Neuro-fuzzy identification of an internal combustion engine

Dynamic modeling and identification of an internal combustion engine (ICE) model is presented in this paper. Initially, an analytical model of an internal combustion engine simulated within SIMULINK environment is excited by pseudorandom binary sequence (PRBS) input. This random signals input is chosen to excite the dynamic behavior of the system over a large range of frequencies. The input and output data obtained from the simulation of the analytical model is used for the identification of the system. Next, a parametric modeling of the internal combustion engine using recursive least squares (RLS) technique within an auto-regressive external input (ARX) model structure and a nonparametric modeling using neuro-fuzzy modeling (ANFIS) approach are introduced. Both parametric and nonparametric models verified using one-step-ahead (OSA) prediction, mean squares error (MSE) between actual and predicted output and correlation tests. Although both methods are capable to represent the dynamic of the system very well, it is demonstrated that ANFIS gives better prediction results than RLS in terms of mean squares error achieved between the actual and predicted signals

A study in the use of fuzzy logic in the management of an automotive heat engine / electric hybrid vehicle powertrain

This thesis addresses the problem of the instant-by-instant control of the powertrain of a hybrid heat engine/electric vehicle. In the absence of a prototype vehicle on which the work could be carried out the work has taken the form of computer simulation experiments. In order to develop the powertrain control strategies, a computer model of a conceptual hybrid vehicle is then developed, containing components from real, production and prototype vehicles. The use of this component based modelling approach allows the models to be validated by comparing their predictions with the performance of the real vehicles in which the components are used. The previous work conducted in the field of hybrid vehicle powertrain control is then reviewed. It is found that fuzzy logic could potentially provide a means of controlling the hybrid powertrain in a realistic manner, in which some of the disadvantages of previous hybrid powertrain control strategies could be overcome. The results of initial simulation experiments are then reported, finding that whilst the basic method appears to have the potential to successfully control the powertrain, there is a need for an adaptive fuzzy powertrain controller. A review is then presented of previous work conducted in the field of adaptive fuzzy control, finding that none of the reported adaptive fuzzy control methods are capable of being easily applied in the case of the hybrid powertrain. An adaptive fuzzy controller is then developed, whose rule modification strategy is specifically designed to work in the hybrid powertrain control problem. This initial adaptive powertrain controller is then modified to improve its ability to control the overall performance of a hybrid vehicle, whilst maintaining vehicle driveability. It is found that this controller is able to adapt to the different driving styles of individual vehicle users within the space of a few simulated urban journeys. Experiments are then performed in which improvements in the overall efficiency of the vehicle powertrain are investigated. It is found that significant improvements in the operation of the powertrain are impossible, due to some of the features of the vehicle model and constraints placed upon the control strategy. Conclusions are then drawn, for the work done in the field of hybrid vehicle powertrain control and, also, for the work done in adaptive methods of fuzzy control. The most significant contribution in the field of hybrid powertrain control is the development of a controller that can adapt to the habits of different users. The most significant contribution in the field of fuzzy control is the form of the basic hybrid powertrain controller and the use of small fuzzy controllers in the powertrain controller adaptation strategy

Diagnostic and adaptive redundant robotic planning and control

Neural networks and fuzzy logic are combined into a hierarchical structure capable of planning, diagnosis, and control for a redundant, nonlinear robotic system in a real world scenario. Throughout this work levels of this overall approach are demonstrated for a redundant robot and hand combination as it is commanded to approach, grasp, and successfully manipulate objects for a wheelchair-bound user in a crowded, unpredictable environment. Four levels of hierarchy are developed and demonstrated, from the lowest level upward: diagnostic individual motor control, optimal redundant joint allocation for trajectory planning, grasp planning with tip and slip control, and high level task planning for multiple arms and manipulated objects. Given the expectations of the user and of the constantly changing nature of processes, the robot hierarchy learns from its experiences in order to more efficiently execute the next related task, and allocate this knowledge to the appropriate levels of planning and control. The above approaches are then extended to automotive and space applications

