These techniques are widely used for maximum power extraction because of their inexpensive and straightforward nature. However, these techniques remain ineffective under faster varying environmental conditions [16; 17; 18]. In the class of bio-inspired techniques, artificial bee colony [19], particle swarm optimization [20], cuckoo search algorithm [21] and genetic algorithms [22] are commonly used techniques for extraction of maximum power from solar system. These algorithms follow a same scheme or procedure to attain optimization. In the initial step, a population of particles or individuals is produced in the area where the solution exists. These individuals are then randomly initialized to interact with other ones to generate their off springs or new states. The useful solution is obtained by comparing the off springs with their parents via a cost function and the one with good performance than other transforms the new generation. The possibility of attaining the MPP is very good using these techniques as the first population is randomly produced. They show low converging time as compared to the conventional techniques. Moreover, the developed controller effectively controls the system's nonlinearities, making it suitable for resolving the MPPT problem, more often in partial shading conditions. Several parameters like crossover rate, mutation rate, size of the population, chromosome selection are needed in these techniques whose estimation is itself a difficult task. Under abruptly changing atmospheric conditions, these parameters are adjusted with time to track the MPP, otherwise tracking becomes difficult. Artificial intelligence (AI) based algorithms for maximum power extraction include Artificial Neural Networks (ANN) [23] and Fuzzy Logic Controllers (FLC) [24; 25]. These algorithms have advantages like self-learning capabilities, operating with variable inputs and self-converging ability. In the start, the ANN-based method requires a training data set to train the output-input relation, but when it develops, it becomes efficient and robust under abruptly changing input parameters. This method of extracting maximum power has fast-tracking speed and low computation requirement, but they require a large memory size and training to track MPP. In contrast, FLCs do not require mathematical modeling of the system and can handle the system's nonlinear behavior [26; 27]. However, its performance and working depends upon the technical information of designer and rule-based tables for tracking and consequently, large memory size in needed. Different non-linear control techniques are proposed in literature for extraction of maximum power from a PV module like nonlinear backstepping [28] and robust integral backstepping [29]. Backstepping controller is very efficient, but its performance depends on the modeling of a nonlinear system. The system is subjected to variation in time, so the equations use to model the system may vary. Hence the performance of the controller can be affected. The integral backstepping controller has been found robust and effective in maximum power extraction under abruptly changing meteorological conditions. However, a significant overshoot and steady-state error have been witnessed during the implementation of this controller. Pros and cons of developed and existing techniques are summarized in Table 1. Main challenge that is faced during the implementation of the controller is that the tracking error is needed to converge to zero under the settling time. Moreover, the nonlinear behavior of the PV system is also the major challenge under changing environmental conditions. To resolve the problems state-above, an adaptive nonlinear Sliding Mode Control (SMC) based method is presented, which is insensitive to parameter uncertainties, and internal/external disturbances [30]. In this study, a nonlinear arbitrary order SMC scheme is proposed to collect maximum power from a PV system using DC-DC buck-boost converter.