In HEV and EV, higher fuel economy is achieved by operating the ICE and electric motor in the most efficient region and by using regenerative braking. Such a braking system converts, transfers, stores and reuses kinetic energy which would otherwise be dissipated as heat through friction brakes to the environment. This research investigates the control of braking for a mixed-mode braking system in a these vehicles based on the proportion of braking energy that can be stored. Achieving mixed-mode braking requires the ‘blending’ of the two systems (regenerative and friction), and in brake blending, the electric motor/generator (M/G) and the hydraulic actuation pressure are controlled together to meet the driver’s braking demand. The research presented here has established a new robust dynamic modelling procedure for the design of combined regenerative and hydraulic braking systems. Direct torque control and pressure control were selected as the control criteria in both brakes. Two simulation models have been developed in Matlab/Simulink to generate analysis the performance of the control strategy in the blended braking system. Integration of the regenerative braking system with ABS has also been completed, based on two conditions, with and without the deactivation of the regenerative braking. Verification of the models is presented, based on experimental work on two EVs manufactured by TATA Motors; the ACE light commercial vehicle and the VISTA small passenger car. It is concluded that braking demand and vehicle speed determine the operating point of the motor/generator and hence the regenerative braking ratio.

A soft quadruped robot is being developed at the Department of Machine Design and Department of Production Engineering at KTH. The legs of the robot consist of four continuum actuators that can achieve complex movements. In order to efficiently develop gaits for the robot, reinforcement learning will be used. The learning process will use data from simulation instead of directly from the real robot to save time and resources. However, it is significantly more computationally expensive to simulate soft robotics than rigid, because the physical laws of flexible materials are inherently complex. Because of this, soft robot simulations tend to be slower which limits their usability for reinforcement learning. This thesis explores simulation modelling options in Matlab Simscape for the soft quadruped robot, that can be used in reinforcement learning. Four simulation models of the soft actuator were implemented in order to be tested and compared. Two actuation methods and two build options were chosen based on the literature study and related works, and were then permuted for the different combinations. The tested combinations are: lumped-parameter method actuated by internal force, flexible beam actuated by internal force, lumped-parameter method actuated by cable/pulley network and flexible beam actuated by cable/pulley network. The four actuators were built and tested separately. Computational time and simulation-to-reality gap were used for evaluating the modeling methods. The results show that the best option when modelling the soft actuator for reinforcement learning in Matlab Simscape is to use the lumped-parameter method in combination with a cable and pulley network. High accuracy level can still be achieved despite not keeping the true number of attachment points between the cable and actuator. The number of pulleys in the model is linearly correlated to the time cost required to simulate the model. / En mjuk fyrbent robot är under utveckling vid institutionen för maskinkonstruktion och institutionen för industriell produktion på KTH. Robotens ben består av fyra kontinuerligt deformerbara ställdon som kan åstadkomma komplexa rörelser. För att effektivt utveckla gångstilar till roboten kommer förstärkt inlärning att användas. Inlärningsprocessen kommer att använda data från simulering istället för från den fysiska roboten för att spara tid och resurser. Det är dock betydligt dyrare beräkningsmässigt att simulera mjuk robotik än styv, eftersom flexibla material är mer komplexa. På grund av detta tenderar simuleringar av mjuka robotar att vara långsammare, vilket begränsar deras användbarhet för förstärkt inlärning. Detta examensarbete utforskar därför alternativ för modellering och simulering av den mjuka fyrbenta roboten i Matlab Simscape, med målet att den ska kunna användas med förstärkt inlärning. Fyra olika simuleringsmodeller av det mjuka ställdonet implementerades för att testas och jämföras. Två aktiveringsmetoder och två konstruktionsalternativ valdes baserat på litteraturstudien och relaterade arbeten, och permuterades sedan till möjliga versioner. De testade versionerna är således: klumpparametermetod som aktiveras av intern kraft, flexibel balk som aktiveras av intern kraft, klumpparametermetod som aktiveras av kabelnätverk och flexibel balk som aktiveras av kabelnätverk. De fyra ställdonen byggdes och testades separat. Beräkningstid och grad av verklighetstrogenhet, användes för att jämföra resultaten av dessa tester. Resultaten visar att det bästa alternativet vid modellering av det mjuka ställdonet för förstärkt inlärning i Matlab Simscape är att använda klumpparametermetoden i kombination med ett kabelnätverk. Hög noggrannhetsnivå kan uppnås trots att man inte bibehåller det verkliga antalet fästpunkter mellan kabeln och ställdonet. Antalet fästpunkter för kabeln i modellen är linjärt korrelerat till den tidskostnad som krävs för att simulera modellen.

