This thesis deals with advanced programming of CNC milling machines in Sinumerik 840D powerline control system. Mostly it is aimed on issues of synchronized actions. These actions can adaptively react on progress of milling process regarding to signal detection from the machine and execute specific action. In the introduction of the thesis there are briefly described basic issues and fundamentals of CNC machines and control systems programming. The problematic regarding the programming of synchronized actions is also described. The examples with evaluation of their function are created in the practical part of thesis.

Plastic injection moulding machines are a positive example of the possibilities in terms of performance and energy efficiency of modern hydraulic drives technology. In addition to the performance and energy efficiency of the machines, the quality of the plastic mouldings and an easy to use machines control is the focus. To ensure a constant plastics part quality the set process parameters of the injection moulding machines are kept constant by appropriate closed loop control strategies today. Assuming a constant quality of the processed plastic raw material, this strategy is effective. If it comes to a qualitative variation in the processed plastics, which often leads to a change in viscosity of the plastics melt, keeping processing parameters constant will not lead to a constant quality of the moulded parts. The deviations in the plastics viscosity have such a great influence on the moulding process that the relevant process parameters have to be adjusted manually in many cases. Often the stroke of the reciprocating screw system has to be adapted to reach a constant filling volume of the cavity and therefore avoid burr formation or short shots. In this paper an approach for adaptive process control is introduced. This control loop is able to correct the set points of specific machines parameters online within the production cycle and therefore is able to avoid changes in the produced parts quality.

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The application of Model Reference Adaptive Control (MRAC) for train dynamic braking is investigated in order to control dynamic braking forces while remaining within the allowable adhesion and coupler forces.  This control method can accurately determine the train braking distance.  One of the critical factors in Positive Train Control (PTC) is accurately estimating train braking distance under different operating conditions.  Accurate estimation of the braking distance will allow trains to be spaced closer together, with reasonable confidence that they will stop without causing a collision.  This study develops a dynamic model of a train consist based on a multibody formulation of railcars, trucks (bogies), and suspensions.   The study includes the derivation of the mathematical model and the results of a numerical study in Matlab.  A three-railcar model is used for performing a parametric study to evaluate how various elements will affect the train stopping distance from an initial speed.  Parameters that can be varied in the model include initial train speed, railcar weight, wheel-rail interface condition, and dynamic braking force.  Other parameters included in the model are aerodynamic drag forces and air brake forces. An MRAC system is developed to control the amount of current through traction motors under various wheel/rail adhesion conditions while braking.  Minimizing the braking distance of a train requires the dynamic braking forces to be maximized within the available wheel/rail adhesion.  Excessively large dynamic braking can cause wheel lockup that can damage the wheels and rail.  Excessive braking forces can also cause large buff loads at the couplers.  For DC traction motors, an MRAC system is used to control the current supplied to the traction motors.  This motor current is directly proportional to the dynamic braking force.  In addition, the MRAC system is also used to control the train speed by controlling the synchronous speed of the AC traction motors.  The goal of both control systems for DC and AC traction motors is to apply maximum available dynamic braking while avoiding wheel lockup and high coupler forces.  The results of the study indicate that the MRAC system significantly improves braking distance while maintaining better wheel/rail adhesion and coupler dynamics during braking.  Furthermore, according to this study, the braking distance can be accurately estimated when MRAC is used.  The robustness of the MRAC system with respect to different parameters is investigated, and the results show an acceptable robust response behavior. / Ph. D.

