a b s t r a c tConventional fuzzy logic controller is applicable when there are only two fuzzy inputs with usually one output. Complexity increases when there are more than one inputs and outputs making the system unrealizable. The ordinal structure model of fuzzy reasoning has an advantage of managing high-dimensional problem with multiple input and output variables ensuring the interpretability of the rule set. This is achieved by giving an associated weight to each rule in the defuzzification process. In this work, a methodology to design an ordinal fuzzy logic controller with application for obstacle avoidance of Khepera mobile robot is presented. The implementation will show that ordinal structure fuzzy is easier to design with highly interpretable rules compared to conventional fuzzy controller. In order to achieve high accuracy, a specially tailored Genetic Algorithm (GA) approach for reinforcement learning has been proposed to optimize the ordinal structure fuzzy controller. Simulation results demonstrated improved obstacle avoidance performance in comparison with conventional fuzzy controllers. Comparison of direct and incremental GA for optimization of the controller is also presented.
The conventional methods of observer poles placement in sensor fault detection usually adopt the trial-and-error methods. These methods cannot achieve global optimal performance because of their fixed poles placement and it leads to an observer with constant parameters, which could be reducing the system performance. Therefore, this paper proposes a fuzzy-based observer tuning method to optimize and adapt the selection of poles locations to determine the optimal gains of the observer, and it is experimentally applied to a composite sensor fault detection. Fuzzy logic is a promising method that could overcome the trial-and-error method challenges by introducing better adaptation and system robustness. The proposed observer structure includes adaptive tuning corresponding to an unknown input. Utilizing self-tuning for the observer correction stage, the gain is going to be updated online using the proposed fuzzy adaptive poles placement (FAPP) system. This paper validated the system simulation by implementing fault detection algorithms by using a real-time embedded observer-based system. The experimental results demonstrate the effectiveness of the proposed fuzzy-based observer schemes at detecting sensor faults in the Brushless DC (BLDC) motors, with significantly better performance than conventional counterparts' methodologies. The experiments indicate that the average estimation error is 0.146, which less by 43.8% than was obtained for high levels of noise and disturbances compared with the traditional Luenberger observer approach.
Deploying large numbers of mobile robots which can interact with each other produces swarm intelligent behavior. However, mobile robots are normally running with finite energy resource, supplied from finite battery. The limitation of energy resource required human intervention for recharging the batteries. The sharing information among the mobile robots would be one of the potentials to overcome the limitation on previously recharging system. A new approach is proposed based on integrated intelligent system inspired by foraging of honeybees applied to multimobile robot scenario. This integrated approach caters for both working and foraging stages for known/unknown power station locations. Swarm mobile robot inspired by honeybee is simulated to explore and identify the power station for battery recharging. The mobile robots will share the location information of the power station with each other. The result showed that mobile robots consume less energy and less time when they are cooperating with each other for foraging process. The optimizing of foraging behavior would result in the mobile robots spending more time to do real work.
In this study, we examined the effect of phase noise on the optical millimeter-wave (mm-wave) signal in a dense wavelength division multiplexing radio-over-fiber (DWDM-RoF) system. A single modulator was used to generate the optical mm-wave signal in the DWDM-RoF system. This paper addresses the impact of phase noise, which results from phase imbalance, on the optical mm-wave signal. To lower the effect of phase noise on the optical mm-wave signal, the phase imbalance should be controlled. The phase imbalance can be controlled and decreased by adjusting the phase at the phase shift (PS). The system performance was analyzed using various parameters such as bit error rate (BER), signal-to-noise ratio (SNR), optical signal to noise ratio (OSNR), and error vector magnitude (EVM). From the results, we found the phase imbalance affected the optical mm-wave signal due to the imbalanced splitting of the signal intensity at the MZM. The phase imbalance impacts the phase noise, which impacts the optical mm-wave signal. The phase noise could be decreased by controlling the phase imbalance at the phase of 5π/12. The best results at the phase of 5π/12 were collected for phase noise at 0.02 degrees.
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