This article proposes an adaptive neuro-fuzzy inference system (ANFIS) for solving navigation problems of an autonomous ground vehicle (AGV). The system consists of four ANFIS controllers; two of which are used for regulating both the left and right angular velocities of the AGV in order to reach the target position; and other two ANFIS controllers are used for optimal heading adjustment in order to avoid obstacles. The two velocity controllers receive three sensor inputs: front distance (FD); right distance (RD) and left distance (LD) for the low-level motion control. Two heading controllers deploy the angle difference (AD) between the heading of AGV and the angle to the target to choose the optimal direction. The simulation experiments have been carried out under two different scenarios to investigate the feasibility of the proposed ANFIS technique. The simulation results have been presented using MATLAB software package; showing that ANFIS is capable of performing the navigation and path planning task safely and efficiently in a workspace populated with static obstacles.

A multi-pulse injection strategy for premixed charge compression ignition (PCCI) combustion was investigated in a four-valve, direct-injection diesel engine by a computational fluid dynamics (CFD) simulation using KIVA-3V code coupled with detailed chemistry. The effects of fuel splitting proportion, injection timing, spray angles, and injection velocity were examined. The mixing process and formation of soot and nitrogen oxide (NO_x) emissions were investigated as the focus of the research. The results show that the fuel splitting proportion and the injection timing impacted the combustion and emissions significantly due to the considerable changes of the mixing process and fuel distribution in the cylinder. While the spray, inclusion angle and injection velocity at the injector exit, can be adjusted to improve mixing, combustion and emissions, appropriate injection timing and fuel splitting proportion must be jointly considered for optimum combustion performance.

Purpose: The motion control of unmanned ground vehicles (UGV) is a challenge in the industry of automation. The purpose of this paper is to propose a fuzzy inference system (FIS) based on sensory information for solving the navigation challenge of UGV in cluttered and dynamic environments. Design/methodology/approach: The representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system. If dynamic obstacles move randomly in the operation field, the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations. This paper considers the construction of the FIS, which consists of two controllers. The first controller uses three sensors based on the obstacles distances from the front, right and left. The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay. The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments. Findings: Experimental analyses are conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the FIS. The reported simulation results are obtained using MATLAB software package. The results show that the controllers of the FIS consistently perform the manoeuvring task and manage the route plan efficiently, even in a complex environment that is populated with dynamic obstacles. The paper demonstrates that the destination was reached optimally using the shortest free route. Research limitations/implications: The paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move at various speeds and directions. The methodology of designing the FIS is accomplished to guide the UGV to the desired destination while avoiding collisions with obstacles. However, the methodology is approached using two-dimensional analyses. Hence, the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement. Originality/value: This paper presents the design of a FIS and its characterizations in dynamic environments, specifically for obstacles that move at different velocities. This facilitates an improved functionality of the operation of UGV.

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PI^λD^u (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.