Mechanical Engineering - Research Publications
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ItemA UNIFYING FRAMEWORK FOR ANALYSIS AND DESIGN OF EXTREMUM SEEKING CONTROLLERSNesic, D ; Tan, Y ; Manzie, C ; Mohammadi, A ; Moase, W (IEEE, 2012-01-01)We summarize a unifying design approach to continuous-time extremum seeking that was recently reported by the authors. This approach is based on a feedback control paradigm that was to the best of our knowledge explicitly summarized for the first time in this form in our recent work. This paradigm covers some existing extremum seeking schemes, provides a direct link to off-line optimization and can be used as a unifying framework for design of novel extremum seeking schemes. Moreover, we show that other extremum seeking problem formulations can be interpreted using this unifying viewpoint. We believe that this unifying view will be invaluable to systematically design and analyze extremum seeking controllers in various settings.
ItemExtremum Seeking Methods for Online Automotive CalibrationManzie, C ; Moase, W ; Shekhar, R ; Mohammadi, A ; Nesic, D ; Tan, Y ; Waschl, H ; Kolmanovsky, I ; Steinbuch, M ; del Re, L (Springer, 2014-01-01)The automotive calibration process is becoming increasingly difficult as the degrees of freedom in modern engines rises with the number of actuators. This is coupled with the desire to utilise alternative fuels to gasoline and diesel for the promise of lower CO2 levels in transportation. However, the range of fuel blends also leads to variability in the combustion properties, requiring additional sensing and calibration effort for the engine control unit (ECU). Shifting some of the calibration effort online whereby the engine controller adjusts its operation to account for the current operating conditions may be an effective alternative if the performance of the controller can be guaranteed within some performance characteristics. This tutorial chapter summarises recent developments in extremum seeking control, and investigates the potential of these methods to address some of the complexity in developing fuel-flexible controllers for automotive powertrains.
ItemExtremum seeking methods for online optimization of spark advance in alternative fueled enginesMohammadi, A ; Manzie, C ; Nešić, D (Elsevier, 2012-01-01)Alternative fueled engines offer greater challenges for engine control courtesy of uncertain fuel composition. This make optimal tuning of input parameters like spark advance extremely difficult in most existing ECU architectures. This paper proposes the use of greybox extremum seeking techniques to provide real-time optimization of the spark advance in alternative fueled engines. The ability and flexibility of the proposed framework is demonstrated through simulation examples. The approaches demonstrated may be extended to other engine inputs requiring online optimization.
ItemOnline optimization of spark advance in alternative fueled engines using extremum seeking controlMohammadi, A ; Manzie, C ; Nesic, D (Elsevier, 2014-08-01)Alternative fueled engines offer greater challenges for engine control courtesy of uncertain fuel composition. This makes optimal tuning of input parameters like spark advance extremely difficult in most existing ECU architectures. This paper proposes the use of grey-box extremum seeking techniques to provide real-time optimization of the spark advance in alternative fueled engines. Since practical implementation of grey-box extremum seeking methods is typically done using digital technology, this paper takes advantage of emulation design methods to port the existing continuous-time grey-box extremum seeking methods to discrete-time frameworks. The ability and flexibility of the proposed discrete-time framework is demonstrated through simulations and in practical situation using a natural gas fueled engine.
ItemA Systematic Approach to Extremum Seeking Based on Parameter EstimationNesic, D ; Mohammadi, A ; Manzie, C (IEEE, 2010-01-01)We present a systematic approach for design of extremum seeking (ES) controllers for a class of uncertain plants that are parameterized with unknown parameters. First, we present results for static plants and show how it is possible to combine, under certain general conditions, an arbitrary optimization method with an arbitrary parameter estimation method in order to obtain extremum seeking. Our main results also specify how controller needs to be tuned in order to achieve extremum seeking. Then, we consider dynamic plants and separate our results into the stable plant case and unstable plant case. For each of these cases, we present conditions on general plants, controllers, observers, parameter estimators and optimization algorithms that guarantee semi-global practical convergence to the extremum when controller parameters are tuned appropriately. Our results apply to general nonlinear plants with multiple inputs and multiple parameters.
ItemEmulation Design for a Class of Extremum Seeking Controllers: Case Studies in ABS Design and Spark Timing CalibrationMohammadi, A ; Nesic, D ; Manzie, C (IEEE, 2013-01-01)The vast majority of extremum seeking designs in the literature are in continuous-time, however their practical implementation is typically done using digital technology. In this paper, a sampled-data implementation of extremum seeking controllers using emulation design methods is studied to address this gap. The conditions under which the emulated controller preserves the performance of the continuous-time plant are investigated. The main result also provides a guideline on how to tune the controller parameters including sample period in order to achieve the desired performance. The examples of anti-lock braking and spark timing calibration are used to illustrate the proposed design method through simulation and experimental tests.
ItemA Framework for Extremum Seeking Control of Systems With Parameter UncertaintiesNesic, D ; Mohammadi, A ; Manzie, C (Institute of Electrical and Electronics Engineers, 2013-02-01)Traditionally, the design of extremum seeking algorithm treats the system as essentially a black-box, which for many applications means disregarding known information about the model structure. In contrast to this approach, there have been recent examples where a known plant structure with uncertain parameters has been used in the online optimization of plant operation. However, the results for these approaches have been restricted to specific classes of plants and optimization algorithms. This paper seeks to provide general results and a framework for the design of extremum seekers applied to systems with parameter uncertainties. General conditions for an optimization method and a parameter estimator are presented so that their combination guarantees convergence of the extremum seeker for both static and dynamic plants. Tuning guidelines for the closed loop scheme are also presented. The generality and flexibility of the proposed framework is demonstrated through a number of parameter estimators and optimization algorithms that can be combined to obtain extremum seeking. Examples of anti-lock braking and model reference adaptive control are used to illustrate the effectiveness of the proposed framework.