In this article, a genetic algorithm–based proportional integral differential–type fuzzy logic controller for speed control of brushless direct current motors is presented to improve the performance of a conventional proportional integral differential controller and a fuzzy proportional integral differential controller, which consists of a genetic algorithm–based fuzzy gain tuner and a conventional proportional integral differential controller. The tuner is used to adjust the gain parameters of the conventional proportional integral differential controller by a new fuzzy logic controller. Different from the conventional fuzzy logic controller based on expert experience, the proposed fuzzy logic controller adaptively tunes the membership functions and control rules by using an improved genetic algorithm. Moreover, the genetic algorithm utilizes a novel reproduction operator combined with the fitness value and the Euclidean distance of individuals to optimize the shape of the membership functions and the contents of the rule base. The performance of the genetic algorithm–based proportional integral differential–type fuzzy logic controller is evaluated through extensive simulations under different operating conditions such as varying set speed, constant load, and varying load conditions in terms of overshoot, undershoot, settling time, recovery time, and steady-state error. The results show that the genetic algorithm–based proportional integral differential–type fuzzy logic controller has superior performance than the conventional proportional integral differential controller, gain tuned proportional integral differential controller, conventional fuzzy proportional integral differential controller, and scaling factor tuned fuzzy proportional integral differential controller.

In wireless sensor networks, organizing nodes into clusters, finding routing paths and maintaining the clusters are three critical factors that significantly impact the network lifetime. In this paper, using a chaotic genetic algorithm, a clustering routing protocol combined with these three features called CRCGA is proposed to improve the network energy efficiency and load balancing. In CRCGA, the chaotic genetic algorithm is used to select the best cluster heads (CHs) and to find the optimal routing paths by coding them into a single chromosome simultaneously. Chaotic genetic operators based on a novel fitness function considering minimum energy consumption and load balancing along with new determination conditions make the algorithm converge quickly. Besides, an adaptive round time considering energy and load balancing is presented to maintain the clusters so as to further reduce energy consumption. Simulation results indicate that CRCGA is better than LEACH, GECR, OMPFM and GADA-LEACH in terms of convergence speed, energy efficiency, load balancing, network throughput and lifetime. INDEX TERMS WSNs, Multi-hop routing, Chaotic genetic algorithm, Clustering, Energy and load Balancing.

Organizing nodes into clusters and forwarding data to the Base Station (BS) in clustering routing protocols have been widely utilized to improve the energy efficiency, scalability and stability of Wireless Sensor Networks (WSN). Making decisions on how many clusters are formed, which nodes are selected as Cluster Heads (CHs) and who become the relay nodes significantly impact the network performance. Therefore, a Distributed clustering routing protocol combined Affinity Propagation (AP) with Fuzzy Logic called DAPFL is proposed in this paper, which considers not only energy efficiency but also energy balance to extend the network lifetime. In DAPFL, AP is firstly used to determine the number of clusters and select the best CHs simultaneously based on residual energy, distance between nodes. Then the optimal next-hop CHs are chosen by using fuzzy logic system with residual energy, data length and distance to BS as descriptors. Simulations in different scenarios are carried out to verify the effectiveness of DAPFL, and the results show that DAPFL exhibits the promising performance in terms of network energy consumption, standard deviation of residual energy, network throughput and lifetime, compared with the up-to-date distributed clustering routing protocols EEFUC, EEFRP, LEACH-AP and APSA.

This paper proposes a linear quadratic regulator (LQR) optimized back propagation neural network (BPNN) PI controller called LN-PI for the speed control of brushless direct current (BLDC) motor. The controller adopts BPNN to adjust the gain [Formula: see text] and [Formula: see text] of PI, which improves the dynamic characteristics and robustness of the controller. Moreover, LQR is adopted to optimize the output of BPNN so as to make it close to the target PI gains. Finally, the optimized control output is inputted into the BLDC motor system to achieve speed control. The performance analysis of the proposed controller is presented to compare with traditional PI controller, neural network PI controller and LQR optimized PI controller under MATLAB/Simulink, the results shows that the proposed controller effectively improves the response speed, reduces the steady-state error and enhances the anti-interference ability.

A trust‐aware secure routing protocol (TSRP) for wireless sensor networks is proposed in this paper to defend against varieties of attacks. First, each node calculates the comprehensive trust values of its neighbors based on direct trust value, indirect trust value, volatilization factor, and residual energy to defend against black hole, selective forwarding, wormhole, hello flood, and sinkhole attacks. Second, any source node that needs to send data forwards a routing request packet to its neighbors in multi‐path mode, and this continues until the sink at the end is reached. Finally, the sink finds the optimal path based on the path's comprehensive trust values, transmission distance, and hop count by analyzing the received packets. Simulation results show that TSRP has lower network latency, smaller packet loss rate, and lower average network energy consumption than ad hoc on‐demand distance vector routing and trust based secure routing protocol.