Adaptive Discrete Second Order Sliding Mode Control with Application to Nonlinear Automotive Systems

Sliding mode control (SMC) is a robust and computationally efficient model-based controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. However, the implementation of the conventional continuous-time SMC on digital computers is limited, due to the imprecisions caused by data sampling and quantization, and the chattering phenomena, which results in high frequency oscillations. One effective solution to minimize the effects of data sampling and quantization imprecisions is the use of higher order sliding modes. To this end, in this paper, a new formulation of an adaptive second order discrete sliding mode control (DSMC) is presented for a general class of multi-input multi-output (MIMO) uncertain nonlinear systems. Based on a Lyapunov stability argument and by invoking the new Invariance Principle, not only the asymptotic stability of the controller is guaranteed, but also the adaptation law is derived to remove the uncertainties within the nonlinear plant dynamics. The proposed adaptive tracking controller is designed and tested in real-time for a highly nonlinear control problem in spark ignition combustion engine during transient operating conditions. The simulation and real-time processor-in-the-loop (PIL) test results show that the second order single-input single-output (SISO) DSMC can improve the tracking performances up to 90%, compared to a first order SISO DSMC under sampling and quantization imprecisions, in the presence of modeling uncertainties. Moreover, it is observed that by converting the engine SISO controllers to a MIMO structure, the overall controller performance can be enhanced by 25%, compared to the SISO second order DSMC, because of the dynamics coupling consideration within the MIMO DSMC formulation.Comment: 12 pages, 7 figures, 1 tabl

Duino-Based Learning (DBL) in control engineering courses

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis document presents a project to develop freely redistributable materials to conduct educational lab projects with MATLAB, Simulink, Arduino and low-cost plants. This work materials introduce the fundamentals of Control Engineering through exercises and videos. Along with all this, the most important steps and issues appeared in the project are explained, so anyone interested on doing a project can have a starting point instead of starting a project from scratch, which most of times this results hard to implementPeer ReviewedPostprint (author's final draft

EASILY VERIFIABLE CONTROLLER DESIGN WITH APPLICATION TO AUTOMOTIVE POWERTRAINS

Bridging the gap between designed and implemented model-based controllers is a major challenge in the design cycle of industrial controllers. This gap is mainly created due to (i) digital implementation of controller software that introduces sampling and quantization imprecisions via analog-to-digital conversion (ADC), and (ii) uncertainties in the modeled plant’s dynamics, which directly propagate through the controller structure. The failure to identify and handle these implementation and model uncertainties results in undesirable controller performance and costly iterative loops for completing the controller verification and validation (V&V) process. This PhD dissertation develops a novel theoretical framework to design controllers that are robust to implementation imprecision and uncertainties within the models. The proposed control framework is generic and applicable to a wide range of nonlinear control systems. The final outcome from this study is an uncertainty/imprecisions adaptive, easily verifiable, and robust control theory framework that minimizes V&V iterations in the design of complex nonlinear control systems. The concept of sliding mode controls (SMC) is used in this study as the baseline to construct an easily verifiable model-based controller design framework. SMC is a robust and computationally efficient controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. The SMC structure allows for further modification to improve the controller robustness against implementation imprecisions, and compensate for the uncertainties within the plant model. First, the conventional continuous-time SMC design is improved by: (i) developing a reduced-order controller based on a novel model order reduction technique. The reduced order SMC shows better performance, since it uses a balanced realization form of the plant model and reduces the destructive internal interaction among different states of the system. (ii) developing an uncertainty-adaptive SMC with improved robustness against implementation imprecisions. Second, the continuous-time SMC design is converted to a discrete-time SMC (DSMC). The baseline first order DSMC structure is improved by: (i) inclusion of the ADC imprecisions knowledge via a generic sampling and quantization uncertainty prediction mechanism which enables higher robustness against implementation imprecisions, (ii) deriving the adaptation laws via a Lyapunov stability analysis to overcome uncertainties within the plant model, and (iii) developing a second order adaptive DSMC with predicted ADC imprecisions, which provides faster and more robust performance under modeling and implementation imprecisions, in comparison with the first order DSMC. The developed control theories from this PhD dissertation have been evaluated in real-time for two automotive powertrain case studies, including highly nonlinear combustion engine, and linear DC motor control problems. Moreover, the DSMC with predicted ADC imprecisions is experimentally tested and verified on an electronic air throttle body testbed for model-based position tracking purpose

