A parameter adaptation method for a previously developed spark ignition (SI) to homogeneous charge compression ignition (HCCI) combustion mode transition control architecture is described. The goal of the adaptive method is to use transient SI–HCCI transition data gathered in online operation to tune the controller model parameters on a cylinder individual basis, in order to improve the accuracy of the controller's model-based calculations and account for cylinder to cylinder variability and drifts over time. The parameter adaptation is implemented on an experimental engine in an indirect adaptive control structure where the model parameters of the SI–HCCI transition controller are updated based on real-time measurements and used in subsequent model-based calculations. Comparison of SI–HCCI transition responses before and after adaptation at a single operating condition shows notable benefits from use of the adaptive method. When tested at differing operating points, the performance of the adapted controller remains overwhelmingly favorable to that of the baseline controller even when conditioned on data from only a single operating point.

This paper describes a model-based feedback control method to transition from spark ignition (SI) to homogeneous charge compression ignition (HCCI) combustion in gasoline engines. The purpose of the control structure is to improve robustness and reduce calibration complexity by incorporating feedback of the engine variables into nonlinear model-based calculations that inherently generalize across operating points. This type of structure is sought as an alternative to prior SI-HCCI transition approaches that involve open-loop calibration of input command sequences that must be scheduled by operating condition. The control architecture is designed for cam switching type SI-HCCI mode transition strategies with practical two-stage cam profile hardware, which previously have only been investigated in a purely open-loop framework. Experimental results on a prototype engine show that the control architecture is able to carry out SI-HCCI transitions across the HCCI load range at 2000 rpm engine speed while requiring variation of only one major set point and three minor set points with operating condition. These results suggest a noteworthy improvement in controller generality and ease of calibration relative to previous SI-HCCI transition approaches.

A methodology is introduced to analyze the drive cycle fuel economy of a vehicle equipped with a multimode combustion engine, utilizing commercial cam phasers and two-step cam profile switching. The analysis is based on a longitudinal vehicle model with manual transmission. The engine model employs a finite state machine describing mode switches between two distinct combustion modes, namely, spark-ignited and homogeneous charge compression ignition. Preliminary combustion mode switch experiments were used to parameterize the model. The influence of mode switch fuel penalties on drive cycle fuel economy was quantified through application of the model to the federal test procedure (FTP-75), the highway fuel economy test (HWFET) and the US06 supplemental federal test procedure. The mode switches were analyzed individually in terms of their benefit on fuel economy and a distinction was made between harmful and beneficial mode switches. Mode switches were defined as harmful whether their fuel penalty is greater than the benefits originating from spending time in the subsequent homogeneous charge compression ignition mode. A parametric study was conducted to investigate the impact of harmful mode switches on fuel economy as a function of the fuel penalties during the switch. In the case of high fuel penalties, supervisory control becomes an important tool for minimizing the number of harmful mode switches. One possible supervisory strategy discussed is a smoothing strategy, in which a mode switch is delayed by introducing a dwell time.

This article presents a low-order engine model to support model-based control development for mode transitions between spark ignition (SI) and homogeneous charge compression ignition (HCCI) combustion modes in gasoline engines. The modeling methodology focuses on cam switching mode transition strategies wherein the mode is abruptly changed between SI and recompression HCCI via a switch of the cam lift and phasing. The model is parameterized to a wide range of steady-state data which are selected to include conditions pertinent to cam switching mode transitions. An additional HCCI combustion model parameter is augmented and tuned based on transient data from SI to HCCI mode transitions where the conditions can be significantly outside any contained in the baseline steady-state parameterization. An adaptation routine is given which allows transient data be assimilated in online operation to update the augmented parameter and improve SI–HCCI transition predictions. With the baseline steady-state parameterization and augmented mode transition parameter, the model is shown to reproduce both steady-state data and transient performance output time histories from SI–HCCI transitions with considerable accuracy.

A low-order homogeneous charge compression ignition (HCCI) combustion model to support model-based control development for spark ignition (SI)/HCCI mode transitions is presented. Emphasis is placed on mode transition strategies wherein SI combustion is abruptly switched to recompression HCCI combustion through a change of the cam lift and opening of the throttle, as is often employed in studies utilizing two-stage cam switching devices. The model is parameterized to a steady-state dataset which considers throttled operation and significant air-fuel ratio variation, which are pertinent conditions to two-stage cam switching mode transition strategies. Inspection and simulation of transient SI to HCCI (SI-HCCI) mode transition data shows that the extreme conditions present when switching from SI to HCCI can cause significant prediction error in the combustion performance outputs even with the model's adequate steady-state fit. When a correction factor related to residual gas temperature is introduced to account for these extreme conditions, it is shown that the model reproduces transient performance output time histories in SI-HCCI mode transition data. The model is thus able to capture steady-state data as well as transient SI-HCCI mode transition data while maintaining a low-order cycle to cycle structure, making it tractable for model-based control of SI-HCCI mode transitions.

