Appraisal against Other Controllers

Comparing the DANNSCO controller to other controller performances has been argued to provide the best evidence of the application of ANN for stability control. Figure 8.72 and Figure 8.73 provide information for the wet conditions, with each of the runs ranked in order of ultimate speed. Furthermore, because some types of control are represented in greater numbers than others, the process of ranking runs 1 to 10 (for ten runs) was replaced with a normalisation process. Here, all of the runs for each controller are placed into the order of highest to lowest ultimate speed and (1=fastest, 10=slowest) and the runs between these extremes given a corresponding fraction in this range. This allows each type of control to be directly compared for performance, and performance repeatability.

Clearly, the METC (“MoTeC ECU Control”) has better performance than each of the other controllers. This is expected, as discussed above, because of the faster control the ECU is capable of and the tuning method employed. As such, this does not provide a good comparison for the DANNSCO and DEMTC control. Another feature is that the “No Control” option, while capable of high accelerations, does not provide repeatable

performance. This is also expected, and shows the clear benefits of any type of traction control.

Figure 8.72: Straight-line acceleration performance comparison of different

controllers by normalised 1-10 rank

Figure 8.73: Circle run performance comparison of different controllers by

normalised 1-10 rank

As such, the most important comparison made on these two graphs is between the DEMTC and the DANNSCO controllers. The slip control methods between these two techniques are identical and the control rates are very similar, which means they provide a very good comparison basis. In fact, the only significant difference between the two methods is that the DEMTC has a pre-tuned static aim slip, while the DANNNSCO model varies aim slip based on ANN predictions of optimum slip.

To this end, that the DANNSCO and DEMTC models generally produce similar performance, which alone prove the potential of ANN control. However, the DANNSCO model has constantly higher performance than the DEMTC model at the faster end of the rank spectrum. This shows that the ANN prediction of optimum aim slips actually produces better performance than the traditional method, despite the slightly slower control rate of the model. In addition, the DEMTC aim slips were tuned separately for the two cases, while the DANNSCO control did not have to be, which further highlights the capability of the ANN control. Therefore, the performance of ANN control on the wet and slippery surface of the police academy test track is proven to be at least as good as the traditional control method, if not better.

8.9 Remarks

The development of the “Intelligent Traction Controller” involved a number of significant steps. This included determining the philosophy of control, finding appropriate test tracks, training a number of ANN model for improved accuracy, evaluating a range of ANN controllers to find the most promising and conducting off-line and on-line performance tests of the chosen controller. This process lead to the development, appraisal and performance verification of the Direct ANN Slip Curve Optimisation (DANNSCO) controller, which provided a number of very positive results.

In particular, the DANNSCO controller was appraised as being highly adept at determining optimum slips for maximum driven wheel longitudinal acceleration in all tested conditions, and in a generic manner. In practice, it performed as well, if not better, than comparable traditional controllers due to its ability to modify aim slip for different manoeuvres. This provides very good verification that intelligent traction control exhibits benefits above those provided by traditional controllers. It is noted, however, that these results are not conclusive due to the limitations in testing equipment, infrastructure, tyre testing data, and test-driving ability. There are many aspects of the ANN modelling accuracy that could not be explored in this work, although general performance results could be obtained.

These positive results for traction control are considered generic for all stability controllers, and were highlighted by its simple transportation to unsealed road conditions. The DANNSCO model is a generic tool used within a specific application here. The model is capable of optimising many parameters to achieve maximum acceleration in the longitudinal direction, and should be easily modified to provide other forms of wheel control. Furthermore, the goal of optimising longitudinal acceleration could be replaced with optimising acceleration in the driver desired direction, which would enable many new forms of stability control with additional actuation devices. If this can be done, which this research suggests it can, then very significant inroads will made into vehicle safety systems in regards to performance and cost.

The potential of using ANN models to predict the driver’s desired acceleration direction and an acceptable yaw rate was also briefly discussed in this chapter. As it stands, these crucial parameters are determined using traditional methods. ANN models could be used

to replace this process, in a fashion well suited to ANN modelling but seemingly unexplored. By modelling the driver’s behaviour during stabile conditions using ANN, their control actions in different situations can be linked to the vehicle acceleration direction and yaw rate. In this manner, ANN models would be able to observe the driver responses when the vehicle is unstable to determine the vehicle dynamics the driver is expecting. This would have the clear benefit over traditional algorithms because the model would be able to adaptively take driving style into account, as well as providing capacity to finely determine the driver’s desired acceleration direction and yaw rate.

CHAPTER

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CONCLUDING REMARKS AND