Mirko Čorić

Bond Graph Analysis of Automatic Transmission Shifts including Potential of Extra Clutch Control

New generation of torque converter automatic transmissions (AT) include a large number of gears for improved fuel economy. Control requirements for such transmissions become more demanding, which calls for the development of new shift optimization and analysis tools. This paper presents two contributions to the field of transmission dynamics analysis: (i) bond graph method-based shift transient analysis, and (ii) deriving a unique set of conditions for beneficial use of a third (normally-open) clutch for any upshift or downshift, with emphasis on inertia phase. The derived conditions are examined on an example of 10- speed AT based on the clutch torque input trajectory optimization results. The examination results point out that the extra clutch has a potential of significant performance improvement for any single- transition upshift in the inertia phase, in terms of reduced vehicle jerk RMS value due to the suppressed inertia bump effect. The shift quality improvement is possible in the torque phase, as well, but it is of much smaller extent when compared to that related to the inertia phase.

Optimisation of active suspension control inputs for improved vehicle handling performance

ABSTRACT Active suspension is commonly considered under the framework of vertical vehicle dynamics control aimed at improvements in ride comfort. This paper uses a collocation-type control variable optimisation tool to investigate to which extent the fully active suspension (FAS) application can be broaden to the task of vehicle handling/cornering control. The optimisation approach is firstly applied to solely FAS actuator configurations and three types of double lane-change manoeuvres. The obtained optimisation results are used to gain insights into different control mechanisms that are used by FAS to improve the handling performance in terms of path following error reduction. For the same manoeuvres the FAS performance is compared with the performance of different active steering and active differential actuators. The optimisation study is finally extended to combined FAS and active front- and/or rear-steering configurations to investigate if they can use their complementary control authorities (over the vertical and lateral vehicle dynamics, respectively) to further improve the handling performance.

Applications of Computational Optimal Control to Vehicle Dynamics

Modern vehicle dynamic control systems are based on new types of actuators, such as active steering and active differentials, in order to improve the overall handling performance including stability, responsiveness, and agility. Numerical techniques of off-line optimization of vehicle dynamics control variables can conveniently be used to facilitate decisions on optimal actuator configurations and provide guidance for design of realistic, on-line controllers. This chapter overviews the previous authors’ results of assessment of various vehicle dynamics actuator configurations based on application of a back propagation through time (BPTT) conjugate gradient optimization algorithm. It is then focused on detailed optimization of active front and rear steering control variables for various maneuvers and design specifications, where a nonlinear programming-based optimization tool is used.

Optimisation of active suspension control inputs for improved performance of active safety systems

ABSTRACT A collocation-type control variable optimisation method is used to investigate the extent to which the fully active suspension (FAS) can be applied to improve the vehicle electronic stability control (ESC) performance and reduce the braking distance. First, the optimisation approach is applied to the scenario of vehicle stabilisation during the sine-with-dwell manoeuvre. The results are used to provide insights into different FAS control mechanisms for vehicle performance improvements related to responsiveness and yaw rate error reduction indices. The FAS control performance is compared to performances of the standard ESC system, optimal active brake system and combined FAS and ESC configuration. Second, the optimisation approach is employed to the task of FAS-based braking distance reduction for straight-line vehicle motion. Here, the scenarios of uniform and longitudinally or laterally non-uniform tyre–road friction coefficient are considered. The influences of limited anti-lock braking system (ABS) actuator bandwidth and limit-cycle ABS behaviour are also analysed. The optimisation results indicate that the FAS can provide competitive stabilisation performance and improved agility when compared to the ESC system, and that it can reduce the braking distance by up to 5% for distinctively non-uniform friction conditions.

Control variable optimisation for an extended range electric vehicle

A dynamic programming-based algorithm is used for off-line optimisation of control variables of a series-parallel extended range electric vehicle powertrain. The aim is to minimise the fuel and/or electricity consumption, subject to battery state-of-charge constraints and physical limits of different powertrain variables. The optimised control variables include engine torque and electric machine speed, as well as a variable that selects the powertrain operating mode defining the state of powertrain clutches. The optimisation results are presented for four characteristic certification driving cycles and different vehicle operating regimes including electric driving during charge depleting (CD) regime, hybrid driving during charge sustaining (CS) regime, and combined/blended regime. The emphasis is on the blended regime and a related analysis of fuel saving potential when compared to the CD-CS regime. The optimisation results are used to provide recommendations for design of a realistic (online) control strategy that is a subject of another publication.

A modular control system for warehouse automation - algorithms and simulations in USARSim

In this paper, we present a control system for a fully autonomous material handling facility. The scenario we are considering is motivated by the 2011 IEEE Virtual Manufacturing Automation Challenge (VMAC). It consists of multiple autonomously guided vehicles (AGVs), transporting pallets of various goods between several input and output locations, through an unstructured warehouse environment. Only a map of the warehouse and a pallet delivery list are provided a priori. Pallets must be delivered to the output locations in the shortest time possible, while respecting the ordering of different pallet types specified by the delivery list. The presented control system handles all aspects of warehouse operation, from individual vehicle control to high-level mission planning and coordination. Delivery mission assignments are optimized using dynamic programming and simulated annealing techniques. Mission executions are coordinated using graph search methods and a modified version of the Bankers algorithm, to ensure safe, collision and deadlock-free system operation. System performance is evaluated on a virtual warehouse model, using the high fidelity USARSim simulator.

Optimization-Inspired Control Strategy for a Magnus Effect-Based Airborne Wind Energy System

An optimization study has been conducted and the corresponding control strategy developed for the lighter-than-air airborne wind energy system. The linchpin of the system is an airborne module in the form of a buoyant, rotating cylinder, whose rotation in a wind stream induces the Magnus effect-based aerodynamic lift, thereby facilitating traction power generation. The optimization is aimed at maximizing the average power produced at the ground-based generator during a continuously repeatable operating cycle. This chapter provides a recap of the optimization methodology, results, and their physical interpretation, and builds on this foundation to develop control strategies aimed at approaching the optimization results. Comparative analysis of the two proposed control strategies and the optimization results shows that the simpler and more robust strategy can approach the performance of the more sensitive strategy that closely matches the optimization results.