Nedjeljko Perić

Hybrid Theory-Based Time-Optimal Control of an Electronic Throttle

An electronic throttle is a dc-motor-driven valve that regulates air inflow into the combustion system of the engine. The throttle control system should ensure fast and accurate reference tracking of the valve plate angle while preventing excessive wear of the throttle components by constraining physical variables to their normal-operation domains. These high-quality control demands are hard to accomplish since the plant is burdened with strong nonlinear effects of friction and limp-home nonlinearity. In this paper, the controller synthesis is performed in discrete time by solving a constrained time-optimal control problem for the piecewise affine (PWA) model of the throttle. To that end, a procedure is proposed to model friction in a discrete-time PWA form that is suitable both for simulation and controller design purposes. The control action computation can, in general, be restated as a mixed-integer program. However, due to the small sampling time, solving such a program online (in a receding horizon fashion) would be very prohibitive. This issue is resolved by applying recent theoretical results that enable offline precomputation of the state-feedback optimal control law in the form of a lookup table. The technique employs invariant set computation and reachability analysis. The experimental results on a real electronic throttle are reported and compared with a tuned PID controller that comprises a feedforward compensation of the process nonlinearities. The designed time-optimal controller achieves considerably faster transient, while preserving other important performance measures, like the absence of overshoot and static accuracy within the measurement resolution

An electronic throttle control strategy including compensation of friction and limp-home effects

An electronic throttle is a low-power dc servo drive which positions the throttle plate. Its application in modern automotive engines leads to improvements in vehicle drivability, fuel economy, and emissions. Transmission friction and the return spring limp-home nonlinearity significantly affect the electronic throttle performance. The influence of these effects is analyzed by means of computer simulations, experiments, and analytical calculations. A dynamic friction model is developed in order to adequately capture the experimentally observed characteristics of the presliding-displacement and breakaway effects. The linear part of electronic throttle process model is also analyzed and experimentally identified. A nonlinear control strategy is proposed, consisting of a proportional-integral-derivative (PID) controller and a feedback compensator for friction and limp-home effects. The PID controller parameters are analytically optimized according to the damping optimum criterion. The proposed control strategy is verified by computer simulations and experiments.

Stator-Current Spectrum Signature of Healthy Cage Rotor Induction Machines

Before applying current-signature-analysis-based monitoring methods, it is necessary to thoroughly analyze the existence of the various harmonics on healthy machines. As such an analysis is only done in very few papers, the objective of this paper is to make a clear and rigorous characterization and classification of the harmonics present in a healthy cage rotor induction motor spectrum as a starting point for diagnosis. Magnetomotive force space harmonics, slot permeance harmonics, and saturation of main magnetic flux path through the virtual air-gap permeance variation are taken into analytical consideration. General rules are introduced giving a connection between the number of stator slots, rotor bars, and pole pairs and the existence of rotor slot harmonics as well as saturation-related harmonics in the current spectrum. For certain combinations of stator and rotor slots, saturation-related harmonics are shown to be most prominent in motors with a pole pair number of two or more. A comparison of predicted and measured current harmonics is given for several motors with different numbers of pole pairs, stator slots, and rotor bars.

Constrained Optimal Control of an Electronic Throttle

The overall vehicle performance is strongly influenced by the quality of the control of the electronic throttle – a DC motor driven valve that regulates the inflow of air to the vehicles engine. Designing a controller for the throttle system is a challenging task since one has to cope with two strong non-linearities: the gearbox friction and the so-called “limp-home” non-linearity. In this paper we address these issues by solving a constrained optimal control problem formulated for the discrete-time piecewise affine (PWA) model of the throttle. In an off-line, dynamic programming procedure we obtain the look-up table like solution to the optimal control problem. Such a solution allows the real-time controller implementation that would otherwise be impossible to achieve due to the small sampling time needed for the application at hand. We address the issue of the PWA friction modelling in more detail by considering both static and dynamic friction models. Two different control strategies are studied: constrained finite time optimal control (CFTOC), used in the regulator case, and constrained time-optimal control (CTOC), used in the reference tracking case. We report experimental results with both control strategies. The reference tracking controller significantly outperformed a tuned PID controller with a feedforward compensation of non-linearities in terms of the response speed while preserving the response quality regarding the absence of an overshoot and the static accuracy within the measurement resolution.

