This thesis develops a framework for real-time estimation of the forces acting at the interface between tyre and road on large off-highway mining trucks. These forces, together with tyre-slip, characterize the interaction between truck and road and strongly influence tyre life and fuel consumption.
Solving the problem of tyre-force estimation involves the deconvolution of the tyre-road interaction forces from an inertial measurement set. An extended Kalman filter augmented by a shaping-filter, whose states are the tyre-forces (and their derivatives), is used to perform this deconvolution. This methodology uses the feedback structure of the Kalman filter to perform an implicit, stable inversion of the system dynamics.
The thesis begins by exploring the expected patterns in tyre-forces by simulating a mining truck performing various steering manoeuvres. These simulations use a virtual mining truck model developed using the ADAMS modelling software. The observed tyre-force patterns help to identify a feasible structure for the system model used in the prediction step of the Kalman filter. The system model developed captures the truck's dynamics relevant to tyre-road interaction and gives a filter that is (locally) stochastically observable using measurements that are achievable in practice. Stochastic observability is a requirement for convergence of the force estimates.
Tyre-forces predicted by the Kalman filter based force estimator are compared with those simulated by the ADAMS virtual truck model. A sensitivity analysis is conducted to determine the susceptibility of the errors in the tyre-force estimates to measurement noise (i.e. to determine the required accuracy of the measurement data) and to truck model parameters (i.e. to determine how much system uncertainty can be tolerated).
Force estimates are found to be sensitive to a number of parameters that vary during normal truck operation including, tray mass and centre-of-mass, wheel rolling radius, and rolling resistance. To reduce these sensitivities, a combined state and parameter estimation scheme based on the method of maximum likelihood is developed. This method employs an estimate of the Fisher information matrix to determine when there is sufficient information in the measurement data to estimate the unknown parameters simultaneously with the tyre-forces. Among other things, this methodology provides a basis for the real-time monitoring of rolling resistance, which could be used to identify regions of the haul road where fuel consumption rates are high.
The thesis also develops a methodology based on a two-stage filtering process which adaptively determines the driving noise applied to the shaping-filter for minimum variance of the force estimate error. In addition to improving the quality of tyre-force estimates, it is argued that the approach provides a method for monitoring road unevenness, which could be used to identify regions on a haul route requiring grading or other maintenance.
Several recommendations are made for future directions of research.