic separation and concentration of particles and cells. Most recent demonstrations of the technology use piezoelectric actuators to excite resonant modes in silicon or glass microchannels. Here, we focus on acoustic manipulation in disposable, plastic microchannels in order to enable a low-cost processing tool for point-of-care diagnostics. Unfortunately, the performance of resonant acoustofluidic devices in plastic is hampered by a lack of a predictive model. In this paper, we build and test a plastic blood-bacteria separation device informed by a design of experiments approach, parametric rapid prototyping, and screening by image-processing. We demonstrate that the new device geometry can separate bacteria from blood while operating at 275% greater flow rate as well as reduce the power requirement by 82%, while maintaining equivalent separation performance and resolution when compared to the previously published plastic acoustofluidic separation device.

A nonlinear regression model-based predictive control algorithm.

This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.

Application of infinite model predictive control methodology to other advanced controllers.

This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (m-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system.

Real-time comparison of a number of predictive controllers.

Many model predictive control (MPC) algorithms have been proposed in the literature depending on the conditionality of the system matrix and the choice of its cost-function. This paper presents the newer MPC schemes such as extended predictive control (EPC) and shifted MPC as well as other well known forms. The control performance of these controllers are compared using two systems that are slow and fast reacting. The closed-loop responses are compared and the differences and similarities are explained on the basis of the structure of the control schemes. Disturbance rejection and the tracking of various setpoint trajectories are performed with good closed-loop results from all the controllers. It was found that the controllers that were specifically designed to reduce the system matrix ill-conditionality such as EPC and generalized predictive control provided better control performance when compared to other MPC methods.

Properties of extended predictive control.

Many tuning strategies for model predictive control algorithms have been proposed in the literature depending on the conditionality of the system matrix and the choice of its cost function. In this paper, the properties of a new predictive controller termed extended predictive control (EPC) are investigated and presented. These properties are important to the understanding of the unique tuning strategy of EPC. EPC is based on the assumption of infinite horizon which is preferable to guarantee stability. The EPC properties are derived using a second order plant with relatively large dead time and is applicable to any open-loop stable system. The tuning strategy of EPC was applied to generalized predictive control with good results.

MIMO extended predictive control-implementation and robust stability analysis.

The objective of this work is to develop a new tuning strategy for multivariable extended predictive control (EPC). A natural concern is the problem of ill conditionality in controlling multi-input multi-output (MIMO) systems. The main advantage of EPC is that it has a simple and effective tuning strategy that results in a well-conditioned system which can achieve tight closed-loop response. Moreover, unlike most existing model predictive control tuning strategies, the proposed strategy establishes a direct relationship between one main tuning parameter for each subprocess of the MIMO system. This tuning method has been derived based on the assumption of an infinite control horizon resulting in powerful stability for the nominal case and in the presence of model uncertainty. This tuning method is applicable to unconstrained multivariable processes, and was proven to have good control on nonsquare systems. The main features of the new tuning strategy are practically illustrated on a MIMO temperature system with improved control performance as compared to move suppressed predictive control.

Risk aversion predictive control.

A quality-controlled predictive control method, suitable for control of fast, remote systems subject to significant communication delays, is developed. Each move is quality controlled in that it independently satisfies a risk-based control performance criterion. The method is found to be capable of mitigating the ill effects of highly nonstationary delay distributions while providing good control performance for milder nonstationarity. It is demonstrated on simplified predictive control (SPC) of a single-input, single-output process. SPC is preferred here due to its simplicity and well-conditioned dependence of both the sampling time and its single parameter.

SISO extended predictive control: implementation and robust stability analysis.

The proposed algorithm of extended predictive control (EPC) represents an exact method for removing the ill-conditioning in the system matrix by developing a unique weighting structure for any control horizon. The main feature of the EPC algorithm is that it uses the condition number of the system matrix to evaluate a single tuning parameter that provides a specified closed-loop response. Robust analysis demonstrated that EPC is more robust in comparison with move-suppressed and m-shifted predictive controllers in all aspects of process variation in gain, delay, and time-constant ratios. Tuning of EPC is effective and simple since there is a direct relationship between closed-loop performance and its tuning parameter.

SISO extended predictive control-formulation and the basic algorithm.

A new predictive controller is developed that represents a significant change from conventional model predictive control. The method termed extended predictive control (EPC) uses one tuning parameter, the condition number of the system matrix to provide an easy-to-follow tuning procedure. EPC drastically improves the system matrix conditionality resulting in faster closed-loop response without oscillatory transients. The control performance of EPC is compared with the original move suppressed and recently derived shifted predictive controllers, with improved results.

Development of characteristic equations and robust stability analysis for SISO move suppressed and shifted DMC.