Continuous time controller based on SMC and disturbance observer for piezoelectric actuators

Abstract – In this work, analog application for the Sliding Mode Control (SMC) to piezoelectric actuators (PEA) is presented. DSP application of the algorithm suffers from ADC and DAC conversions and mainly faces limitations in sampling time interval. Moreover piezoelectric actuators are known to have very large bandwidth close to the DSP operation frequency. Therefore, with the direct analog application, improvement of the performance and high frequency operation are expected. Design of an appropriate SMC together with a disturbance observer is suggested to have continuous control output and related experimental results for position tracking are presented with comparison of DSP and analog control application

Primjena optimalnog kliznog režima upravljanja u sekundarnoj regulaciji frekvencije i djelatne snage razmjene regulacijskim hidroelektranama

In this paper an optimal load-frequency controller for a nonlinear power system is proposed. The mathematical model of the power system consists of one area with several power plants, a few concentrated loads and a transmission network, along with simplified models of the neighbouring areas. Firstly, a substitute linear model is derived, with its parameters being identified from the responses of the nonlinear model. That model is used for load-frequency control (LFC) algorithm synthesis, which is based on discrete-time sliding mode control. Due to a non-minimum phase behaviour of hydro power plants, full-state feedback sliding mode controller must be used. Therefore, an estimation method based on fast output sampling is proposed for estimating the unmeasured system states and disturbances. Finally, the controller parameters are optimized using a genetic algorithm. Simulation results show that the proposed control algorithm with the proposed estimation technique can be used for LFC in a nonlinear power system.U radu se predlaže optimalna regulacija frekvencije i djelatne snage razmjene za nelinearni elektroenergetski sustav. Unutar matematičkog modela sustava jedno se regulacijsko područje sastoji od nekoliko elektrana, manjeg broja koncentriranih trošila i prijenosne mreže. Ostala su regulacijska područja u modelu modelirana pojednostavljeno, nadomjesnim linearnim modelom sustava čiji su parametri dobiveni identifikacijom iz odziva nelinearnog sustava. Taj je linearni model zatim primijenjen u sintezi algoritma sekundarne regulacije koji je zasnovan na kliznom režimu upravljanja. Zbog neminimalno-faznog vladanja hidroelektrana primijenjena je struktura regulatora zasnovana na svim varijablama stanja sustava. Estimacija nemjerljivih stanja i poremećaja zasnovana je na metodi brzog uzorkovanja izlaznih signala sustava. Optimizacija parametara regulatora provedena je korištenjem genetičkog algoritma. Simulacijski rezultati pokazuju kako je predloženi upravljački algoritam, uz predloženu metodu estimacije, moguće koristiti za sekundarnu regulaciju frekvencije i djelatne snage razmjene u nelinearnom elektroenergetskom sustavu

Using a Second Order Sigma-Delta Control to Improve the Performance of Metal-Oxide Gas Sensors

Controls of surface potential have been proposed to accelerate the time response of MOX gas sensors. These controls use temperature modulations and a feedback loop based on first-order sigma-delta modulators to keep constant the surface potential. Changes in the surrounding gases, therefore, must be compensated by average temperature produced by the control loop, which is the new output signal. The purpose of this paper is to present a second order sigma-delta control of the surface potential for gas sensors. With this new control strategy, it is possible to obtain a second order zero of the quantization noise in the output signal. This provides a less noisy control of the surface potential, while at the same time some undesired effects of first order modulators, such as the presence of plateaus, are avoided. Experiments proving these performance improvements are presented using a gas sensor made of tungsten oxide nanowires. Plateau avoidance and second order noise shaping is shown with ethanol measurements.Postprint (author's final draft