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<p>The design of vehicle components for crashworthiness is one of the most challenging problems in the automotive industry. The safety of the occupants during a crash event relies on the energy absorption capability of vehicle structures. Therefore, the body components of a vehicle are required to be lightweight and highly integrated structures. Moreover, reducing vehicle weight is another crucial design requirement since fuel economy is directly related to the mass of a vehicle. In order to address these requirements, various design concepts for vehicle bodies have been proposed using high-strength steel and different aluminum alloys. However, the price factor has always been an obstacle to completely replace regular body steels with more advanced alloys. To this end, the integration of numerical simulation and structural optimization techniques has been widely practiced addressing these requirements. Advancements in nonlinear structural design have shown the promising potential to generate innovative, safe, and lightweight vehicle structures. In addition, the implementation of structural optimization techniques has the capability to shorten the design cycle time for new models. A reduced design cycle time can provide the automakers with an opportunity to stay ahead of their competitors. During the last few decades, enormous structural optimization methods were proposed. A vast majority of these methods use mathematical programming for optimization, a method that relies on availability sensitivity analysis of objective functions. Thus, due to the necessity of sensitivity analyses, these methods remain limited to linear (or partially nonlinear) material models under static loading conditions. In other words, these methods are no able to capture all non-linearities involved in multi-body crash simulation. As an alternative solution, heuristic approaches, which do need sensitivity analyses, have been developed to address structural optimization problems for crashworthiness. The Hybrid Cellular Automaton (HCA), as a bio-inspired algorithm, is a well-practiced heuristic method that has shown promising capabilities in the structural design for vehicle components. The HCA has been continuously developed during the last two decades and designated to solve specific structural design applications. Despite all advancements, some fundamental aspects of the algorithm are still not adequately addressed in the literature. For instance, the HCA numerically implemented as a closed-loop control system. The local controllers, which dictate the design variable updates, need parameter tuning to efficiently solve different sets of problems. Previous studies suggest that one can identify some default values for the controllers. However, still, there is no well-organized strategy to tune these parameters, and proper tuning still relies on the designer’s experience.</p> <p> </p> <p> Moreover, structures with multiple materials have now become one of the perceived necessities for the automotive industry to address vehicle design requirements such as weight, safety, and cost. However, structural design methods for crashworthiness, including the HCA, are mainly applied to binary structural design problems. Furthermore, the conventional methods for the design of multi-material structures do not fully utilize the capabilities of premium materials. In other words, the development of a well-established method for the design of multi-material structures and capable of considering the cost of the materials, bonding between different materials (especially categorical materials), and manufacturing considering is still an open problem. Lastly, the HCA algorithm relies only on one hyper-parameter, the mass fraction, to synthesize structures. For a given problem, the HCA only provides one design option directed by the mass constraint. In other words, the HCA cannot tailor the dynamic response of the structure, namely, intrusion and deceleration profiles.</p> <p> </p> <p>The main objective of this dissertation is to develop new methodologies to design structures for crashworthiness applications. These methods are built upon the HCA algorithm. The first contribution is about introducing s self-tuning scheme for the controller of the algorithm. The proposed strategy eliminates the need to manually tune the controller for different problems and improve the computational performance and numerical stability. The second contribution of this dissertation is to develop a systematic approach to design multi-material crashworthy structures. To this end, the HCA algorithm is integrated with an ordered multi-material SIMP (Solid Isotropic Material with Penalization) interpolation. The proposed multi-material HCA (MMHCA) framework is a computationally efficient method since no additional design variables are introduced. The MMHCA can synthesize multi-material structures subjected to volume fraction constraints. In addition, an elemental bonding method is introduced to simulate the laser welding applied to multi-material structures. The effect of the bonding strength on the final topology designs is studied using numerical simulations. In the last step, after obtaining the multi-material designs, the HCA is implemented to remove the desired number of bonding elements and reduce the weld length.</p> <p> </p> <p>The third contribution of this dissertation is to introduce a new Cluster-based Structural Optimization method (CBSO) for the design of multi-material structures. This contribution introduces a new Cluster Validity Index with manufacturing considerations referred to as CVI_m. The proposed index can characterize the quality of the cluster in structural design considering volume fraction, size, interface as a measure of manufacturability. This multi-material structural design approach comprises three main steps: generating the conceptual design using adaptive HCA algorithm, clustering of the design domain using Multi-objective Genetic Algorithm (MOGA) optimization. In the third step, MOGA optimization is used to choose categorical materials in order to optimize the crash indicators (e.g., peak intrusion, peak contact force, load uniformity) or the cost of the raw materials. The effectiveness of the algorithm is investigated using numerical examples.</p>

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Biological phenomena found in nature can be learned and customized to obtain innovative engineering solutions. In recent years, biologists found that birds and bats use their mechanoreceptors to sense the airflow information and use this information directly to achieve their agile flight performance. Inspired by this phenomenon, an attitude control system for micro air vehicles using rich amount of airflow sensor information is proposed, designed and tested. The dissertation discusses our research findings on this topic. First, we quantified the errors between the calculated and measured lift and moment profiles using a limited number of micro pressure sensors over a straight wing. Then, we designed a robust pitching controller using 20 micro pressure sensors and tested the closed-loop performance in a simulated environment. Additionally, a straight wing was designed for the pressure sensor based pitching control with twelve pressure sensors, which was then tested in our low-speed wind tunnel. The closed-loop pitching control system can track the commanded angle of attack with a rising time around two seconds and an overshoot around 10%. Third, we extended the idea to the three-axis attitude control scenarios, where both of the pressure and shear stress information are considered in the simulation. Finally, a fault tolerant controller with a guaranteed asymptotically stability is proposed to deal with sensor failures and calculation errors. The results show that the proposed fault tolerant controller is robust, adaptive, and can guarantee an asymptotically stable performance even in case that 50% of the airflow sensors fail in flight.

No / An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.

There is a considerable scope for improving the temperature control of exothermic processes. In this thesis, a sub-optimal control strategy is developed through utilizing the dynamic, simulation tool. This scheme is built around easily obtained knowledge of the system and still retains flexibility. It can be applied to both exothermic batch and fed-batch processes. It consists of servo and regulatory modes, where a Generalized Predictive Controller (GPC) was used to provide self-tuning facilities. The methods outlined allow for limited thermal runaway whilst keeping some spare cooling capacity to ensure that operation at constraints are not violated. A special feature of the method proposed is that switching temperatures and temperature profiles can be readily found from plant trials whilst the addition rate profile Is capable of fairly straightforward computation. The work shows that It is unnecessary to demand stability for the whole of the exothermic reaction cycle, permitting a small runaway has resulted in a fast temperature response within the given safety margin. The Idea was employed for an exothermic single Irreversible reaction and also to a set of complex reactions. Both are carried out in a vessel with a heating/cooling coil. Two constraints are Imposed; (1) limited heat transfer area, and (11) a maximum allowable reaction temperature Tmax. The non-minimum phase problem can be considered as one of the difficulties in managing exothermic fed-batch process when cold reactant Is added to vessel at the maximum operating temperature. The control system coped with this within limits, a not unexpected result. In all cases, the new strategy out-performed the conventional controller and produced smoother variations in the manipulated variable. The simulation results showed that batch to batch variations and disturbances In cooling were successfully handled. GPC worked well but can be susceptible to measurement noise. / Higher Education Ministry and Scientific Research