Objective. To explore the use of classical feedback control methods to achieve an improved deep brain stimulation (DBS) algorithm for application to Parkinson's disease (PD). Approach. A computational model of PD dynamics was employed to develop model-based rational feedback controller design. The restoration of thalamocortical relay capabilities to patients suffering from PD is formulated as a feedback control problem with the DBS waveform serving as the control input. Two high-level control strategies are tested: one that is driven by an online estimate of thalamic reliability, and another that acts to eliminate substantial decreases in the inhibition from the globus pallidus interna (GPi) to the thalamus. Control laws inspired by traditional proportional-integral-derivative (PID) methodology are prescribed for each strategy and simulated on this computational model of the basal ganglia network. Main Results. For control based upon thalamic reliability, a strategy of frequency proportional control with proportional bias delivered the optimal control achieved for a given energy expenditure. In comparison, control based upon synaptic inhibitory output from the GPi performed very well in comparison with those of reliability-based control, with considerable further reduction in energy expenditure relative to that of open-loop DBS. The best controller performance was amplitude proportional with derivative control and integral bias, which is full PID control. We demonstrated how optimizing the three components of PID control is feasible in this setting, although the complexity of these optimization functions argues for adaptive methods in implementation. Significance. Our findings point to the potential value of model-based rational design of feedback controllers for Parkinson's disease.

This paper takes a first step towards model-based feedback control for the transition between spark ignition (SI) and homogeneous charge compression ignition (HCCI) combustion modes by approaching the transfer out of SI operation during the SI into HCCI transition in a closed-loop control framework. The combustion mode switch is taken to be directly from SI to HCCI without an intermediate combustion mode between the two, and the HCCI phase of the transition is not addressed. The transfer out of SI operation is formulated as a multi-input, multi-output control problem with input and output constraints. A baseline feedback controller for the transfer is designed using linear quadratic regulator methods, and is tested in simulation on a nonlinear mean value engine model. A simple open-loop transition based on look-up table set points is included as well for comparison. The feedback controller shows the ability to complete the SI phase of the transition in a short number of cycles, while maintaining a minimal disturbance to the engine torque in comparison to the open-loop controller.Copyright © 2012 by ASME

This paper presents a control approach for turbocharged spark ignition engines that coordinates the throttle and wastegate air path actuators in order to improve driveability without significant compromise in efficiency. The coordinated controller is based on a two-input single-output (TISO) architecture which can be tuned through classical frequency response techniques using linearizations of a nonlinear mean value engine model. A systematic tuning method for the TISO architecture based on prior work is recommended and explained. The controller is evaluated in simulation with a one-dimensional GT-Power model and its performance is compared to that of a controller that does not coordinate the throttle and wastegate.

As our knowledge of Parkinson's disease increases, we are developing sophisticated computational models of the neuronal networks that go awry in this condition. Such models can form a test bed for the rational design of control strategies to reduce the pathological dynamics. Although deep brain stimulation is becoming increasingly popular for treating Parkinson's disease, all of the present stimulation scenarios involve open-loop stimulation that does not take into account the brain's dynamics through feedback. Most engineering control systems, such as automobile cruise controls, make use of variants of proportional-integral-differential (PID) control strategies in response to a measured quantity. We here design an optimized PID controller for Parkinson's disease modulation. We adapted the sparse-structured model of a small network of synaptically coupled neurons in several basal ganglia areas, originally described by [4], to an efficient structured Matlab implementation. The major outputs from the basal ganglia are inhibitory signals from the globus pallidus interna (GPi) to its localized target areas within thalamus. Correspondingly, a natural view is that parkinsonian basal ganglia activity is transduced into motor symptoms through effects on thalamic dynamics. Specifically, computational [2] and data-based [1] analysis shows that pathological GPi synaptic outputs can compromise thalamic relay of excitatory signals, as can be assessed by calculation of a reliability index or by estimation of GPi output patterns. We assume an overarching principle for control in Parkinson's disease – we never want to put more than 1 multicontact electrode shaft in the brain. We start with GPi as a target, since the clinical differences between GPi and STN stimulation effects have not been well documented in controlled studies, and the GPi is a larger and easier target into which to insert stereotactic electrodes (see discussion in [3]). We set up 2 empirical control schemes: 1) a reliability control based upon the fraction of the sensorimotor spikes, accurately relayed 1-for-1 by the thalamic cells, and a GPi synaptic control based upon the estimated effective output of the GPi. We contrast several proportional control schemes based upon reliability: a frequency-proportional case, and a frequency-proportional-biased case. Although the energy expenditure is almost identical for both cases, the reliability is higher with adding a bias term. We contrast several proportional control schemes using a GPi output controller. Using an amplitude-proportional-derivative control scheme, where the error is equal to the instantaneous calculated GPi synaptic output minus the average GPi output. We flip the sign of the derivative term, from positive to negative, to explore whether amplifying or suppressing the instantaneous response to the differential error is best. Use of a negative differential coefficient yields substantially greater reliability and lower energy expenditure. Finally, we exhaustively optimize a PID controller using proportional, derivative, and integral terms, as shown in as shown in Figure ​Figure11. Figure 1 Thalamic reliability as a function of derivative, kd, and integral, ki, terms following exhaustive optimization. We have shown the feasibility of rational PID controller design for Parkinson's disease. Both reliability, and GPi synaptic outputs, can be estimated from reduced model based sensors, and the results of the PID scheme instituted in trials with real feedback controllers. Developing these reduced models for incorporation into future devices is the next step of this research.