In this paper, a generic multi-sensor fusion framework is developed for the localization of intelligent vehicles and mobile robots. The localization framework is based on moving horizon estimation (MHE). Unlike the commonly used probabilistic filtering algorithms – for example, extended Kalman filter (EKF) and unscented Kalman filter (UKF) – MHE relies on solving successive least squares optimization problems over the innovation of multiple sensors’ measurements and a specific estimation horizon. In this paper, we present an efficient and generic multi-sensor fusion scheme, based on MHE. The proposed multi-sensor fusion scheme is capable of operating with different sensors’ rates, missing measurements, and outliers. Moreover, the proposed scheme is based on a multi-threading architecture to reduce its computational cost, making it more feasible for practical applications. The MHE fusion method is tested using simulated data as well as real experimental data sequences from an intelligent vehicle and a mobile robot combining measurements from different sensors to get accurate localization results. The performance of MHE is compared against that of UKF, where the MHE estimation results show superior performance.

The use of semi-active dampers in suspension systems is increasing rapidly in the automotive industry. This is attributed to the fact that damping can be adjusted to improve ride comfort and road handling. However, accomplishing ride comfort and better road handling concurrently is challenging. In this paper, we propose an intelligent system to optimise the suspension performance in terms of concurrently achieving the above-mentioned objectives. A neural network-based fuzzy logic controller is designed to control the system that embodies this damper. The proposed controller can enhance the vehicle handling and ride comfort concurrently while consuming less energy than existing control methodologies.

In this paper we propose a neuro-fuzzy (NF) control strategy to enhance desired suspension performance. The proposed method consists of two parts: a fuzzy control strategy to establish an efficient controller to improve ride comfort and road handling (RCH) and an inverse mapping model to estimate the current needed for a semi-active damper. The fuzzy logic rules are extracted based on Skyhook and Groundhook. The inverse mapping model is based on an artificial neural network and incorporated into the fuzzy controller to enhance RCH. To validate the effectiveness of the proposed NF controller, a quarter car model is adopted and numerical analysis is presented. To verify the performance of the NF controller (NFC), comparisons with existing semiactive techniques are made and two sets of results are reported. First, a sinusoidal road input is considered and time domain results are presented. Second, for the same sinusoidal input, frequency response of the developed controllers is obtained. It is shown that the developed NFC enhances RCH considerably and outperforms other existing controllers in terms of both ride comfort and handling

A few studies have been conducted recently in order to improve the aerodynamic performance of Darrieus vertical-axis wind turbines with straight blades (H-type VAWTs). The blade pitch angle control is proposed to enhance the performance of H-type VAWTs. This paper aims to investigate the performance of an H-type VAWT in terms of its power output and self-starting capability using an intelligent blade pitch control strategy based on a multi-layer perceptron artificial neural network (MLP-ANN) method. The performance of the proposed blade pitch controller is investigated by adding a conventional controller (PID) to the MLP-ANN controller (i.e., a hybrid controller). The dynamics of an H-type VAWT is mathematically modeled in a nonlinear state space for the stability analysis in the sense of Lyapunov. The effectiveness of the proposed pitch control system is validated by building an H-type VAWT prototype model that is extensively tested outdoors under different conditions for both fixed and variable pitch angle configurations. Results demonstrated that the blade-pitching technique enhanced the power output of an H-type VAWT by approximately $22\%$. The hybrid controller that used a high percentage of the MLP-ANN controller achieved a better control performance by reducing the overshoot of the control response at high rotor speeds.

This article presents redundancy resolution and disturbance rejection via torque optimization in hybrid cable-driven robots (HCDRs). To begin with, we present a redundant HCDR for nonlinear whole-body system modeling and model reduction. Based on the reduced dynamic model, two new methods are proposed to solve the redundancy resolution problem: 1) joint-space torque optimization for actuated joints (TOAJ) and 2) joint-space torque optimization for actuated and unactuated joints (TOAUJ), and they can be extended to other HCDRs. Compared to the existing approaches, this article provides the first solution (TOAUJ-based method) for HCDRs that can solve the redundancy resolution problem as well as disturbance rejection. Additionally, this article develops detailed algorithms targeting TOAJ and TOAUJ implementation. A simple yet effective controller is designed for analysis and validation. Case studies are conducted to evaluate the performance of TOAJ and TOAUJ. The results show the effectiveness of the aforementioned approaches.