Agrivoltaic systems (AVS) offer a symbiotic strategy for co-location sustainable renewable energy and agricultural production. This is particularly important in densely populated developing and developed countries, where renewable energy development is becoming more important; however, profitable farmland must be preserved. As emphasized in the Food-Energy-Water (FEW) nexus, AVS advancements should not only focus on energy management, but also agronomic management (crop and water management). Thus, we critically review the important factors that influence the decision of energy management (solar PV architecture) and agronomic management in AV systems. The outcomes show that solar PV architecture and agronomic management advancements are reliant on (1) solar radiation qualities in term of light intensity and photosynthetically activate radiation (PAR), (2) AVS categories such as energy-centric, agricultural-centric, and agricultural-energy-centric, and (3) shareholder perspective (especially farmers). Next, several adjustments for crop selection and management are needed due to light limitation, microclimate condition beneath the solar structure, and solar structure constraints. More importantly, a systematic irrigation system is required to prevent damage to the solar panel structure. To summarize, AVS advancements should be carefully planned to ensure the goals of reducing reliance on non-renewable sources, mitigating global warming effects, and meeting the FEW initiatives.

A novel observer-based parameter estimation scheme with sliding mode term has been developed to estimate the road gradient and the vehicle weight using only the vehicle's velocity and the driving torque. The estimation algorithm exploits all known terms in the system dynamics and a low-pass filtered representation of the dynamics to derive an explicit expression of the parameter estimation error without measuring the acceleration. The proposed parameter estimation scheme which features a sliding-mode term to ensure the fast and robust convergence of the estimation in the presence of persistent excitation is augmented to an adaptive observer and analyzed using Lyapunov Theory. The analytical results show that the algorithm is stable and ensures finite-time error convergence to a bounded error even in the presence of disturbances. In the absence of disturbances, convergence to the true values in finite time is guaranteed. A simple practical method for validating persistent excitation is provided using the new theoretical approach to estimation. This is validated by the practical implementation of the algorithm on a small-scaled vehicle, emulating a car system. The slope gradient as well as the vehicle's mass/weight are estimated online. The algorithm shows a significant improvement over previous results.

This paper presents a new fuzzy switching median (FSM) filter employing fuzzy techniques in image processing. The proposed filter is able to remove salt-and-pepper noise in digital images while preserving image details and textures very well. By incorporating fuzzy reasoning in correcting the detected noisy pixel, the low complexity FSM filter is able to outperform some well known existing salt-and-pepper noise fuzzy and classical filters.

In this paper, a new structure to design model-free control (MFC) based on the ultralocal model is presented for an unknown nonlinear single-input single-output (SISO) dynamic system. The proposed structure includes two adaptive laws corresponding to the unknown linear and nonlinear terms. Utilizing the adaptive law for linear term, the controller gain is going to be updated online using a differential Riccati equation (DRE). Subsequently, the control policy which includes an optimal term as well as a term for compensating the system unknown dynamics is generated. Here, the two proposed adaptive laws are model-free estimation algorithms, in which the need for any regressor parameter and also the persistent excitation (PE) condition is eliminated. Finally, two simulation studies are presented to show that the proposed adaptive model-free control (AMFC) policy outperforms the two well-known controllers. Moreover, the AMFC is applied on a Duffing-Holmes chaotic oscillator plant and the convincing performance of the algorithm is observed through the simulation results.