ANN‐PID based automatic braking control system for small agricultural tractors

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Abstract

Abstract Braking system is a crucial component of tractors as it ensures safe operation and control of the vehicle. The limited space availability in the workspace of a small tractor exposes the operator to undesirable posture and a maximum level of vibration during operation. The primary cause of road accidents, particularly collisions, is attributed to the tractor operator's insufficient capacity to provide the necessary pedal power for engaging the brake pedal. During the process of engaging the brake pedal, the operator adjusts the backrest support to facilitate access to the brake pedal while operating under stressed conditions. In the present study, a linear actuator‐assisted automatic braking system was developed for the small tractors. An integrated artificial neural network proportional–integral–derivative (ANN‐PID) controller‐based algorithm was developed to control the position of the brake pedal based on the input parameters like terrain condition, obstacle distance, and forward speed of the tractor. The tractor was operated at four different speeds (i.e., 10, 15, 20, and 25 km/h) in different terrain conditions (i.e., dry compacted soil, tilled soil, and asphalt road). The performance parameters like sensor digital output (SDO), force applied on the brake pedal (), and deceleration were considered as dependent parameters. The SDO was found to good approximation for sensing the position of the brake pedal during braking. The optimized network topology of the developed multilayer perceptron neural network (MLPNN) was 3‐6‐2 for predicting SDO and deceleration of the tractor with a coefficient of determination () for the training and testing datasets of the SDO and deceleration were obtained as 0.9953 and 0.9854, and 0.9254 and 0.9096, respectively. The Ziegler–Nichols (Z‐N method) method was adopted to determine the initial optimal gains of the PID controller and later these coefficients were optimized using response surface methodology. The optimized proportional (), integral (), and derivative () coefficient values were 4.8, 6.782, and 3.15, respectively. The developed integrated ANN, that is, MLPNN and PID‐based algorithm could successfully control the position of the brake pedal during braking. The stopping distance and slip of the tractor during automatic braking increased with an increase in the forward speed for the tractor from 10 to 25 km/h in all the selected terrain conditions.

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