Neural network based tire/road friction force estimation

This paper deals with the problem of robust tire/road friction force estimation. Availability of actual value of the friction force generated in contact between the tire and the road has significant importance for active safety systems in modern cars, e.g. anti-lock brake systems, traction control systems, vehicle dynamic systems, etc. Since state estimators are usually based on the process model, they are sensitive to model inaccuracy. In this paper we propose a new neural network based estimation scheme, which makes friction force estimation insensitive to modelling inaccuracies. The neural network is added to the estimator in order to compensate effects of the friction model uncertainties to the estimation quality. An adaptation law for the neural network parameters is derived using Lyapunov stability analysis. The proposed state estimator provides accurate estimation of the tire/road friction force when friction characteristic is only approximately known or even completely unknown. Quality of the estimation is examined through simulation using one wheel friction model. Simulation results suggest very fast friction force estimation and compensation of the changes of the model parameters even when they vary in wide range.

Individual pitch control of wind turbine based on loads estimation

The use of wind energy for generating electricity has been constantly and rapidly increasing over last few decades and this growth is expected to continue. In order to enable even greater role of wind energy in power production wind turbinespsila sizes and rated powers must increase. As wind turbines grow in size they are subjected to extreme loads and fatigue caused by uniform turbulent winds. This prevents further growth of wind turbines. Therefore control methods that assure loads reduction become a necessity. In this paper methods for wind turbine loads reduction based on individual pitch control are explored. To overcome problems related to needed measurements of blade loads a method for their estimation based on loads measured on the fixed parts of the structure is proposed.

Neural network-based sliding mode control of electronic throttle

A neural network-based sliding mode controller for an electronic throttle of an internal combustion engine is proposed. Electronic throttle is modeled as a linear system with uncertainties and affected by disturbances depending on the states of the system. The disturbances, consisting of an unknown friction and a torque caused by the dual spring mechanism inside the mechanical part of the throttle, are estimated by a neural network whose parameters are adapted on-line. The sliding mode controller and the parameters adaptation scheme are derived in order to achieve a tracking of a smooth reference signal, while preserving boundedness of all signals in the closed-loop system. Experimental results are presented which demonstrate the efficiency and robustness of the proposed control scheme.

A reinforcement learning approach to obstacle avoidance of mobile robots

One of the basic issues in the navigation of autonomous mobile robots is the obstacle avoidance task that is commonly achieved using a reactive control paradigm where a local mapping from perceived states to actions is acquired. A control strategy with learning capabilities in an unknown environment can be obtained using reinforcement learning where the learning agent is given only sparse reward information. This credit assignment problem includes both temporal and structural aspects. While the temporal credit assignment problem is solved using core elements of the reinforcement learning agent, solution of the structural credit assignment problem requires an appropriate internal state space representation of the environment. In this paper, a discrete coding of the input space using a neural network structure is presented as opposed to the commonly used continuous internal representation. This enables a faster and more efficient convergence of the reinforcement learning process.

State estimation of an electronic throttle body

Electronic throttle body (ETB) is a device used in cars to regulate air inflow into the motors combustion system. Its good behavior is crucial for the superimposed engine speed control system. However, electronic throttle body is a highly nonlinear process, and its only measurable state is the throttle valve position measured by a cheap potentiometer of low resolution, resulting in significant quantization noise. In order to apply an advanced control strategy, all states should be usually available and the measurement noise should be reduced. With these two goals in mind we have implemented an extended Kalman filter (EKF), as a common solution for state estimation of nonlinear systems, and an unscented Kalman filter (UKF), which is a preferable solution when the process nonlinearities are very strong. Both filters are based on discrete time piece-wise affine process model which uses new friction model. By experimental tests on a real ETB it is shown that UKF gives better estimates of its state variables.

Estimation based Individual Pitch Control of Wind Turbine

The use of wind power for generating electricity has experienced an uninterrupted and accelerating growth over last few decades and this growth is likely to continue. In order to enable even greater role of wind energy in power production it is necessary to increase the size and unit power of wind turbines. As wind turbines grow in size they are subjected to extreme loads and fatigue caused by nonuniform turbulent winds. Therefore, control algorithms that can assure load and fatigue reduction become a necessity. In this paper the individual pitch control for reduction of the periodic blade and hub loading is explored. To avoid problems related to the required blade loads measurements a method for their estimation based on other process variables is proposed. The performance of the individual pitch controller that uses such load estimations instead of measurements is tested and compared to the collective pitch control and the individual pitch control based on measured loads.