New controller and closed loop transfer functions for move suppressed and shifted dynamic matrix control were derived in order to compare the controller robustness on several plants as a function of tuning parameters lambda and m. The derivation of these transfer functions are for any order plant requiring its open loop step or impulse response. A generic control design algorithm was developed for selecting the controller tuning parameters using controller robustness as a performance index, in the presence of plant parameter variations and uncertainties. Shifted dynamic matrix control (DMC) was found to be more robust with respect to all plant parameter variations, and therefore more suited than move suppressed DMC to control plants with wide ranging parameters. This result was demonstrated on an experimental direct current servomotor system, and further verified on a plant having a cascade control structure with the (m , m) being the most robust to plant variations.

Development of characteristic equations and robust stability analysis for MIMO move suppressed and shifted DMC.

Discrete-time controller and closed-loop transfer functions were developed for move suppressed lambda and the recently formulated m-shifted multiple-input-multiple-output (MIMO) dynamic matrix control (DMC). Using these transfer functions, robust analyses were conducted for MIMO plants by varying corresponding delay and gain ratios of the system. In all instances, robust plots indicate that the shifted DMC is less sensitive and hence more robust to variations in the plant parameters than move suppressed DMC. It was shown that the design of these MIMO DMC controllers depends on the plant closed-loop performance and overall stability, since the selection of lambda and m directly influences the plant robustness and closed-loop dynamics.

On simplified predictive control as a generalization of least-squares dynamic matrix control.

Simplified predictive control (SPC) of a single-input single-output control scheme is compared to the more sophisticated, least-squares formulation of dynamic matrix control (DMC) and its move-suppressed variant (move-suppressed DMC) for a typical two time-step control horizon. A closed-loop, continuous analysis shows that the discrete form of SPC generalizes the discrete DMC algorithm, and its variants, to control responses faster than one-half the process response time while remaining well conditioned.

Online optimization of fuzzy-PID control of a thermal process.

A constrained optimization of a simple fuzzy-PID (PID-proportional integral derivative) system is designed for the online improvement of PID control performance during productive control runs. The cost function design yields a desirable balance between rise time, setpoint overshoot, and settling time to the setpoint. The constraints determined by simulation yield control performance no worse than the existing control performance during online optimization. The optimized fuzzy-PID system is compared to a similarly optimized PID controller and a benchmark model predictive controller.

Continuous analysis of move suppressed and shifted DMC.

"Shifted DMC" (shifted dynamic matrix control) has been empirically shown to have significant improved closed-loop control characteristics over "move-suppressed DMC" where, in the latter, diagonal terms of the dynamic matrix DMC prediction model are augmented to reduce numerical ill conditioning. An added benefit of shifted DMC was that the so-called "shifting parameter," replacing the move suppression parameter, was easily found from the open-loop response. Therefore a novel analytical method, based on a closed form, continuous approximation to closed-loop DMC control, is introduced here and used to quantify the previous empirical results. The dependence of slow and fast time scales of the closed-loop response on the parameters is examined for move-suppressed and shifted DMC methods. It is found that in move-suppressed DMC the slow control time scale is sharply dependent upon the sampling time and move-suppression parameter and that these difficulties are eliminated in shifted DMC.

Self-optimizing MPC of melt temperature in injection moulding.

The parameters in plastic injection moulding are highly nonlinear and interacting. Good control of plastic melt temperature for injection moulding is very important in reducing operator setup time, assuring consistent product quality, and preventing thermal degradation of the melt. Step response testing was performed on the barrel heating zones on an industrial injection moulding machine (IMM). The open loop responses indicated a high degree of process coupling between the heating zones. From these experimental step responses, a multiple-input-multiple-output model predictive control strategy was developed and practically implemented. The requirement of negligible overshoot is important to the plastics industry for preventing material overheating and wastage, and reducing machine operator setup time. A generic learning and self-optimizing MPC methodology was developed and implemented on the IMM to control melt temperature for any polymer to be moulded on any machine having different electrical heater capacities. The control performance was tested for varying setpoint trajectories typical of normal machine operations. The results showed that the predictive controller provided good control of melt temperature for all zones with negligible oscillations, and, therefore, eliminated material degradation and extended machine setup time.

PID gain scheduling using fuzzy logic.

A simple, yet robust and stable alternative to proportional, integral, derivative (PID) gain scheduling is developed using fuzzy logic. This fuzzy gain scheduling allows simple online duplication of PID control and the online improvement of PID control performance. The method is demonstrated with a physical model where PID control performance is improved to levels comparable to model predictive control. The fuzzy formulation is uniquely characterized by; (i) one fuzzy input variable involving the PID manipulated variable, (ii) two parameters to be tuned, while previously tuned PID parameters are retained, and (iii) a gain scheduling differential equation which relates the fuzzy and conventional PID manipulated variables and enables fuzzy gain scheduling.