APPRAISAL OF TAKAGI–SUGENO TYPE NEURO-FUZZY NETWORK SYSTEM WITH A MODIFIED DIFFERENTIAL EVOLUTION METHOD TO PREDICT NONLINEAR WHEEL DYNAMICS CAUSED BY ROAD IRREGULARITIES

Wheel dynamics play a substantial role in traversing and controlling the vehicle, braking, ride comfort, steering, and maneuvering. The transient wheel dynamics are difficult to be ascertained in tire–obstacle contact condition. To this end, a single-wheel testing rig was utilized in a soil bin facility for provision of a controlled experimental medium. Differently manufactured obstacles (triangular and Gaussian shaped geometries) were employed at different obstacle heights, wheel loads, tire slippages and forward speeds to measure the forces induced at vertical and horizontal directions at tire–obstacle contact interface. A new Takagi–Sugeno type neuro-fuzzy network system with a modified Differential Evolution (DE) method was used to model wheel dynamics caused by road irregularities. DE is a robust optimization technique for complex and stochastic algorithms with ever expanding applications in real-world problems. It was revealed that the new proposed model can be served as a functional alternative to classical modeling tools for the prediction of nonlinear wheel dynamics

Energy loss optimization of run-off-road wheels applying imperialist competitive algorithm

The novel imperialist competitive algorithm (ICA) has presented outstanding fitness on various optimization problems. Application of meta-heuristics has been a dynamic studying interest of the reliability optimization to determine idleness and reliability constituents. The application of a meta-heuristic evolutionary optimization method, imperialist competitive algorithm (ICA), for minimization of energy loss due to wheel rolling resistance in a soil bin facility equipped with single-wheel tester is discussed. The required data were collected thorough various designed experiments in the controlled soil bin environment. Local and global searching of the search space proposed that the energy loss could be reduced to the minimum amount of 15.46 J at the optimized input variable configuration of wheel load at 1.2 kN, tire inflation pressure of 296 kPa and velocity of 2 m/s. Meanwhile, genetic algorithm (GA), particle swarm optimization (PSO) and hybridized GA–PSO approaches were benchmarked among the broad spectrum of meta-heuristics to find the outperforming approach. It was deduced that, on account of the obtained results, ICA can achieve optimum configuration with superior accuracy in less required computational time

Off-road vehicle dynamics: analysis, modelling and optimization

This book deals with the analysis of off-road vehicle dynamics from kinetics and kinematics perspectives and the performance of vehicle traversing over rough and irregular terrain. The authors consider the wheel performance, soil-tire interactions and their interface, tractive performance of the vehicle, ride comfort, stability over maneuvering, transient and steady state conditions of the vehicle traversing, modeling the aforementioned aspects and optimization from energetic and vehicle mobility perspectives. This book brings novel figures for the transient dynamics and original wheel terrain dynamics at on-the-go condition

Wavelet neural network applied for prognostication of contact pressure between soil and driving wheel

This paper describes the measurement of contact pressure in the context of wheel–terrain interaction as affected by wheel load and tire inflation pressure when fusion of the wavelet transform with the back-propagation (BP) neural network is applied to construct the wavelet neural network contact pressure prediction model. To this aim, a controlled soil bin testing facility equipped with single-wheel tester was utilized while three levels of velocity, three levels of slippage and three levels of wheel load were applied. Using image processing technique, contact area values were determined which were subsequently used for quantification of contact pressure. Performances of the different predictor models incorporated of various mother wavelets were evaluated using standard statistical evaluation criteria. Root mean square error and coefficient of determination values of 0.1382 and 0.9864 achieved by the optimal wavelet neural network are better than that of BP neural network. The proposed tool typifies a high learning speed, enhanced predicting accuracy, and strong robustness

Net traction of a driven wheel as affected by slippage, velocity and wheel load

The objective was to assess the effect of velocity at three levels (i.e. 0.8, 1 and 1.2 m/s), slippage at three levels (i.e. 10, 12 and 15%) and three levels of wheel load (i.e. 2, 3 and 4 kN) on net traction utilizing a single-wheel tester in the soil bin facility of the Department of Agricultural Machinery of Urmia University. Analysis of variance (ANOVA) was developed to verify the effectiveness of the aforementioned parameters on the objective of the study at 1% significance level. It was found that the increment of wheel load and slippage results in the increment of net traction. However, it was deduced that velocity has no significant effect on net traction

Effect of velocity, wheel load and multipass on soil compaction

Vehicle imposed soil compaction is one of the serious concerns in agriculture and environmental problems that requires accurate studies. We were inspired to launch an investigation for soil compaction determination at three levels of wheel load (1, 2 and 3 kN), three levels of velocity (0.5, 0.75 and 1 m/s) and at 1, 2 and 3 passages of wheel with three replications on clay-loam soil. Experiments were conducted utilizing a single wheel-tester inside a soil bin. Penetration resistance and soil sinkage were determined as soil compaction indices. Data were examined by analysis of variance (ANOVA) at.%1 significance level. Results indicated that the highest penetration resistance of 260 kPa occurred at a depth of 210 mm, third pass, wheel load of 3 kN and velocity of 0.5 m/s. The lowest penetration resistance of 121 kPa was at 1 kN wheel load, first pass and at a velocity of 1 m/s. The greatest soil sinkage obtained was 62.91 mm for wheel load of 3 kN, at 0.5 m/s and at the third passage of wheel while the lowest soil sinkage was 18.04 mm for wheel load of 1 kN, at a velocity of 1 m/s and at first pass. Findings disclosed that augmentation of wheel load and multiple pass increased soil compaction while the increase of velocity had a reverse effect. Two models were proposed for penetration resistance and soil sinkage with coefficient of determination of 0.9375 and 0.9731, respectively

Application of artificial neural networks for the prediction of traction performance parameters

This study handles artificial neural networks (ANN) modeling to predict tire contact area and rolling resistance due to the complex and nonlinear interactions between soil and wheel that mathematical, numerical and conventional models fail to investigate multivariate input and output relationships with nonlinear and complex characteristics. Experimental data acquisitioning was carried out using a soil bin facility with single-wheel tester at seven inflation pressures of tire (i.e. 100–700 kPa) and seven different wheel loads (1–7 KN) with two soil textures and two tire types. The experimental datasets were used to develop a feed-forward with back propagation ANN model. Four criteria (i.e. R-value, T value, mean squared error, and model simplicity) were used to evaluate model’s performance. A well-trained optimum 4-6-2 ANN provided the best accuracy in modeling contact area and rolling resistance with regression coefficients of 0.998 and 0.999 and T value and MSE of 0.996 and 2.55 × 10−12, respectively. It was found that ANNs due to faster, more precise, and considerably reliable computation of multivariable, nonlinear, and complex computations are highly appropriate for soil–wheel interaction modeling