Mission Aware Energy Saving Strategies For Army Ground Vehicles

Fuel energy is a basic necessity for this planet and the modern technology to perform many activities on earth. On the other hand, quadrupled automotive vehicle usage by the commercial industry and military has increased fuel consumption. Military readiness of Army ground vehicles is very important for a country to protect its people and resources. Fuel energy is a major requirement for Army ground vehicles. According to a report, a department of defense has spent nearly $13.6 billion on fuel and electricity to conduct ground missions. On the contrary, energy availability on this plant is slowly decreasing. Therefore, saving energy in Army ground vehicles is very important. Army ground vehicles are embedded with numerous electronic systems to conduct missions such as silent and normal stationary surveillance missions. Increasing electrical energy consumption of these systems is influencing higher fuel consumption of the vehicle. To save energy, the vehicles can use any of the existing techniques, but they require complex, expensive, and time consuming implementations. Therefore, cheaper and simpler approaches are required. In addition, the solutions have to save energy according to mission needs and also overcome size and weight constraints of the vehicle. Existing research in the current literature do not have any mission aware approaches to save energy. This dissertation research proposes mission aware online energy saving strategies for stationary Army ground vehicles to save energy as well as to meet the electrical needs of the vehicle during surveillance missions. The research also proposes theoretical models of surveillance missions, fuzzy logic models of engine and alternator efficiency data, and fuzzy logic algorithms. Based on these models, two energy saving strategies are proposed for silent and normal surveillance type of missions. During silent mission, the engine is on and batteries power the systems. During normal surveillance mission, the engine is on, gear is on neutral position, the vehicle is stationary, and the alternator powers the systems. The proposed energy saving strategy for silent surveillance mission minimizes unnecessary battery discharges by controlling the power states of systems according to the mission needs and available battery capacity. Initial experiments show that the proposed approach saves 3% energy when compared with the baseline strategy for one scenario and 1.8% for the second scenario. The proposed energy saving strategy for normal surveillance mission operates the engine at fuel-efficient speeds to meet vehicle demand and to save fuel. The experiment and simulation uses a computerized vehicle model and a test bench to validate the approach. In comparison to vehicles with fixed high-idle engine speed increments, experiments show that the proposed strategy saves fuel energy in the range of 0-4.9% for the tested power demand range of 44-69 kW. It is hoped to implement the proposed strategies on a real Army ground vehicle to start realizing the energy savings

H-infinity Estimation for Fuzzy Membership Function Optimization

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a specific shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a few variables and the membership optimization problem can be reduced to a parameter optimization problem. The parameter optimization problem can then be formulated as a nonlinear filtering problem. In this paper we solve the nonlinear filtering problem using H∞ state estimation theory. However, the membership functions that result from this approach are not (in general) sum normal. That is, the membership function values do not add up to one at each point in the domain. We therefore modify the H∞ filter with the addition of state constraints so that the resulting membership functions are sum normal. Sum normality may be desirable not only for its intuitive appeal but also for computational reasons in the real time implementation of fuzzy logic systems. The methods proposed in this paper are illustrated on a fuzzy automotive cruise controller and compared to Kalman filtering based optimization

Sum Normal Optimization of Fuzzy Membership Functions

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a certain shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a small number of variables and the membership optimization problem can be reduced to a parameter optimization problem. This is the approach that is typically taken, but it results in membership functions that are not (in general) sum normal. That is, the resulting membership function values do not add up to one at each point in the domain. This optimization approach is modified in this paper so that the resulting membership functions are sum normal. Sum normality is desirable not only for its intuitive appeal but also for computational reasons in the real time implementation of fuzzy logic systems. The sum normal constraint is applied in this paper to both gradient descent optimization and Kalman filter optimization of fuzzy membership functions. The methods are illustrated on a fuzzy automotive cruise controller

Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies

An algorithm that learns from a set of examples should ideally be able to exploit the available resources of (a) abundant computing power and (b) domain-specific knowledge to improve its ability to generalize. Connectionist theory-refinement systems, which use background knowledge to select a neural network's topology and initial weights, have proven to be effective at exploiting domain-specific knowledge; however, most do not exploit available computing power. This weakness occurs because they lack the ability to refine the topology of the neural networks they produce, thereby limiting generalization, especially when given impoverished domain theories. We present the REGENT algorithm which uses (a) domain-specific knowledge to help create an initial population of knowledge-based neural networks and (b) genetic operators of crossover and mutation (specifically designed for knowledge-based networks) to continually search for better network topologies. Experiments on three real-world domains indicate that our new algorithm is able to significantly increase generalization compared to a standard connectionist theory-refinement system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file

