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Scalable stability conditions for heterogeneous networks via integral quadratic constraints
Decentralised and scalable conditions for robust stability of networks of heterogenous linear time-invariant (LTI) systems are derived based on integral quadratic constraints. These generalise previous works in the literature with an increased flexibility in the choice of multipliers employed. The results allow for arbitrary interconnection matrices and accommodate multi-input-multi-output systems
Initialization of the Kalman Filter without Assumptions on the Initial State
In absence of covariance data, Kalman filters are usually initialized by guessing the initial state. Making the variance of the initial state estimate large makes sure that the estimate converges quickly and that the influence of the initial guess soon will be negligible. If, however, only very few measurements are available during the estimation process and an estimate is wanted as soon as possib
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Control and Design of Computing Systems: What to Model and How
The application of feedback control to computing systems is a promising research area, but has to date been hindered by the almost unanimously perceived complexity in creating control-oriented system models. Computing systems are in fact considered very hard to describe with dynamic models allowing for simple and powerful control design tools, so that complex ones need bringing in to the detriment
Increasing the Accuracy for a Piezo-Actuated Micro Manipulator for Industrial Robots using Model-Based Nonlinear Control
We consider the problem of modeling and control of the nonlinear dynamics of a micro manipulator, utilized for machining operations in combination with industrial robots. Position control of the micro manipulator is a challenging problem because of the actuation principle, which is based on piezo-actuators with inherent nonlinear behavior. The major nonlinearities in the manipulator are identified
Bayesian Combination of Multiple Plasma Glucose Predictors
This paper presents a novel on-line approach of merging multiple different predictors of plasma glucose into a single optimized prediction. Various different predictors are merged by recursive weighting into a single prediction using regularized optimization. The approach is evaluated on 12 data sets of type I diabetes data, using three parallel predictors. The performance of the combined predicti
Augmenting L1 Adaptive Control of Piecewise Constant Type to a Fighter Aircraft. Performance and Robustness Evaluation for Rapid Maneuvering
An L1 adaptive controller of piecewise constant type has been applied to a fighter aircraft by augmenting it to a linear state-feedback controller. Angle of attack and sideslip as well as velocity vector roll rate is demanded and controlled. It is relatively easy to design a controller augmentation this way; few parameters need to be tuned. To design an L1-controller for roll/pitch/yaw-motion of a
Towards a seamless integration between process modeling descriptions at Business and Production levels - work in progress
To fulfill increasing requirements in the manufacturing sector, companies are faced with several challenges. Three major challenges have been identified regarding time-to-market, vertical feedback loops and level of automation. Grafchart, a graphical language aimed for supervisory control applications, can be used from the process-planning phase, through the implementation phase and all the way to
Stability and Worst-Case Performance Analysis of Sampled-Data Control Systems with Input and Output Jitter
When a feedback controller is implemented in a networked embedded system, the computations and communications induce delays and jitter, which may destabilize the control loop. The majority of previous work on analysis of control loops with time-varying delays has focused on output (actuation) jitter. In many embedded systems, input (sampling) jitter is also an issue. In this paper, we analyze the
Convenient Representations of Structured Systems for Model Order Reduction
In control theory there exist two convenient representations of a model, which are transfer functions and state-space matrices. However, structure in the transfer functions is not clearly seen in the state-space form, and on the other hand structure in the state-space form is not clearly seen in the transfer functions. The main reason for this is that Laplace transformation destroys most types of
Transfer Function Parameter Identification by Modified Relay Feedback
This paper proposes a transfer function parameter identification method, applicable to SISO systems of any order. Parameter identification is posed as a (non-convex) squared output error minimization problem, numerically solved utilizing Newton-Raphson iteration with back tracking line search. Focus lies on computing the cost function gradient and Hessian with respect to the parameter vector and o
The Role of Vegetation-Climate Feedbacks in Regional Earth System Dynamics
The hypothesis of this thesis is that climate models should take structural vegetation changes that operate on interannual to centennial time scales into account in order to simulate climate change in a more realistic manner. To evaluate this hypothesis, a regional Earth System Model (ESM) has been developed, validated and applied over Europe. The model has enabled the identification of a number o
Separable Lyapunov functions for monotone systems
Separable Lyapunov functions play vital roles, for example, in stability analysis of large-scale systems. A Lyapunov function is called max-separable if it can be decomposed into a maximum of functions with one-dimensional arguments. Similarly, it is called sum-separable if it is a sum of such functions. In this paper it is shown that for a monotone system on a compact state space, asymptotic stab
A Framework for Nonlinear Model-Predictive Control Using Object-Oriented Modeling with a Case Study in Power Plant Start-Up
In this paper, nonlinear model predictive control (NMPC) is applied to the start-up of a combined-cycle power plant. An object-oriented first-principle model library expressed in the high-level language Modelica has been written for the plant and used to set up the simulation and optimization models. The NMPC optimization problems are both encoded, using a high-level notation, and solved in the op
On Self-adaptive Resource Allocation through Reinforcement Learning
Autonomic computing was proposed as a promising solution to overcome the complexity of modern systems, which is causing management operations to become increasingly difficult for human beings. This work proposes the Adaptation Manager, a comprehensive framework to implement autonomic managers capable of pursuing some of the objectives of autonomic computing (i.e., self-optimization and self-healin
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An Analytic Solution to Fixed-Time Point-to-Point Trajectory Planning
We derive an analytic solution to the problem of fixed-time trajectory generation with a quadratic cost function under velocity and acceleration constraints. This problem has a wide range of applications within motion planning. The advantage of the analytic solution compared to numerical optimization of the discretized problem is the unlimited resolution of the solution and the efficiency of the c
On feasibility, stability and performance in distributed model predictive control
We present a stopping condition to the duality based distributed optimization algorithm presented in [1] when used in a distributed model predictive control (DMPC) context. To enable distributed implementation, the optimization problem has neither terminal constraints nor terminal cost that has become standard in model predictive control (MPC). The developed stopping condition guarantees a prespec
Are Beautiful traffic environments safer than Ugly traffic environments?
To link aesthetics to traffic safety, the field of environmental psychology was consid-ered. A new model of driving behaviour was developed, based on Lewin’s (1951) equation, B = f (P,E) and by employing Küllers’ (1991) model of the basic emotional process. The employed model B = f (R(Road), U(Users), A(Task), P(Person)), states that driving behaviour (B) is related to the physical environment (R)
