Complementary Sensitivity Design of PID Controllers for Arbitrary-Order Transfer Functions With Time Delay

A graphical technique for finding all proportional integral derivative (PID) controllers that stabilize a given single-input-single-output (SISO) linear time-invariant (LTI) system of any order system with time delay has been solved. In this paper a method is introduced that finds all PID controllers that also satisfy an H∞ complementary sensitivity constraint. This problem can be solved by finding all PID controllers that simultaneously stabilize the closed-loop characteristic polynomial and satisfy constraints defined by a set of related complex polynomials. A key advantage of this procedure is the fact that it does not require the plant transfer function, only its frequency response.

A Stochastic Optimal Control Approach for Power Management in Plug-In Hybrid Electric Vehicles

This paper examines the problem of optimally splitting driver power demand among the different actuators (i.e., the engine and electric machines) in a plug-in hybrid electric vehicle (PHEV). Existing studies focus mostly on optimizing PHEV power management for fuel economy, subject to charge sustenance constraints, over individual drive cycles. This paper adds three original contributions to this literature. First, it uses stochastic dynamic programming to optimize PHEV power management over a distribution of drive cycles, rather than a single cycle. Second, it explicitly trades off fuel and electricity usage in a PHEV, thereby systematically exploring the potential benefits of controlled charge depletion over aggressive charge depletion followed by charge sustenance. Finally, it examines the impact of variations in relative fuel-to-electricity pricing on optimal PHEV power management. The paper focuses on a single-mode powersplit PHEV configuration for mid-size sedans, but its approach is extendible to other configurations and sizes as well.

Variable Powder Flow Rate Control in Laser Metal Deposition Processes

This paper proposes a novel method, called Variable Powder Flow Rate Control (VPFRC), for the regulation of powder flow rate in laser metal deposition processes. The idea of VPFRC is to adjust the powder flow rate to maintain a uniform powder deposition per unit length even when disturbances occur (e.g., the motion system accelerates and decelerates). Dynamic models of the powder delivery system motor and the powder transport system (i.e., five–meter pipe, powder dispenser, and cladding head) are constructed. A general tracking controller is then designed to track variable powder flow rate references. Since the powder flow rate at the nozzle exit cannot be directly measured, it is estimated using the powder transport system model. The input to this model is the DC motor rotation speed, which is estimated on–line using a Kalman filter. Experiments are conducted to examine the performance of the proposed control methodology. The experimental results demonstrate that the VPFRC method is successful in maintaining a uniform track morphology, even when the motion system accelerates and decelerates.

Effect of Interaction Modeling in AFM-Based Nanomanipulation

AFM-based nanomanipulation is very challenging because of the complex mechanics in tip-sample interactions and the limitations in AFM visual sensing capabilities. In the present paper, we investigate the modeling of AFM-based nanomanipulation emphasizing the effects of the relevant interactions at the nanoscale. The major contribution of the present work is the use of a combined DMT-JKR interaction model in order to describe the complete collision process between the AFM tip and the sample. The coupling between the interactions and the friction at the nanoscale is emphasized. The efficacy of the proposed model to reproduce experimental data is demonstrated via numerical simulations.

High-Performance Motion Control With Slosh: A Constrained Sliding Mode Approach

The problem of controlling the rectilinear motion of an open container without exceeding a prescribed liquid level and other constraints is considered using a recently-developed constrained sliding mode control design methodology based on invariant cylinders. A conventional sliding mode regulator is designed first to address nominal performance in the sliding mode. Then an robustly-invariant cylinder is constructed and used to describe the set of safe initial conditions from which the closed-loop controller can be operated without constraint violation. Simulations of a typical transfer illustrate the usefulness of the method in an industrial setting. Experimental results corresponding to a high-speed transfer validate the theory.

Strain Sensing With Piezoelectric Zinc Oxide Thin Films for Vibration Suppression in Hard Disk Drives

This paper describes the integration of thin film ZnO strain sensors onto hard disk drive suspensions for improved vibration suppression for tracking control. Sensor location was designed using an efficient optimization methodology based on linear quadratic gaussian (LQG) control. Sensors were fabricated directly onto steel wafers that were subsequently made into instrumented suspensions. Prototype instrumented suspensions were installed into commercial hard drives and tested. For the first time, a sensing signal was successfully obtained while the suspension was flying on a disk as in normal drive operation. Preliminary models were identified from experimental transfer functions. Nominal H2 control simulations demonstrated improved vibration suppression as a result of both the better resolution and higher sensing rate provided by the sensors.

Differential Flatness-Based Robust Control of a Two-Wheeled Mobile Robot in the Presence of Slip

In this paper, we present two robust trajectory-tracking controllers for a differentially driven two-wheeled mobile robot using its kinematic and dynamic model in the presence of slip. The structure of the differential flatness-based controller, which is an integrated framework for planning and control, is extended in this paper to account for slip disturbances by adding a corrective control term. Simulation results for both kinematic and dynamic controllers are presented to demonstrate the effectiveness of the robust controllers. Experiments with the kinematic controller were conducted to validate the performance of the robust controller. The simulation and experimental results show that the robust controllers are very effective in the presence of slip.

Estimation of Ethanol Content in Flex-Fuel Vehicles Using an Exhaust Gas Oxygen Sensor: Model, Tuning and Sensitivity

Throughout the history of the automobile there have been periods of intense interest in using ethanol as an alternative fuel to petroleum-based gasoline and diesel derivatives. Currently available flexible fuel vehicles (FFVs) can operate on a blend of gasoline and ethanol in any concentration of up to 85% ethanol. In all these FFVs, the engine management system relies on the estimation of the ethanol content in the fuel blend, which typically depends on the estimated changes in stoichiometry through an Exhaust Gas Oxygen (EGO) sensor. Since the output of the EGO sensor is used for the air-to-fuel ratio (AFR) regulation and the ethanol content estimation, several tuning and sensitivity problems arise. In this paper, we develop a simple phenomenological model of the AFR control process and a simple ethanol estimation law which can be representative of the currently practiced system in FFVs. Tuning difficulties and interactions of the two learning loops are then elucidated using classical control techniques. The sensitivity of the ethanol content estimation with respect to sensor and modeling errors is also demonstrated via simulations. The results point to an urgent need for model-based analysis and design of the AFR controller, the ethanol adaptation law and the fault detection issues in FFVs. Tuning and sensitivity issues are demonstrated via simulations and limitations are also discussed.

Mutual Identifiability Analysis for Parameter Signature Isolation in the Time-Scale Plane

It was shown recently that regions in the time-scale plane can be isolated wherein the prediction error can be attributed to the error of an individual model parameter. A necessary condition for this isolation capacity is the mutual (pairwise) identifiability of the model parameters. This paper presents conditions for mutual identifiability of parameters of linear models and refines these conditions for models that exhibit rank-1 dependency on the parameters.

Optimal Foraging Theory: Lessons and Application to Adaptive Engineering Systems

In biological systems, optimal strategy is generally defined as optimizing fitness, measured as reproductive value (RV), the expectation of producing surviving offspring from time t onward, given that an organism is in state S(t). Any action can be associated with an expectation of immediate reproductive success. Maximum RV results from the action that maximizes the sum of immediate and future surviving offspring. Adaptive biological behavior is the product of historical experience, heritability, individual variation, and differential fitness among individuals. Foraging tasks are a standard test bed for robot research because of their applicability to many problems. Optimal foraging theory offers explanations and predictions with direct applicability to engineering problems. Much theory development involves optimal solutions based on complete information about the system, but animals do not always conform to predictions of such models. Adaptive approximations to optimality in biological systems offer models for design of engineered systems.