Behrouz Ebrahimi

Second-Order Sliding Mode Strategy for Air–Fuel Ratio Control of Lean-Burn SI Engines

Higher fuel economy and lower exhaust emissions for spark-ignition engines depend significantly on precise air-fuel ratio (AFR) control. However, the presence of large time-varying delay due to the additional modules integrated with the catalyst in the lean-burn engines is the primary limiting factor in the control of AFR. In this paper, the engine dynamics are rendered into a nonminimum phase system using Padé approximation. A novel systematic approach is presented to design a parameter-varying dynamic sliding manifold to compensate for the instability of the internal dynamics while achieving desired output tracking performance. A second-order sliding mode strategy is developed to control the AFR to remove the effects of time-varying delay, canister purge disturbance, and measurement noise. The chattering-free response of the proposed controller is compared with conventional dynamic sliding mode control. The results of applying the proposed method to the experimental data demonstrate improved closed-loop system responses for various operating conditions.

AQM controller design for TCP networks based on a new control strategy

When the network suffers from congestion, the core or edge routers signal the incidence of congestion through the active queue management (AQM) to the sources. The time-varying nature of the network dynamics and the complex process of retuning the current AQM algorithms for different operating points necessitate the development of a new AQM algorithm. Since the non-minimum phase characteristics of the network dynamics restrict direct application of the proportional-integral-derivative (PID) controller, we propose a compensated PID controller based on a new control strategy addressing the phase-lag and restrictions caused by the delay. Based on the unstable internal dynamics caused by the non-minimum phase characteristics, a dynamic compensator is designed and a PID controller is then allowed to meet the desired performance objectives by specifying appropriate dynamics for the tracking error. Since the controller gains are obtained directly from the dynamic model, the designed controller does not require to be tuned over the system operating envelop. Moreover, simulation results using ns2 show improvements over previous works especially when the range of variation of delay and model parameters are drastic. Simplicity, low computational cost, self-tuning structure and yet considerable improvement in performance are exclusive features of the proposed AQM for the edge or core routers.

A dynamic feedback control strategy for control loops with time-varying delay

Dynamic systems of nth order with time-varying delay in the control loop are examined in this paper. The infinite-dimensional pure delay problem is approximated using a jth-order Padé approximation. Although the approximation provides a well-matched finite-dimensional configuration, it poses a new challenge in terms of unstable internal dynamics for the resulted non-minimum phase system. Such a non-minimum phase characteristic limits the closed-loop system bandwidth and leads to an imperfect tracking performance. To circumvent this problem, the unstable internal dynamics of the system is captured and a new dynamic compensator is proposed to stabilise it in a systematic framework. A dynamic controller is developed, which provides the overall system stability against unmatched perturbation and meets the desired tracking error dynamics. The proposed approach is then applied to fuelling control in gasoline engines addressing the varying transport delay of the oxygen-sensor measurement in the exhaust. The developed methodology is finally validated on a Ford F-150 SI lean-burn engine model with large time-varying delay in the control loop.

A compensated PID active queue management controller using an improved queue dynamic model

Beside the major objective of providing congestion control, achieving predictable queuing delay, maximizing link utilization, and robustness are the main objectives of an active queue management AQM controller. This paper proposes an improved queue dynamic model while incorporating the packet drop probability as well. By applying the improved model, a new compensated PID AQM controller is developed for Transmission Control Protocol/Internet Protocol TCP/IP networks. The non-minimum phase characteristic caused by Pade approximation of the network delay restricts the direct application of control methods because of the unstable internal dynamics. In this paper, a parameter-varying dynamic compensator, which operates on tracking error and internal dynamics, is proposed to not only capture the unstable internal dynamics but also reduce the effect of uncertainties by unresponsive flows. The proposed dynamic compensator is then used to design a PID AQM controller whose gains are obtained directly from the state-space representation of the system with no further gain tuning requirements. The packet-level simulations using network simulator ns2 show the outperformance of the developed controller for both queuing delay stability and resource utilization. The improved underlying model leads also to the faster response of the controller. Copyright

Internal model control for a class of uncertain time-delay systems

Robust stability synthesis of a class of uncertain parameter-varying first-order time-delay systems is presented in this paper. Internal model principle is used to design a robust control using ℋ∞ small-gain theorem. The closed-loop system robustness is investigated against bounded variation of the parameters and sensitivity analysis is performed to determine the stability conditions and provide a systematic framework to derive an explicit delay-dependent stability bound for the tuning parameter. The tuning parameter is further refined through performance analysis incorporating the complementary sensitivity function. Finally, the closed-loop response of a typical delay system is demonstrated for various operating conditions and parameters variations.

