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Solution of Combined Heat and Power Economic Dispatch Problem Using Direct Optimization Algorithm 被引量:1
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作者 Dedacus N. Ohaegbuchi Olaniyi S. Maliki +1 位作者 Chinedu P. A. Okwaraoka Hillary Erondu Okwudiri 《Energy and Power Engineering》 CAS 2022年第12期737-746,共10页
This paper presents the solution to the combined heat and power economic dispatch problem using a direct solution algorithm for constrained optimization problems. With the potential of Combined Heat and Power (CHP) pr... This paper presents the solution to the combined heat and power economic dispatch problem using a direct solution algorithm for constrained optimization problems. With the potential of Combined Heat and Power (CHP) production to increase the efficiency of power and heat generation simultaneously having been researched and established, the increasing penetration of CHP systems, and determination of economic dispatch of power and heat assumes higher relevance. The Combined Heat and Power Economic Dispatch (CHPED) problem is a demanding optimization problem as both constraints and objective functions can be non-linear and non-convex. This paper presents an explicit formula developed for computing the system-wide incremental costs corresponding with optimal dispatch. The circumvention of the use of iterative search schemes for this crucial step is the innovation inherent in the proposed dispatch procedure. The feasible operating region of the CHP unit three is taken into account in the proposed CHPED problem model, whereas the optimal dispatch of power/heat outputs of CHP unit is determined using the direct Lagrange multiplier solution algorithm. The proposed algorithm is applied to a test system with four units and results are provided. 展开更多
关键词 Economic Dispatch Lagrange Multiplier Algorithm Combined Heat and Power Constraints and Objective Functions Optimal Dispatch
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Optimal Path Planning for a Remote Sensing Unmanned Ground Vehicle in a Hazardous Indoor Environment 被引量:1
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作者 Mohammad R. Alenezi Abdullah M. Almeshal 《Intelligent Control and Automation》 2018年第4期147-157,共11页
Urban search and rescue robots are playing an increasingly important role during disasters and with their ability to search within hazardous and dangerous environments to assist the first respond teams. Surveying and ... Urban search and rescue robots are playing an increasingly important role during disasters and with their ability to search within hazardous and dangerous environments to assist the first respond teams. Surveying and remote sensing the hazardous areas are two of the urgent needs of the rescue team to identify the risks before the intervention of the emergency teams. With such time-critical missions, the path planning and autonomous navigation of the robot is one of the primary concerns due to the need of fast and feasible path that is comprehensive enough to assess the associated risks. This paper presents a path planning method for navigating an unmanned ground vehicle within in an indoor hazardous area with minimum priori information. The algorithm can be generalized to any given map and is based on probabilistic roadmap path planning method with spiral dynamics optimization algorithm to obtain the optimal navigating path. Simulations of the algorithm are presented in this paper, and the results promising results are illustrated using Matlab and Simulink simulation environments. 展开更多
关键词 REMOTE Sensing PATH PLANNING UGV AUTONOMOUS Mobile ROBOT
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Load Disturbance Conditions for Current Error Feedback and Past Error Feedforward State-Feedback Iterative Learning Control 被引量:1
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作者 Athari Alotaibi Asmaa Alkandri Muhammad Alsubaie 《Intelligent Control and Automation》 2021年第2期65-72,共8页
<div style="text-align:justify;"> <span style="font-family:Verdana;">Iterative learning control is a controlling tool developed to overcome periodic disturbances acting on repetitive sy... <div style="text-align:justify;"> <span style="font-family:Verdana;">Iterative learning control is a controlling tool developed to overcome periodic disturbances acting on repetitive systems. State-feedback ILC controller was designed based on the use of the small gain theorem. Stability conditions were reported in the case of past error and current error feedback schemes based on Singular values. Disturbances acting on the load of the system w</span><span style="font-family:Verdana;">ere </span><span style="font-family:Verdana;">reported for the case of past error feedforward only which kept the investigation of the current error feedback as an open question. This paper develops </span><span style="font-family:Verdana;">a comparison between the past error feedforward and current error feedback schemes disturbance conditions in singular values. As a result, the conditions found highly support the use of the past error over the current error feedback.</span> </div> 展开更多
关键词 Iterative Learning Control Disturbance Conditions Singular Values
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Using Singular Value to Set Output Disturbance Limits to Feedback ILC Control
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作者 Rashid Alzuabi Athari Alotaibi Humoud Alqattan 《Intelligent Control and Automation》 2022年第2期17-24,共8页
Iterative Learning Control is an effective way of controlling the errors which act directly on the repetitive system. The stability of the system is the main objective in designing. The Small Gain Theorem is used in t... Iterative Learning Control is an effective way of controlling the errors which act directly on the repetitive system. The stability of the system is the main objective in designing. The Small Gain Theorem is used in the design process of State Feedback ILC. The feedback controller along with the Iterative Learning Control adds an advantage in producing a system with minimal error. The past error and current error feedback Iterative control system are studied with reference to the region of disturbance at the output. This paper mainly focuses on comparing the region of disturbance at the output end. The past error feed forward and current error feedback systems are developed on the singular values. Hence, we use the singular values to set an output disturbance limit for the past error and current error feedback ILC system. Thus, we obtain a result of past error feed forward performing better than the current error feedback system. This implies greater region of disturbance suppression to past error feed forward than the other. 展开更多
关键词 Iterative Learning Control Disturbance Output Singular Values
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