Mechanical Engineering - Research Publications
Permanent URI for this collection
Search Results
1 - 8 of 8
-
ItemOnline optimization of spark advance in alternative fueled engines using extremum seeking control(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.
-
ItemMulti-agent source seeking via discrete-time extremum seeking control(PERGAMON-ELSEVIER SCIENCE LTD, 2014-09)Recent developments in extremum seeking theory have established a general framework for the methodology, although the specific implementations, particularly in the context of multi-agent systems, have not been demonstrated. In this work, a group of sensor-enabled vehicles is used in the context of the extremum seeking problem using both local and global optimisation algorithms to locate the extremum of an unknown scalar field distribution. For the former, the extremum seeker exploits estimates of gradients of the field from local dithering sensor measurements collected by the mobile agents. It is assumed that a distributed coordination which ensures uniform asymptotic stability with respect to a prescribed formation of the agents is employed. An inherent advantage of the frameworks is that a broad range of nonlinear programming algorithms can be combined with a wide class of cooperative control laws to perform extreme source seeking. Semi-global practical asymptotically stable convergence to local extrema is established in the presence of field sampling noise. Subsequently, global extremum seeking with multiple agents is investigated and shown to give rise to robust practical convergence whose speed can be improved via computational parallelism. Nonconvex field distributions with local extrema can be accommodated within this global framework.
-
ItemExtremum seeking of dynamical systems via gradient descent and stochastic approximation methods(Elsevier, 2015-06)Abstract This paper examines the use of gradient based methods for extremum seeking control of possibly infinite-dimensional dynamic nonlinear systems with general attractors within a periodic sampled-data framework. First, discrete-time gradient descent method is considered and semi-global practical asymptotic stability with respect to an ultimate bound is shown. Next, under the more complicated setting where the sampled measurements of the plant’s output are corrupted by an additive noise, three basic stochastic approximation methods are analysed; namely finite-difference, random directions, and simultaneous perturbation. Semi-global convergence to an optimum with probability one is established. A tuning parameter within the sampled-data framework is the period of the synchronised sampler and hold device, which is also the waiting time during which the system dynamics settle to within a controllable neighbourhood of the steady-state input–output behaviour.
-
ItemMulti-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery(ELSEVIER SCIENCE BV, 2016-12-15)The accurate online state estimation for some types of nonlinear singularly perturbed systems is challenging due to extensive computational requirements, ill-conditioned gains and/or convergence issues. This paper proposes a multi-time-scale estimation algorithm for a class of nonlinear systems with coupled fast and slow dynamics. Based on a boundary-layer model and a reduced model, a multi-time-scale estimator is proposed in which the design parameter sets can be tuned in different time-scales. Stability property of the estimation errors is analytically characterized by adopting a deterministic version of extended Kalman filter (EKF). This proposed algorithm is applied to estimator design for the state-of-charge (SOC) and state-of-health (SOH) in a lithium-ion battery using the developed reduced order battery models. Simulation results on a high fidelity lithium-ion battery model demonstrate that the observer is effective in estimating SOC and SOH despite a range of common errors due to model order reductions, linearisation, initialisation and noisy measurement.
-
ItemA Framework for Simplification of PDE-Based Lithium-Ion Battery Models(Institute of Electrical and Electronics Engineers (IEEE), 2016)Simplified models are commonly used in battery management and control, despite their (often implicit) limitations in capturing the dynamic behavior of the battery across a wide range of operating conditions. This paper seeks to develop a framework for battery model simplification starting from an initial high-order physics-based model that will explicitly detail the assumptions underpinning the development of simplified battery models. Starting from the basis of a model capturing the electrochemical, thermal, electrical, and aging dynamics in a set of partial differential equations, a systematic approach based on singular perturbations and averaging is used to simplify the dynamics through identification of disparate timescales inherent in the problem. As a result, libraries of simplified models with interconnections based on the specified assumptions are obtained. A quantitative comparison of the simplified models relative to the original model is used to justify the model reductions. To demonstrate the utility of the framework, a set of battery charging strategies is evaluated at reduced computational effort on simplified models.
-
ItemMesh adaptation in direct collocated nonlinear model predictive control(WILEY, 2018-10-01)Summary Direct methods are often deployed to solve nonlinear model predictive control problems where the optimal control problem is first transcribed into a nonlinear program and then solved to obtain the control input. This makes the computational cost of direct methods nontrivial; however, efficiencies can be gained by utilizing adaptation methods during transcription. Goal‐oriented a priori error estimation is used as an adaptation strategy. Unlike other strategies, the refinement is directly related to the cost function. Therefore, refinement only occurs where it is needed with respect to the cost function. Two examples are presented and an improvement of up to 50% in the computational time is observed with no degradation in the closed‐loop performance.
-
ItemNo Preview Available
-
ItemFast Calibration of a Robust Model Predictive Controller for Diesel Engine Airpath(IEEE, 2020-07)A significant challenge in the development of control systems for diesel airpath applications is to tune the controller parameters to achieve satisfactory output performance, especially while adhering to input and safety constraints in the presence of unknown system disturbances. Model-based control techniques, such as model predictive control (MPC), have been successfully applied to multivariable and highly nonlinear systems, such as diesel engines, while considering operational constraints. However, efficient calibration of typical implementations of MPC is hindered by the high number of tuning parameters and their nonintuitive correlation with the output response. In this paper, the number of effective tuning parameters is reduced through suitable structural modifications to the controller formulation and an appropriate redesign of the MPC cost function to aid rapid calibration. Furthermore, a constraint tighteninglike approach is augmented to the control architecture to provide robustness guarantees in the face of uncertainties. A switched linear time-invariant MPC strategy with recursive feasibility guarantees during controller switching is proposed to handle transient operation of the engine. The robust controller is first implemented on a high-fidelity simulation environment, with a comprehensive investigation of its calibration to achieve desired transient response under step changes in the fuelling rate. An experimental study then validates and highlights the performance of the proposed controller architecture for the selected tunings of the calibration parameters for fuelling steps and over drive cycles.