Browsing by Subject "Real-Time"
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Item Distributed real-time control via the internet(Texas A&M University, 2004-09-30) Srivastava, AbhinavThe objective of this research is to demonstrate experimentally the feasibility of using the Internet for a Distributed Control System (DCS). An algorithm has been designed and implemented to ensure stability of the system in the presence of upper bounded time-varying delays. A single actuator magnetic ball levitation system has been used as a test bed to validate the proposed algorithm. Experiments were performed to obtain the round-trip time delay between the host PC and the client PC under varying network loads and at different times. A digital real-time lead-lag controller was implemented for the magnetic levitation system. Upper bounds for the artificial and experimental round-trip time delay that can be accommodated in the control loop for the maglev system were estimated. The artificial time delay was based on various probabilistic distributions and was generated through MATLAB. To accommodate sporadic surges in time delays that are more than these upper bounds, a timeout algorithm with sensor data prediction was developed. Experiments were performed to validate the satisfactory performance of this algorithm in the presence of the bonded sporadic excessive time delays.Item Dynamics and real-time optimal control of satellite attitude and satellite formation systems(Texas A&M University, 2006-10-30) Yan, HuiIn this dissertation the solutions of the dynamics and real-time optimal control of magnetic attitude control and formation flying systems are presented. In magnetic attitude control, magnetic actuators for the time-optimal rest-to-rest maneuver with a pseudospectral algorithm are examined. The time-optimal magnetic control is bang-bang and the optimal slew time is about 232.7 seconds. The start time occurs when the maneuver is symmetric about the maximum field strength. For real-time computations, all the tested samples converge to optimal solutions or feasible solutions. We find the average computation time is about 0.45 seconds with the warm start and 19 seconds with the cold start, which is a great potential for real-time computations. Three-axis magnetic attitude stabilization is achieved by using a pseudospectral control law via the receding horizon control for satellites in eccentric low Earth orbits. The solutions from the pseudospectral control law are in excellent agreement with those obtained from the Riccati equation, but the computation speed improves by one order of magnitude. Numerical solutions show state responses quickly tend to the region where the attitude motion is in the steady state. Approximate models are often used for the study of relative motion of formation flying satellites. A modeling error index is introduced for evaluating and comparing the accuracy of various theories of the relative motion of satellites in order to determine the effect of modeling errors on the various theories. The numerical results show the sequence of the index from high to low should be Hill's equation, non- J2, small eccentricity, Gim-Alfriend state transition matrix index, with the unit sphere approach and the Yan-Alfriend nonlinear method having the lowest index and equivalent performance. A higher order state transition matrix is developed using unit sphere approach in the mean elements space. Based on the state transition matrix analytical control laws for formation flying maintenance and reconfiguration are proposed using low-thrust and impulsive scheme. The control laws are easily derived with high accuracy. Numerical solutions show the control law works well in real-time computations.Item Energy Efficient Scheduling for Real-Time Systems(2012-02-14) Gupta, NikhilThe goal of this dissertation is to extend the state of the art in real-time scheduling algorithms to achieve energy efficiency. Currently, Pfair scheduling is one of the few scheduling frameworks which can optimally schedule a periodic real-time taskset on a multiprocessor platform. Despite the theoretical optimality, there exist large concerns about efficiency and applicability of Pfair scheduling in practical situations. This dissertation studies and proposes solutions to such efficiency and applicability concerns. This dissertation also explores temperature aware energy management in the domain of real-time scheduling. The thesis of this dissertation is: the implementation efficiency of Pfair scheduling algorithms can be improved. Further, temperature awareness of a real-time system can be improved while considering variation of task execution times to reduce energy consumption. This thesis is established through research in a number of directions. First, we explore the applicability of Dynamic Voltage and Frequency Scaling (DVFS) feature in the underlying platform, within Pfair scheduled systems. We propose techniques to reduce energy consumption in Pfair scheduling by using DVFS. Next, we explore the problem of quantum size selection in Pfair scheduled system so that runtime overheads are minimized. We also propose a hardware design for a central Pfair scheduler core in a multiprocessor system to minimized the overheads and energy consumption of Pfair scheduling. Finally, we propose a temperature aware energy management scheme for tasks with varying execution times.Item Real-Time Task Scheduling under Thermal Constraints(2010-10-12) Ahn, YoungwooAs the speed of integrated circuits increases, so does their power consumption. Most of this power is turned into heat, which must be dissipated effectively in order for the circuit to avoid thermal damage. Thermal control therefore has emerged as an important issue in design and management of circuits and systems. Dynamic speed scaling, where the input power is temporarily reduced by appropriately slowing down the circuit, is one of the major techniques to manage power so as to maintain safe temperature levels. In this study, we focus on thermally-constrained hard real-time systems, where timing guarantees must be met without exceeding safe temperature levels within the microprocessor. Speed scaling mechanisms provided in many of today?s processors provide opportunities to temporarily increase the processor speed beyond levels that would be safe over extended time periods. This dissertation addresses the problem of safely controlling the processor speed when scheduling mixed workloads with both hard-real-time periodic tasks and non-real-time, but latency-sensitive, aperiodic jobs. We first introduce the Transient Overclocking Server, which safely reduces the response time of aperiodic jobs in the presence of hard real-time periodic tasks and thermal constraints. We then propose a design-time (off-line) execution-budget allocation scheme for the application of the Transient Overclocking Server. We show that there is an optimal budget allocation which depends on the temporal character istics of the aperiodic workload. In order to provide a quantitative framework for the allocation of budget during system design, we present a queuing model and validate the model with results from a discrete-event simulator. Next, we describe an on-line thermally-aware transient overclocking method to reduce the response time of aperiodic jobs efficiently at run-time. We describe a modified Slack-Stealing algorithm to consider the thermal constraints of systems together with the deadline constraints of periodic tasks. With the thermal model and temperature data provided by embedded thermal sensors, we compute slack for aperiodic workload at run-time that satisfies both thermal and temporal constraints. We show that the proposed Thermally-Aware Slack-Stealing algorithm minimizes the response times of aperiodic jobs while guaranteeing both the thermal safety of the system and the schedulability of the real-time tasks. The two proposed speed control algorithms are examples of so-called proactive schemes, since they rely on a prediction of the thermal trajectory to control the temperature before safe levels are exceeded. In practice, the effectiveness of proactive speed control for the thermal management of a system relies on the accuracy of the thermal model that underlies the prediction of the effects of speed scaling and task execution on the temperature of the processor. Due to variances in the manufacturing of the circuit and of the environment it is to operate, an accurate thermal model can be gathered at deployment time only. The absence of power data makes a straightforward derivation of a model impossible. We, therefore, study and describe a methodology to infer efficiently the thermal model based on the monitoring of system temperatures and number of instructions used for task executions.