Effective control of ride quality and handling performance are challenges for active vehicle suspension systems, particularly for off-road applications. The nonlinearities tend to degrade the performance of active suspension systems; these introduce harshness to the ride quality and reduce off-road mobility. Typical control strategies rely on linear models of the suspension dynamics. While these models are convenient, nominally accurate, and controllable due to the abundance of linear control techniques, they neglect the nonlinearities present in real suspension systems. The techniques already implemented and methods used to cope with problem of Half-Car model were studied. Every method and technique had some drawbacks in terms of complexity, cost-effectiveness, and ease of real time implementation. In this paper, an improved control method for Half-Car model was proposed. First, input/output feedback linearization was performed to convert the nonlinear system of Half-Car model into an equivalent linear system. This was followed by a Linear Quadratic Regulator (LQR) controller. This controller had minimized the effects of road disturbances by designing a gain matrix with optimal robustness properties. The proposed control technique was applied in the presence of the deterministic road disturbance. The results were verified using the Matlab/Simulink toolbox.

A system identification of a two-wheeled robot (TWR) using a data-driven approach from its fundamental nonlinear kinematics is investigated. The fundamental model of the TWR is implemented in a Simulink environment and tested at various input/output operating conditions. The testing outcome of TWR’s fundamental dynamics generated 12 datasets. These datasets are used for system identification using simple autoregressive exogenous (ARX) and non-linear auto-regressive exogenous (NLARX) models. Initially the ARX structure is heuristically selected and estimated through a single operating condition. We conclude that the single ARX model does not satisfy TWR dynamics for all datasets in term of fitness. However, NLARX fitted the 12 estimated datasets and 2 validation datasets using sigmoid nonlinearity. The obtained results are compared with TWR’s fundamental dynamics and predicted outputs of the NLARX showed more than 98% accuracy at various operating conditions.

With the increase in energy demand, renewable energy has become a need of almost every country. Solar Energy is an important constituent of it and contributes a large portion in it. Forecasting the output power of a Photovoltaic (PV) system has always been a challenging problem in the power sector from the last few decades. The output power of a PV system depends upon several environmental factors such as irradiance (G), temperature (T), humidity (H), wind speed (W), provided the tilt angle is kept constant, among which the vital role is played by irradiance. Researchers have utilized several techniques to accurately predict the output power of PV module but every method has various pros and cons. In this paper, an experimental measurement dataset of 28296 samples with all the environmental parameters mentioned above are taken as the inputs and power as its output, of a Poly-Silicon (Poly-Si) PV module, is trained through Artificial Neural Network (ANN), to predict the output power accurately. The proposed ANN contains a layer size of 15 and training algorithm used is Levenberg-Marquardt. A detailed analysis and preprocessing of the data is carried out through Pearson's correlation method prior to training. The hyperparameters of Neural Network tuning are selected through heuristic method. The data division is done randomly with 70 % dataset used for training, 15 % dataset used for each validation and testing. The statistical results show that ANN accurately predicted the power output of PV module. The regression analysis values acquired are 98 % and the MSE of all the three phases is 0.0604.INDEX TERMS Artificial Neural Network (ANN), Environmental, Photovoltaic (PV) system, Renewable Energy (RE)

System identification of a Two-Wheeled Robot (TWR) through nonlinear dynamics is carried out in this paper using a data-driven approach. An Artificial Neural Network (ANN) is used as a kinematic estimator for predicting the TWR’s degree of movement in the directions of x and y and the angle of rotation Ψ along the z-axis by giving a set of input vectors in terms of linear velocity ‘V’ (i.e., generated through the angular velocity ‘ω’ of a DC motor). The DC motor rotates the TWR’s wheels that have a wheel radius of ‘r’. Training datasets are achieved via simulating nonlinear kinematics of the TWR in a MATLAB Simulink environment by varying the linear scale sets of ‘V’ and ‘(r ± ∆r)’. Perturbation of the TWR’s wheel radius at ∆r = 10% is introduced to cater to the robustness of the TWR wheel kinematics. A trained ANN accurately modeled the kinematics of the TWR. The performance indicators are regression analysis and mean square value, whose achieved values met the targeted values of 1 and 0.01, respectively.

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