Fundamental intuition of aerodynamics begins with understanding steady flow, a time- independent flow state. A fluid region undergoing steady flow consists of constant properties such as pressure and velocity at different positions in the flow field. This time-independent principle is crucial for beginning a foundation of understanding aerodynamics; however, analyzing this state of flow was beyond the limit at my university's Fundamentals of Aerodynamics course. There was minimal education on time-dependent unsteady flow, which created a vacuum on my understanding of how flow can be analyzed with time. The purpose of writing this thesis is to create a framework for aspiring learners of aerodynamics to better comprehend unsteady flow, including myself. The basis for developing an understanding of unsteady flow is accomplished by analyzing the aerodynamics of a simple two-dimensional zero-thickness flat plate, using a numerical method called Discrete Vortex Method under steady and unsteady conditions. Constructing a numerical method for steady and unsteady flow requires a software to compute enormous quantities of linear equations, therefore a combination of numerous arguments, functions, and loops were developed on MATLAB written in the C/C++ languages. Results from the numerical methods will be compared with the experimental and theoretical results from Katz & Plotkin (2001). The Steady Discrete Vortex Method was a basis for calculating the circulation of the flat plate at varying angles of attack and freestream velocities. The Unsteady Discrete Vortex Method derived much of the self-induced calculations in the body-fixed coordinate system. At the same time, a time-stepping method was developed to calculate the coordinates as the flat plate and shed vortices translated from the origin of an additional frame of reference called the inertial coordinate system. A wake vortex is shed from the trailing-edge of the flat plate at each time step iv to model vorticity shed from a body in motion. The flat plate undergoes sudden acceleration and plunging maneuvers to demonstrate further effects of unsteady aerodynamic conditions. The results from the flat plate undergoing sudden acceleration with a Reynolds number of 68,435.8 was an increasing proportionality between the lift and circulation of the steady and unsteady case until reaching a constant trend as time increases, demonstrating the nature of low-speed flow reaching a steady state after a given period. The results from the flat plate undergoing plunging with a Reynolds number of 106,759.8 demonstrate a sinusoidal trend in the normal force experienced as the flat plate traverses in its sinusoidal plunging translation like that observed in the theoretical results. This thesis intends to expand on the understanding of unsteady aerodynamics by developing a numerical method that can alter its dependent factors to visualize the effects of changing specific parameters on pressure and force acting on the two-dimensional body.

The use of orthogonal frequency-division multiplexing (OFDM) by wireless standards is often preferred due to its high spectral efficiency and ease of implementation. However, data transmission via OFDM still suffers when passing through a noisy channel. In order to maximize the abilities of OFDM, channel effects must be corrected. Unfortunately, channel estimation is often difficult due to the nonlinearity and randomness present in a practical communication channel. Recently, machine learning based approaches have been used to improve existing channel estimation algorithms for a more efficient transmission. This thesis investigates the application of artificial neural networks (ANNs) as a means of improving existing channel estimation techniques. Multi-layer feed forward neural networks (FNNs) and convolutional neural networks (CNNs) are tested on a variety of random fading channels with different signal-to-noise ratios (SNRs) via computer simulations. Compared to the conventional least squares (LS) algorithm, the approach based on CNN can reduce the bit error rate (BER) of data transmission by an average of 47.59%.

Zur Produktion von gewirkten Textilien werden mehrere Nadeln auf eine Legebarre gesetzt, die durch Servoantriebe positioniert werden. Aufgrund der erzwungenen Bewegung kann der Antriebsstrang bei hohen Drehzahlen zur Schwingung angeregt werden. Die richtige Wahl der Steuerkurven ist daher eine sehr wichtige und anspruchsvolle Aufgabe, die durch ein Antriebsstrangmodell unterstützt werden soll. Dafür wird am Beispiel eines Teststands ein Modell des Antriebsstranges in MATLAB/Simulink® entwickelt. Für das Antriebsstrangmodell müssen eine geeignete Modellordnung und Modellparameter gewählt werden. Die unbekannten Modellparameter werden durch eine Parameteridentifikation ermittelt. Mit einer Validierung wird ein geeignetes Anregungsspektrum für die Bestimmung der Modellordnung und die Parameteridentifikation ermittelt.

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