7971167
Robust minimum entropy tracking control with guaranteed stability for nonlinear stochastic systems under modeling errors
In this paper, robust minimum entropy tracking control problem is considered for nonlinear stochastic systems. The controlled systems are described by nonlinear non-Gaussian difference equations with the un-modeled uncertainty and modeling error, as well as time delays. Entropy is adopted to characterize the uncertainty of the tracking error. The nonlinear multi-step-ahead predictive cost function is used and the relationship between the probability density functions of the input and the tracking error via the uncertain mapping is established. With these formulations, the cost function can be bounded as a nonlinear functional of the control input and the known bounds of the errors. Explicit design algorithms are presented for the robust suboptimal controller and further for the stabilization controllers. The Renyi's entropy has also been used to simplify the cost function. Simulations are given to demonstrate the effectiveness of the proposed control algorithm.
19553072
Direct solution of the parametric Stochastic Distribution Control problem
The Stochastic Distribution Control (SDC) problem is a generalised form of the minimum variance control problem where non-Gaussian noise distributions are encountered. The problem has been previously solved using two alternative approaches. When it is assumed that the output Probability Distribution Function (PDF) is measurable, then a parameterized controller is obtained. If on the other hand this assumption is removed (which corresponds to most practical cases), then the controller found is no longer parameterisable (i.e. it is a control action sequence). Both these approaches have thus far been solved using local Newtonian methods. In this paper a third alternative is presented which combines the desirable features of the previous two methods by finding a parameterized controller, without having to assume that the output PDF is directly measurable at the same time. In addition, global direct search algorithms are used to avoid convergence to local solutions. The approach is demonstrated on a SISO nonlinear system corrupted by non-Gaussian input noise.
6906431
On Reduced Structure Control and the 1-norm regularisation
This paper studies the controller structure selection problem for large multivariable systems. Current approaches to this problem either rely on the control engineer's experience and intuition, or solutions of very difficult and non-convex combinatorial optimisation problems. In this paper new computationally inexpensive analysis tools are presented which provide the control engineer with a complete set of optimal controller structures for a given system. Furthermore, the proposed tools can be used directly to assess the local structural sensitivity of the problem and identify key and important connections within the controller. These tools are applied here to the model of a real-life gas-turbine engine.
205433865
Model reference adaptive control of the output stochastic distributions for unknown linear stochastic systems
This paper presents a model reference based adaptive control approach for the control of the output probability density function for unknown linear time-invariant stochastic systems. Different from most existing models used in stochastic control, it is assumed here that the measured control input directly affects the distribution of the system output in probability sense. As such, the purpose of control is to make the shape of the probability density function of the system output as close as possible to a prespecified one. Using the weighted integration of the measurable output probability density functions, two adaptive on-line updating rules are developed which guarantee the global stability for theclosed loop adaptive control system under certain conditions. Ithas been shown, when there is no external disturbance, that the so-formed closed loop system also realizes the perfect tracking (i.e., the probability density function of the system output approaches a class of given distributions asymptotically)...
20772220
Descriptor observer approaches for multivariable systems with measurement noises and application in fault detection and diagnosis
In this paper, new descriptor observer design approaches are presented for multivariable systems with measurement noises. If the system matrix is stable, the proposed modified proportional and derivative observer technique allows us not only to decouple the measurement noise in any forms completely, but also obtain accurate estimations of both system states and measurement noises. If the system is detectable and the noise is bounded, it has been shown that two design parameters can provide more degrees of freedom. In this context, the derivative gain parameter is chosen to reduce the amplification of the noise, and the proportional gain is then selected to guarantee the stability of the error dynamics. For systems with both measurement output noises and input disturbances, a modified proportional, derivative and integral observer is constructed to asymptotically estimate states, input disturbances, and output noises at the same time. The applications of the proposed observers for systems with actuator and sensor faults are also discussed. Numerical examples are given to illustrate the design procedures, and the simulation results show the satisfactory tracking performance.
27905952
Robust fault diagnosis for non-Gaussian stochastic systems based on the rational square-root approximation model
The task of robust fault detection and diagnosis of stochastic distribution control (SDC) systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Convergency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demonstrate the effectiveness of the proposed algorithm.
10464764
Triangles in claw-free graphs
Abstract A graph is said to be claw-free if it does not contain an induced subgraph isomorphic to K 1,3 . Let k be a positive integer. Our main result is as follows: If G is a claw-free graph of order at least 3 k and d(x) + d ( y )⩾3 k + 1 for every pair of non-adjacent vertices x and y of G , then G contains k vertex-disjoint triangles unless either k is odd and G is one exceptional graph of order 3 k + 1, or k is 1 and G is the union of vertex-disjoint cycles of length at least 4.
9428697
A Composite Method to Extract Eye Contour
An eye contour extraction method which combines a simplied version of Active Shape Model(ASM) with a gradient method is proposed. Considering the large amount of calculations required by ASM, it is only used to extract eyelids. As iris is considered to have some more regular shape, the detection of iris is done by the simple but fast gradient method, which is improved by introducing gradient value to the weight matrix. Our detection method has been implemented in the C programming language and experimental results shows good accuracy and efficiency.
6672981
Control Flow Optimization Via Dynamic Reconvergence Prediction
This paper presents a novel microarchitecture technique for accurately predicting control flow reconvergence dynamically. A reconvergence point is the earliest dynamic instruction in the program where we can expect program paths to reconverge regardless of the outcome or target of the current branch. Thus, even if the immediate control flow after a branch is uncertain, execution following the reconvergence point is certain. This paper proposes a novel hardware re-convergence predictor which is both implementable and accurate, with a 4KB predictor achieving more than 95% accuracy for SPEC INT, and larger implementations achieving greater than 99% accuracy. The information provided from reconvergence prediction can increase the effectiveness of a range of previously proposed performance optimizations, including speculative multithreading, control independence, and squash reuse. This paper also demonstrates a new technique that takes advantage of the dynamic reconvergence prediction information in order to predict a wrong path excursion ahead of branch resolution. On average, 34% of wrong path fetches are eliminated.
10264666
Processor Performance Modeling using Symbolic Simulation
We propose a method of analytically characterizing processor performance as a function of circuit latencies. In our approach, we modify traditional simulation to use variables instead of fixed latencies for the internal functional units. The simulation engine then algebraically computes execution times, and the result is a mathematical equation which characterizes the performance space across numerous processor configurations. We discuss the computational complexity issues of this approach and show that instruction chunking and simple equation redundancy checking can make this approach feasible-we can model a large multi-dimensional design space with thousands to millions of design parameter combinations for about 10times the simulation time of a single conventional simulation run. We demonstrate our approach by exploring two different machines: a traditional MlPS-style in-order pipeline and the Intel Graphics Media Accelerator X3000.
10626
All trees contain a large induced subgraph having all degrees 1 (mod k)
We prove that, for integers n ? 2 and k ? 2, every tree with n vertices contains an induced subgraph of order at least 2?(n + 2k ? 3)/(2k ? 1)? with all degrees congruent to 1 modulo k. This extends a result of Radcliffe and Scott, and answers a question of Caro et al.