The paper investigates the construction of fuzzy controllers for a class of nonlinear systems. Our approach implements fuzzy partition to the state variables based on Lyapunov synthesis. The resulting control law is stable and able to exploit the dynamic variables of the system in a linguistic manner. The proposed method enables the designer systematically to derive the rule base of the control. We implement the scheme to both tracking and stabilizing control for a two-link rigid robot and overhead crane load swing. Numerical tests show the feasibility of the proposed control scheme.
Internet of Things 2 nd Warehouse Management System 3 rd Supply ChainNowadays, the warehouse acts as a competitive factor in any supply chain as it has a main role in linking all the partners in it. Hence, it has become very necessary to allocate its resources efficiently and manage it effectively. A sound warehouse management system can contribute to cost reduction and improve customer satisfaction. However, traditional warehouse management system models have become unsuitable and inefficient for today's market requirements. So, companies have started to adopt innovative approaches and technologies to be used for such applications; one of these technologies is the Internet of Things that enables the connection between several physical objects and produces a massive amount of real-time data that can be transferred to useful information that helps in managing and decision making. In this paper, the architecture of this application is illustrated, its potential benefits are overviewed, and a framework for implementing this technology in a warehouse is proposed. This system can help in achieving more control and monitoring on all the operations in the warehouse in real time, increase speed and efficiency, and prevent counterfeiting and inventory shortage. The proposed framework can be taken as a roadmap for enterprises to improve warehouses by using the Internet of Things. Also, the benefits of implementing the proposed framework and its challenges are proposed.
Due to the combinatorial nature of the resource-constrained project scheduling problem (RCPSP), there is a lot of artificial intelligence methods proposed to solve it. The Genetic Algorithm (GA), one of these methods, is considered to be a valuable search algorithm capable of finding a reasonable solution in a short computational time. The primary objective of this paper is to build a genetic algorithm for solving RCPSP problem aiming at minimizing project's makespan. Based on a comprehensive review of different GAs and a full factorial experiment, a proposed GA has been presented. The proposed algorithm has been tested on a well-known benchmark (PSPLIB). The computation results show that the proposed GA outperforms many published algorithms and on average performs as well as other algorithms. Also, the performance of the algorithm improves in solving large scale problems.
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