Productivity analysis of horizontal directional drilling

The National Research Council of Canada reported that rehabilitation of municipal water systems between 1997 and 2012 would cost $28 billion (NRC, 2004). With the rapid increase of new installations, the need for replacement and repair of pipe utilities and also the demand for trenchless excavation methods, increase. This must be done with minimum disruption to public. One alternative to reduce disruption is to use horizontal directional drilling (HDD) for new pipe installation scenarios. Consequently, contractors, engineers, and decision makers are facing continuous challenges regarding to estimation of execution time and cost of new pipe installations, while using HDD. This is because productivity prediction and consequently the cost estimation of HDD involves a large number of objective and subjective factors that need to be considered. It is well known that prediction of both productivity and cost is an important process in establishing and employing management strategies for a construction operation. This calls for the need of developing a dedicated HDD productivity model that meets present day requirements of this area of construction industry. There are two main objectives of the current research. The first objective is to identify the factors that affect productivity of HDD operations. The second objective is to develop a productivity prediction model for different soil conditions. To achieve these two objectives a thorough literature review was carried out. Thereafter, data on potential factors on productivity were collected from HDD experts across North America and abroad. Following data collection, the current research identified managerial, mechanical, environmental and pipe physical conditions parameters operating in three types of soils: clay, rock and sandy soils. Prior to model development, Analytical Hierarchy Process (AHP) technique was used to classify and rank these factors according to their relative importance. A neurofuzzy (NF) approach is employed to develop HDD productivity prediction model for pipe installation. The merits of this approach are that it decreases uncertainties in results, addresses non-linear relationships and deals well with imprecise and linguistic data. The following eight factors were finally selected as inputs of the model to be developed: operator/ crew skills, soil type, drilling rig capabilities, machine conditions, unseen buried obstacles, pipe diameter, pipe length and site weather and safety conditions. The model is validated using actual project data. The developed NF model showed average validation percent of 94.7%, 82.3% and 86.7%, for clay, rock and sand, respectively. The model is also used to produce productivity curves (production rate vs. influencing factors) for each soil type. Finally, an automated user-friendly productivity prediction tool (HDD-PP) based on present NF model is developed to predict HDD productivity. This tool is coded in MatLab ® language using the graphical user interface tool (GUI). The tool was used to test a case study. It was proved to be helpful for contractors, consultants and HDD professionals in predicting execution time and to estimate cost of HDD projects during the preconstruction phase in the environment of imprecise and noisy inputs

Measuring and benchmarking the productivity of excavators in infrastructure projects: A deep neural network approach

Inefficiencies in the management of earthmoving equipment greatly contribute to the productivity gap of infrastructure projects. This paper develops and tests a Deep Neural Network (DNN) model for estimating the productivity of excavators and establishing a productivity measure for their benchmark. After investigating current practices for measuring the productivity of earthwork equipment during 13 interviews with selected industry experts, the DNN model was developed and tested in one of the ‘High Speed rail second phase’ (HS2) sites. The accuracy of prediction achieved by the DNN model was evaluated using the coefficient of determination (R2) and the Weighted Absolute Percentage Error (WAPE) resulting in 0.87 and 69.64%, respectively. This is an adequate level of accuracy when compared to other similar studies. However, according to the WAPE method, the accuracy is still 10.36% below the threshold (i.e. 80%) expected by the industry experts. An inspection of the prediction results over the testing period (21 days) revealed better precision in days with high excavation volumes compared to days with low excavation volumes. This was attributed to the likely involvement of manual work (i.e. archaeologists in the case of the selected site) alongside some of the excavators, which caused gaps in telematics data. This indicates that the accuracy attained is adequate, but the proposed approach is more accurate in a highly mechanised environment (i.e. excavation work with equipment predominantly and limited manual interventions) compared to a mixed mechanised-manual working environment. A bottom-up benchmark measure (i.e. excavation rate) that can be used to measure and benchmark the excavation performance of an individual or a group of equipment, through a work area, to a whole site was also proposed and discussed