Aggregate modeling and control of plug-in electric vehicles for renewable power tracking

A robust strategy is proposed in this paper to control the aggregate charging power of plug-in electric vehicles (PEVs). The charging flexibility of PEVs provides the intermittent renewable power sources with control authority to cope with load fluctuations caused by the variation of grid-connected PEVs population and their instantaneous power demand. In this paper, we consider an aggregate model of PEVs power in the form of a partial differential equation (PDE). A sliding mode control is then developed for the derived PDE load model with no discretization in the spatial domain. The developed sliding mode controller operates on the real-time measurable imbalance between source and demand power. To evaluate the closed-loop response and demonstrate the controllers robustness against PEVs population variations, a Monte Carlo simulation is performed for real driving conditions and using renewable power data.

Subsea production two-phase flow modeling and control of pipeline and manifold assemblies

Developed in this paper is a new approach to subsea production two-phase flow modeling and control of pipeline and manifold assemblies. For that purpose, a reduced-order model is developed for transient two-phase gas-liquid flow in pipelines. First, a mechanistic model is used to calculate the steady-state pressure drop and liquid holdup. From this model, effective fluid properties are calculated and used as arguments to the dissipative distributed parameter model. A modal approximation technique is then used to render the model into a rational polynomial form appropriate for time-domain analysis and controller design. A new low-frequency magnitude correction is applied to the approximated transfer functions providing an improved matching for the steady-state gain without affecting the dynamics of the system. The resulting low-dimensional two-phase flow model is then used to coordinate the arriving pressures at the manifold for different GVF levels through electro-hydraulic valves located at the wellheads.Copyright

A Systematic Air-Fuel Ratio Control Strategy for Lean-Burn SI Engines

Abstract In this paper, we present a new synthesis method to control air-fuel ratio (AFR) in lean-burn spark ignition engines to maximize the fuel economy. The major challenge in the control of AFR for lean-burn engines is the large time-varying delay in the control loop which restricts the application of conventional control techniques. Time-varying delay in the system dynamics is first approximated by the Pade approximation which renders the system dynamics into non-minimum phase characteristics. Application of dynamic compensators is invoked to retrieve unstable internal dynamics. The associated error dynamics is utilized to construct a PID controller combined with a dynamic compensator to track the desired AFR command using the feedback from the universal exhaust gas oxygen (UEGO) sensor. The proposed method is shown to achieve desired dynamic properties independent of the matched and unmatched disturbances. Results of applying the proposed method to experimental data demonstrate the closed-loop system stability and performance against time-varying delay, canister purge disturbances and measurement noise.

Cluster-based correlation of severe driving events with time and location

ABSTRACT In this article, a systematic strategy is proposed to identify severe driving events occurrence correlation with time and location. The proposed approach, which is constructed based on batch clustering and real-time clustering techniques, incorporates historical and real-time data to predict the time and location of severe driving events. Batch clustering is implemented with the combination of subtractive clustering and fuzzy c-means clustering to generate clusters representing the initial correlation patterns. Real-time clustering is then developed to create and update real-time correlation patterns on the foundation of the batch clustering using the evolving Gustafson–Kessel like (eGKL) algorithm. In both clustering processes, the correlation of the events within time domain is identified first, and then two different levels of accurate correlations are conducted for the location domain. Real-time data of operating vehicles each equipped with a data acquisition and wireless communication platform are used to validate the proposed strategy. Batch clustering results reveal the severe braking events distribution and concentration at daytime and nighttime. Real-time clustering provides and updates the variation of the correlations/intercorrelation of different regions. Drivers can be notified of the potential severe driving locations through maps showing the driving routes. Through the variation of the correlations, drivers can recognize the events occurrence at different times and locations. The generated time series can be potentially used to develop spatial-time models for regions to model and forecast the events occurrence.

Physics-Based Lumped-Parameter Modeling of Automotive Canister Fuel Purge

A physics-based model is presented to estimate the flow rate out of the fuel canister purged into the intake manifold. The lumped parameters of the model, including canister capacitance and flow resistance, are employed to obtain a first-order multi-input and single-output dynamic model. The vacuum pressure in the intake manifold and the fuel tank pressure serve as inputs, and the purged fuel flow rate is considered as the model output. The model does not require cumbersome computation, thereby allowing direct implementation in the fueling control to compensate for the extra fuel in regulation of the stoichiometric air–fuel ratio.