Road Surface Estimation Using Machine Learning

Vehicle motion control systems are present on commercial vehicles to improve safety and driving comfort. Many of these control systems could be further improved given accurate online information about the road condition to accommodate for driving under poor weather conditions such as icy roads or heavy rain. However, sensors for direct friction measurement are not present on commercial vehicles due to production costs. Hence, it is beneficial to incorporate an online estimation scheme for road surface classification. This thesis focuses on investigating two fundamentally different machine learning-based methods for road surface classification. The first is an artificial neural network that provides a global function approximation of the underlying dynamics. In particular, Long Short-Term Memory (LSTM) units are used to capture temporal relationships within the training data and to mitigate the vanishing gradient problem. The second is an instance-based learning method referred to as Nadaraya-Watson Kernel Regression, where local function approximations are generated around the input data. Results indicated that both machine learning-based methods were able to classify road conditions to a reasonable degree of accuracy after tuning associated hyperparameters. However, each method has different benefits and drawbacks. The LSTM network model was capable of making accurate predictions on inputs drastically different from data points within the training data set, was generally more accurate on predictions associated with new driving maneuvers, required less storage for implementation, and had relatively short prediction times. Conversely, the Nadaraya-Watson Kernel Regression model was generally more accurate at making predictions on inputs that were very similar to data points within the training data set, did not require any training time to incorporate newly collected data into the model, and generated predictions that were more easily explainable

Novel improvements of empirical wireless channel models and proposals of machine-learning-based path loss prediction models for future communication networks.

Doctoral Degree. University of KwaZulu-Natal, Durban.Path loss is the primary factor that determines the overall coverage of networks. Therefore, designing reliable wireless communication systems requires accurate path loss prediction models. Future wireless mobile systems will rely mainly on the super-high frequency (SHF) and the millimeter-wave (mmWave) frequency bands due to the massively available bandwidths that will meet projected users’ demands, such as the needs of the fifth-generation (5G) wireless systems and other high-speed multimedia services. However, these bands are more sensitive and exhibit a different propagation behavior compared to the frequency bands below 6 GHz. Hence, improving the existing models and developing new models are vital for characterizing the wireless communication channel in both indoor and outdoor environments for future SHF and mmWave services. This dissertation proposes new path loss and LOS probability models and efficiently improves the well-known close-in (CI) free space reference distance model and the floating-intercept (FI) model. Real measured data was taken for both line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios in a typical indoor corridor environment at three selected frequencies within the SHF band, namely 14 GHz, 18 GHz, and 22 GHz. The research finding of this work reveals that the proposed models have better performance in terms of their accuracy in fitting real measured data collected from measurement campaigns. In addition, this research studies the impact of the angle of arrival and the antenna heights on the current and improved CI and FI models. The results show that the proposed improved models provide better stability and sensitivity to the change of these parameters. Furthermore, the mean square error between the models and their improved versions was presented as another proof of the superiority of the proposed improvement. Moreover, this research shows that shadow fading’s standard deviation can have a notable reduction in both the LOS and NLOS scenarios (especially in the NLOS), which means higher precision in predicting the path loss compared to the existing standard models. After that, the dissertation presents investigations on high-ordering the dependency of the standard CI path loss model on the distance between the transmitting and the receiving antennas at the logarithmic scale. Two improved models are provided and discussed: second-order CI and third-order CI models. The main results reveal that the proposed two models outperform the standard CI model and notable reductions in the shadow fading’s standard deviation values as the model’s order increases, which means that more precision is provided. This part of the dissertation also provides a trade-off study between the model’s accuracy and simplicity

A Review of Resonant Converter Control Techniques and The Performances

paper first discusses each control technique and then gives experimental results and/or performance to highlights their merits. The resonant converter used as a case study is not specified to just single topology instead it used few topologies such as series-parallel resonant converter (SPRC), LCC resonant converter and parallel resonant converter (PRC). On the other hand, the control techniques presented in this paper are self-sustained phase shift modulation (SSPSM) control, self-oscillating power factor control, magnetic control and the H-∞ robust control technique

A SIMULATION STUDY OF STATE-FEEDBACK CONTROL METHOD FOR ELECTRO HYDRAULIC SERVO MODEL

Electro hydraulic servo system is used by many industries due to its ability to impart large forces. It also has advantage in term of fast response and robustness. The electro hydraulic system suffered from errors of the transient response which are steady state error, settling time and the ripples. It is crucial to design a controller for the system to ensure the reliability of the system. Aiming at the characteristic of the system, steady state feedback control method is designed to compensate the error. The analysis of the system is done based on the transient response specifically on the actuator part. MATLAB Simulink is used as the simulation software to evaluate the force performance of state feedback controller method. The steady state error, settling time and ripple are observed and recorded for each controller. Three methods is applied, which are full feedback, state feedback with feed forward and integral control are compared with proportional, integral and derivatives (PID) controller. The result of each controller shows the differences performance. Based on the simulation results, the feedforward technique is found to be the best control technique for the electro hydraulic servo system due to the requirement performance such as percent overshoot, settling time, rise time and zero steady state error. This good result will directly benefit industries that use electro hydraulic system as their actuator for production machines

State-Feedback Controller Based on Pole Placement Technique for Inverted Pendulum System

This paper presents a state space control technique for inverted pendulum system using simulation and real experiment via MATLAB/SIMULINK software. The inverted pendulum is difficult system to control in the field of control engineering. It is also one of the most important classical control system problems because of its nonlinear characteristics and unstable system. It has three main problems that always appear in control application which are nonlinear system, unstable and non-minimumbehavior phase system. This project will apply state feedback controller based on pole placement technique which is capable in stabilizing the practical based inverted pendulum at vertical position. Desired design specifications which are 4 seconds settling time and 5 % overshoot is needed to apply in full state feedback controller based on pole placement technique. First of all, the mathematical model of an inverted pendulum system is derived to obtain the state space representation of the system. Then, the design phase of the State-Feedback Controller can be conducted after linearization technique is performed to the nonlinear equation with the aid of mathematical aided software such as Mathcad. After that, the design is simulated using MATLAB/Simulink software. The controller design of the inverted pendulum system is verified using simulation and experiment test. Finally the controller design is compared with PID controller for benchmarking purpose