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This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster–Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a(More)
Heat and air conditioning losses in buildings and factories lead to a large amount of wasted energy. The Action Plan for Energy Efficiency of the Commission of the European Communities (2008) estimates that the largest cost-effective energy savings potential lies in residential (≈ 27%) and commercial (≈ 30%) buildings. Imagine a technology that creates a(More)
This paper presents a design of the fuzzy logic control for a permanent magnet DC motor. The main objective is to achieve a robust controller under disturbances and unmodeled dynamics acting, such as load torque, dead zone, measurement noise and nonlinearities. The whole system contains the DC motor, driver, tachogenerator, external load and microprocessor(More)
This paper develops a fuzzy logic position controller which membership functions are tuned by genetic algorithm. The main goals are to ensure both successfully velocity and position trajectories tracking between the mobile robot and the reference cart. The proposed fuzzy controller has two inputs and two outputs. The first input represents the distance(More)
Ncural networks and fuzzy systcnis havc bccn applicd very succcssfiilly iii tlic idcntification and control of dynamic systcms. This papcr prcscnts combination of the fuzzy logic controllcr and ncural nctwork identification structurc, intcgra~cd into robotic systcm, to providc cxtcnsivc capabilitics. Wc first discuss tiic fuzzy logic controllcr (FLC),(More)
This paper proposes an extension of neural network identification capabilities for on-line identification of a nonlinear closed-loop control system. The neural network (NN) is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate. The optimization algorithm is performed at each sample time to compute the optimal(More)