The availability of location information has become a key factor in today's communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.

Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

A review: On path planning strategies for navigation of mobile robot

A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems

Artificial intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics and motor drives. Neural networks have created a new and advancing frontier in power electronics, which is already a complex and multidisciplinary technology that is going through dynamic evolution in the recent years. This paper gives a comprehensive introduction and perspective of neural network applications in the intelligent control and estimation for power electronics and motor drives area. The principal topologies of neural networks that are currently most relevant for applications in power electronics have been reviewed including the detailed description of their properties. Both feedforward and feedback or recurrent architectures have been covered in the description. The application examples that are discussed in this paper include nonlinear function generation, delayless filtering and waveform processing, feedback signal processing of vector drive, space vector PWM of two-level and multilevel inverters, adaptive flux vector estimation, and some of their combination for vector-controlled ac drive. Additional selected applications in the literature are included in the references. From the current trend of the technology, it appears that neural networks will find widespread applications in power electronics and motor drives in future

Neural Network Applications in Power Electronics and Motor Drives—An Introduction and Perspective

This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm.

Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems

Output-feedback control of uncertain nonlinear systems using a self-structuring adaptive fuzzy observer

Previous research on mobile robots has been based on models of mobile robots with invariant weight parameters. However, the most practical mobile robots that bear heavy burdens have varying weights and centres of gravity. This paper suggests a design for an adaptive PD controller that estimates and adapts to the weight-related parameters of robots that bear varying heavy burdens. Using the suggested adaptive PD controller, the motion of mobile robots can be independent of the weight-related parameters and the operational performance is confirmed by target tracking simulations.

https://journals.sagepub.com/doi/pdf/10.5772/50981

Design of an Adaptive PD Controller for the Weight-Independent Motion Control of a Mobile Robot

Location-awareness can be applied to the various consumer applications. A received signal strength (RSS) based localization system is relatively inexpensive and simple to be implemented without additional hardware supports. However, the radio signals are strongly affected by the obstructed interferences. This is a difficult problem for the RSS based localization systems to be implemented in real world. To solve this problem, we propose a practical and robust localization algorithm in the obstructed environments. The proposed algorithm uses the Maximum Likelihood Estimation (MLE) based on the position probability grid. In addition, the proposed algorithm detects and compensates the large measurement error using the Min-Max algorithm. We evaluated the performance of the proposed algorithm in the obstructed environments. Performance results show that the proposed algorithm outperforms other algorithms on the obstructed environments.

Robust localization over obstructed interferences for inbuilding wireless applications

This paper describes the design of the robust adaptive fuzzy controller for uncertain nonlinear dynamical systems with unknown nonlinearities. These unknown nonlinearities are approximated by the fuzzy system with a set of fuzzy IF-THEN rules whose parameters are adjusted on-line according to some adaptive laws for the purpose of controlling the output of the nonlinear system to track a given trajectory. The Lyapunov synthesis approach is used to develop a state-feedback adaptive control algorithm based on the adaptive fuzzy model. The key assumption is that the reconstruction errors satisfy certain bounding conditions. The control law consists of two terms. One is the certainty equivalent control and the other is the bounding control. A bounding parameter adaptive law is used to obtain this bounding control. The overall control system guarantees that the tracking error converges in the small neighborhood of zero and that all signals involved are uniformly bounded. The theoretical results are illustrated through a simulation example.

Robust adaptive fuzzy controller for nonlinear system with unknown nonlinearities

An adaptive power converter control system that contains an adaptive fuzzy controller is presented. The proposed APCCS (adaptive power converter control system) combines fuzzy logic with adaptive learning algorithm to adjust parameters of the fuzzy control to the most appropriate values. Neither the exact mathematical models of power converters nor the tuning process of the parameters of the fuzzy control are needed in the proposed system. The transient, steady-state responses, and load regulation of the proposed system will be compared with those of the conventional fuzzy logic control system through the simulation results.

An adaptive fuzzy controller for power converters

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