Povezivanje identifikacije i optimalnog upravljanja s ograničenjima za po dijelovima afine sustave

In this paper we focus on identification and time optimal control of nonlinear processes modeled as piecewise affine systems. We combine the piecewise ARX process model identification based on clustering and the constrained time optimal controller design for discrete-time piecewise affine systems. The two procedures are improved and bound into a systematic procedure for design of high-performance nonlinear control systems: from the identification data to the closed-form time optimal controller. We successfully experimentally verify the procedure on the electronic throttle control system case study.U radu se razmatra identifikacija i upravljanje nelinearnih procesa modeliranih po dijelovima afinim modelom. Povezuju se postupak identifikacije po dijelovima ARX modela procesa temeljen na uskupljavanju i postupak sinteze eksplicitnog vremenski optimalnog regulatora uz prisutna ograničenja za vremenski diskretne po dijelovima afine sustave. Ovaj je pristup pogodan za sintezu nelinearnog sustava upravljanja visokih zahtjeva, te je u ovom radu eksperimentalno provjeren na primjeru sustava upravljanja elektroničkom zaklopkom automobila

Nonlinear Optimal Generalized Predictive Functional Control applied to quasi-LPV model of automotive electronic throttle

A Nonlinear Optimal Generalized Predictive Functional Control algorithm is presented for the control of quasi linear parameter varying state-space systems. A scalar automotive electronic throttle body is simulated to demonstrate typical results. The controller structure is specified in a restricted structure form including a set of pre-specified linear transfer-functions and a vector of gains that are found to minimize a GPC cost-index. This approach enables a range of classical controller structures to be used in the feedback loop such as extended PI, PID or of a more general transfer-function form. The controller is introduced along with a dynamic cost-weighting tuning future. A simulation is used to validate the performance of the restricted structure controller for regulation and tracking problems assessed against automotive performance standards

Set-valued estimation of switching linear system: an application to an automotive throttle valve

This paper introduces a polyhedral approximation algorithm for set-valued estimation of switching linear systems. The algorithm generates set-valued estimates for any possible sequence of switching parameters, under the assumption that the system has unknown but bounded disturbances and measurement noises. Our algorithm has practical implications; namely, set-valued estimates were generated for the position and electrical current of a real-time automotive electronic throttle valve, and the corresponding experimental data demonstrate the practical benefits of our approach.Postprint (author's final draft

Learning Throttle Valve Control Using Policy Search

Abstract. The throttle valve is a technical device used for regulating a fluid or a gas flow. Throttle valve control is a challenging task, due to its complex dynamics and demanding constraints for the controller. Using state-of-the-art throttle valve control, such as model-free PID controllers, time-consuming and manual adjusting of the controller is necessary. In this paper, we investigate how reinforcement learning (RL) can help to alleviate the effort of manual controller design by automatically learning a control policy from experiences. In order to obtain a valid control policy for the throttle valve, several constraints need to be addressed, such as no-overshoot. Furthermore, the learned controller must be able to follow given desired trajectories, while moving the valve from any start to any goal position and, thus, multi-targets policy learning needs to be considered for RL. In this study, we employ a policy search RL approach, Pilco [2], to learn a throttle valve control policy. We adapt the Pilco algorithm, while taking into account the practical requirements and constraints for the controller. For evaluation, we employ the resulting algorithm to solve several control tasks in simulation, as well as on a physical throttle valve system. The results show that policy search RL is able to learn a consistent control policy for complex, real-world systems.

Machine Learning Techniques for High Performance Engine Calibration

Ever since the advent of electronic fuel injection, auto manufacturers have been able to increase fuel efficiency and power production, and to meet stricter emission standards. Most of these systems use engine sensors (Speed, Throttle Position, etc.) in concert with look-up tables to determine the correct amount of fuel to inject. While these systems work well, it is time and labor intensive to fine tune the parameters for these look-up tables. In general, automobile manufacturers are able to absorb the cost of this calibration since the variation between engines in a new model line is often small enough as to be inconsequential for a specific calibration. However, a growing number of drivers are interested in modifying their vehicles with the intent of improving performance. While some aftermarket performance upgrades can be accounted for by the original manufacturer equipped (OEM) electronic control unit (ECU), other more significant changes, such as adding a turbocharger or installing larger fuel injectors, require more drastic accommodations. These modifications often require an entirely new ECU calibration or an aftermarket ECU to properly control the upgraded engine. The problem is now that the driver becomes responsible for the calibration of the ECU for this "new" engine. However, most drivers are unable to devote the resources required to achieve a calibration of the same quality as the original manufacturers. At best, this results in reduced fuel economy and performance, and at worst, unsafe and possibly destructive operation of the engine. The purpose of this thesis is to design and develop--using machine learning techniques--an approximate predictive model from current engine data logs, which can be used to rapidly and incrementally improve the calibration of the engine. While there has been research into novel control methods for engine air-fuel ratio control, these methods are inaccessible to the majority of end users, either due to cost or the required expertise with engine calibration. This study shows that there is a great deal of promise in applying machine learning techniques to engine calibration and that the process of engine calibration can be expedited by the application of these techniques

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Optimization of Hydrogen-fueled Engine Ignition Timing Based on L-M Neural Network Algorithm

In view of the improvement measures of the optimization control algorithm for the ignition system of the hydrogen-fueled engine, the L-M neural network algorithm, Powell neural network algorithm and the traditional BP neural network algorithm are used to optimize the ignition system. The results showed that L-M algorithm not only can accurately predict the hydrogen-fueled engine ignition timing, but also has high precision, high convergence speed, a simple model and other outstanding advantages in the training process, which can greatly reduce the workload of human engine bench tests. Only a small amount of engine bench test is carried out, and the obtained sample data can be used to predict the ignition timing under the whole working conditions. The mean square error of the optimization results based on L-M algorithm arrives at 0.0028 after 100 times of calculation, the maximum value of absolute error arrives at 0.2454, and the minimum value of absolute error arrives at 0.00426

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system