In this paper, the Bees Algorithm was used to train multi-layer perceptron neural networks to model the inverse kinematics of an articulated robot manipulator arm. The Bees Algorithm is a recently developed parameter optimisation algorithm that is inspired by the foraging behaviour of honey bees. The Bees Algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. Three neural networks were trained to reproduce a set of input/output numerical examples of the inverse kinematics of the main three joints of an articulated robotic manipulator. The results prove the remarkable robustness of the Bees Algorithm, which consistently trained the neural networks to model the kinematics data with very high accuracy. The learning results obtained by the proposed algorithm are compared to the results obtained by the standard Backpropagation Algorithm and an Evolutionary Algorithm. The comparative study highlights the superior performance of the proposed Bees Algorithm over the other algorithms.
This paper proves the capability of the bees algorithm to solve complex parameter optimization problems for robot manipulator control. Two applications are presented. The first case considers the modelling of the inverse kinematics of an articulated robot arm using neural networks. The weights of the connections between the nodes need to be set so as to minimize the difference between the neural network model and the desired behaviour. In the proposed example, the bees algorithm is used to train three multilayer perceptrons to learn the inverse kinematics of the joints of a three-link manipulator. The second case considers the design of a hierarchical proportional–integral–derivative (PID) controller for a flexible single-link robot manipulator. The six gains of the PID controller need to be optimized so as to minimize positional inaccuracies and vibrations. Experimental tests demonstrated the validity of the proposed approach. In the first case, the bees algorithm proved very effective at optimizing the neural network models. Compared with the results obtained employing the standard back-propagation rule and an evolutionary algorithm, the bees algorithm obtained superior results in terms of training accuracy and robustness. In the second case, the proposed method demonstrated remarkable efficiency and consistency in the tuning of the PID controller parameters. In 50 independent optimization trials, the PID controllers designed using the bees algorithm consistently outperformed a robot controller designed using a standard manual technique.
This paper presents a predictive Energy Management System (EMS), aimed to improve the performance of a domestic PV-battery system and maximize self-consumption by minimizing energy exchange with the utility grid. The proposed algorithm facilitates a self-consumption approach, which reduces electricity bills, transmission losses, and the required central generation/storage systems. The proposed EMS uses a combination of Fuzzy Logic (FL) and a rule based-algorithm to optimally control the PV-battery system while considering the day-ahead energy forecast including forecast error and the battery State of Health (SOH). The FL maximizes the lifetime of the battery by using SOH and State of Charge (SOC) in decision making algorithm to charge/discharge the battery. The proposed Battery Management System (BMS) has been tested using Active Office Building (AOB) located in Swansea University, UK. Furthermore, it is compared with three recently published methods and with the current BMS utilized in the AOB to show the effectiveness of the proposed technique. The results show that the proposed BMS achieves a saving of 18% in the total energy cost over six months compared to a similar day-ahead forecast-based work. It also achieves a saving up to 95% compared to other methods (with a similar structure) but without a day-ahead forecast-based management. The proposed BMS enhances the battery's lifetime by reducing the average SOC up to 47% compared to the previous methods through avoiding unnecessary charge and discharge cycles. The impact of the PV system size and the battery capacity on the net exchanged energy with the utility grid is also investigated in this study.INDEX TERMS Battery management system, energy management system, fuzzy logic, state of charge, state of health.
The authors would like to acknowledge the ASTUTE 2020 (Advanced Sustainable Manufacturing Technologies) operation supporting manufacturing companies across Wales, which has been part-funded by the European Regional Development Fund through the Welsh Government and the participating Higher Education Institutions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.