Pavel Osinenko

Experimental results of slip control with a fuzzy-logic-assisted unscented Kalman filter for state estimation

Slip control is a common and crucial functionality for a number of vehicles ranging from cars to tractors and trucks. The purpose of slip control is to improve vehicles traction and motion stability, prevent excessive wheel slippage and provide stable braking. Slip is a non-linear function of the vehicles ground speed and wheel rotation frequency and as such depends on the internal state variables, such as wheel load torque, which are in turn unknown. A number of approaches exist to estimate the unknown state, one of the most used ones being based on Kalman filter. In the current study, we present experimental results of slip control for an electrical single wheel-drive tractor using an unscented Kalman filter, which is a variant of Kalman filter suitable for non-linear systems. To cope with the problems of state estimation for heavy-duty vehicles, the Kalman filter was augmented with a fuzzy-logic supervisor aimed at assessment of vehicle dynamics. The goal of the supervisor was to adapt the state noise covariance with the goal of improving tracking accuracy. Wheel slip reduction was observed and its mean stayed within the desired limit. Experiments were carried out under two different road conditions and the condition change was identified by the Kalman filter.

Fuzzy-logic assisted power management for electrified mobile machinery

The major elements of a mobile machine - engine and drives - are a crucial subject of control. Implementation of electric drives in mobile machinery is an emerging field of research and industry. Apart from conventional mechanical drives, they allow for wide control and power management possibilities. This paper considers a tractor-implement as a mobile power grid and addresses the problem of smart engine power management for sustainable operation by using power feedback of electric drives. The main focus lies in providing stable operation of the electrically driven implement. This is achieved by an engine power reserve dynamically adjusted according to power demands and their rate of change. To quantitatively evaluate the power demands, a supervisor approach based on the fuzzy logic theory is suggested. The supervisor has the structure of the Mamdani fuzzy system and computes the amount of the diesel engine power reserve as output. An adaptively changing power reserve providing sustainable operation is demonstrated in a partially stochastic simulation model of an electrified tractor with a rotary swather.

Adaptive unscented Kaiman filter with a fuzzy supervisor for electrified drive train tractors

Electrified drive trains for tractors are supposed to realize great potential of raising performance in heavy operations via optimal traction control. The paper proposes to apply an adaptive unscented Kaiman filter (UKF) with a fuzzy supervisor for identification of electrical drive train tractor dynamics. The key advantage of electrical drive trains lies in feedback of drive torque which plays crucial role in traction parameter estimation. It is known that without using special adaptation techniques, an UKF may cause some divergence problems and lowered precision of estimation as well as its predecessor, an extended Kaiman filter (EKF). A method based on a fuzzy logic supervisor in addition to adaptation of an UKF is proposed to maintain trade-off between tracking strength and estimation accuracy. Simulation results with a comprehensive tractor dynamics model showed increase in estimation precision of traction parameters. Laboratory experiments using a test stand with an electrical load machine showed appropriate estimation of the load torque.

Analysis of extremum value theorems for function spaces in optimal control under numerical uncertainty

The extremum value theorem for function spaces plays the central role in optimal control. It is known that computation of optimal control actions and policies is often prone to numerical errors which may be related to computability issues. The current work addresses a version of the extremum value theorem for function spaces under explicit consideration of numerical uncertainties. It is shown that certain function spaces are bounded in a suitable sense i.e. they admit finite approximations up to an arbitrary precision. The proof of this fact is constructive in the sense that it explicitly builds the approximating functions. Consequently, existence of approximate extremal functions is shown. Applicability of the theorem is investigated for finite--horizon optimal control, dynamic programming and adaptive dynamic programming. Some possible computability issues of the extremum value theorem in optimal control are shown on counterexamples

Stacked adaptive dynamic programming with unknown system model

Adaptive dynamic programming is a collective term for a variety of approaches to infinite-horizon optimal control. Common to all approaches is approximation of the infinite-horizon cost function based on dynamic programming philosophy. Typically, they also require knowledge of a dynamical model of the system. In the current work, application of adaptive dynamic programming to a system whose dynamical model is unknown to the controller is addressed. In order to realize the control algorithm, a model of the system dynamics is estimated with a Kalman filter. A stacked control scheme to boost the controller performance is suggested. The functioning of the new approach was verified in simulation and compared to the baseline represented by gradient descent on the running cost.

Winding switching strategy for electric wheel drives in agricultural machinery

Electrified drive trains for tractors open great possibilities of raising performance in heavy agricultural operations due to high controllability and drive torque feedback. However, electric motor characteristics do not completely satisfy traction requirements in agricultural operations which imply high torques at low speeds as well as high transport speeds. The paper proposes to apply a winding switching method for electric wheel motors. The key advantage is the adjustment of resistance and flux to the optimal operation point which influences the motor characteristics and efficiency. Experiments show the transition of three winding configurations. The electric power for the traction drive is provided by a diesel-generator. Interaction of power flow and winding change is investigated.