Showing papers on "Robustness (computer science) published in 1990"

Neuromorphic electronic systems

Abstract: It is shown that for many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those using digital methods. This advantage can be attributed principally to the use of elementary physical phenomena as computational primitives, and to the representation of information by the relative values of analog signals rather than by the absolute values of digital signals. This approach requires adaptive techniques to mitigate the effects of component differences. This kind of adaptation leads naturally to systems that learn about their environment. Large-scale adaptive analog systems are more robust to component degradation and failure than are more conventional systems, and they use far less power. For this reason, adaptive analog technology can be expected to utilize the full potential of wafer-scale silicon fabrication. >

Analysis of gain scheduled control for nonlinear plants

Abstract: Gain scheduling has proven to be a successful design methodology in many engineering applications. In the absence of a sound theoretical analysis, these designs come with no guarantees of the robustness, performance, or even nominal stability of the overall gain-scheduled design. An analysis is presented for two types of nonlinear gain-scheduled control systems: (1) scheduling on a reference trajectory, and (2) scheduling on the plant output. Conditions which guarantee stability, robustness, and performance properties of the global gain schedule designs are given. These conditions confirm and formalize popular notions regarding gain scheduled designs, such as that the scheduling variable should vary slowly, and capture the plant's nonlinearities. >

Optimal robustness in the gap metric

Abstract: The application of the gap metric to robust stabilization of feedback systems is considered. In particular, a solution to the problem of robustness optimization in the gap metric is presented. The problem of robust stabilization under simultaneous plant-controller perturbations is addressed, and the least amount of combined plant-controller uncertainty, measured by the gap metric, that can cause instability of a nominally stable feedback system is determined. Included are a detailed summary of the main properties of the gap metric and the introduction of a dual metric called the T-gap metric. A key contribution of this study is to show that the problem of robustness optimization in the gap metric is equivalent to robustness optimization for normalized coprime factor perturbations. This settles the question as to whether maximizing allowable coprime factor uncertainty corresponds to tolerating the largest ball of uncertainty in a well-defined metric. >

Stability and robustness analysis of a class of adaptive controllers for robotic manipulators

Abstract: The stability and robustness properties of the adaptive control scheme proposed by Sadegh and Horowitz (1987) are stud ied. The properties include the global exponential stability and Lpinput/output stability of the nonadaptive (i.e., fixed- parameter) control system and the global asymptotic stability of the adaptive control scheme. Sufficient conditions for the convergence of the estimated parameters to their true values are also given. A computationally efficient adaptation scheme that is a modified version of the original scheme is proposed. The modified scheme utilizes the desired trajectory outputs, which can be calculated a priori, instead of the actual joint outputs in the parameter adaptation algorithm and the non linearity compensation controller. Sufficient conditions for guaranteeing all the stability properties of the original scheme in the modified scheme are also explicitly derived. A com puter simulation study of the performance of both schemes in the presence of noise disturbances is cond...

A study of vector quantization for noisy channels

Abstract: Several issues related to vector quantization for noisy channels are discussed. An algorithm based on simulated annealing is developed for assigning binary codewords to the vector quantizer code-vectors. It is shown that this algorithm could result in dramatic performance improvements as compared to randomly selected codewords. A modification of the simulated annealing algorithm for binary codeword assignment is developed for the case where the bits in the codeword are subjected to unequal error probabilities (resulting from unequal levels of error protection). An algorithm for the design of an optimal vector quantizer for a noisy channel is briefly discussed, and its robustness under channel mismatch conditions is studied. Numerical results for a stationary first-order Gauss-Markov source and a binary symmetric channel are provided. It is concluded that the channel-optimized vector quantizer design algorithm, if used carefully, can result in a fairly robust system with no additional delay. The case in which the communication channel is nonstationary (as in mobile radio channels) is studied, and some preliminary ideas for quantizer design are presented. >

Environmental robustness in automatic speech recognition

Abstract: Initial efforts to make Sphinx, a continuous-speech speaker-independent recognition system, robust to changes in the environment are reported. To deal with differences in noise level and spectral tilt between close-talking and desk-top microphones, two novel methods based on additive corrections in the cepstral domain are proposed. In the first algorithm, the additive correction depends on the instantaneous SNR of the signal. In the second technique, expectation-maximization techniques are used to best match the cepstral vectors of the input utterances to the ensemble of codebook entries representing a standard acoustical ambience. Use of the algorithms dramatically improves recognition accuracy when the system is tested on a microphone other than the one on which it was trained. >

A theoretical investigation into the performance of the Hopfield model

Abstract: An analysis is made of the behavior of the Hopfield model as a content-addressable memory (CAM) and as a method of solving the traveling salesman problem (TSP). The analysis is based on the geometry of the subspace set up by the degenerate eigenvalues of the connection matrix. The dynamic equation is shown to be equivalent to a projection of the input vector onto this subspace. In the case of content-addressable memory, it is shown that spurious fixed points can occur at any corner of the hypercube that is on or near the subspace spanned by the memory vectors. Analysed is why the network can frequently converge to an invalid solution when applied to the traveling salesman problem energy function. With these expressions, the network can be made robust and can reliably solve the traveling salesman problem with tour sizes of 50 cities or more. >

Learning control theory for robotic motion

Abstract: A new concept of learning control for the improvement of robot motions is proposed, which can be referred to a mathematical modelling of learning and generation of motor programmes in the central nervous system. It differs from conventional classical and modern control techniques. It stands for the repeatability of operating a given robotic system and the possibility of improving the command input on the basis of actual measurement data acquired at the previous operation. Hence adequate conditions on the repeatability and invariance of the system dynamics are assumed, but no precise description of the dynamics is required for construction of the learning algorithms. Two types of iterative learning algorithm are proposed: one uses a PD-type (proportional and differential) update of input commands and the other a PI-type (proportional and integral) update where velocity signals are regarded as outputs. It is shown that in both types a better performance is realized at every attempt of operation, provided a desired motion is given a priori and the actual motion (velocity signals) can be measured at every operation. Further, the robustness of such learning control algorithms with respect to the existence of perturbed errors of initialization of the robot, disturbances and measurement noise during operation is analysed in detail. It is shown that in PD-type learning laws such errors are neither amplified nor aggregated in later consecutive trials of operation. In the case of PI-type learning laws it is shown that such a robustness property is assured if a forgetting factor is adequately introduced into the repetitive learning law.

A neural net controller for underwater robotic vehicles

Abstract: Results of a study on the application of neural networks to the control system of underwater robotic vehicles (URVs) are presented. The robustness of the control system with respect to nonlinear dynamic behavior and parameter uncertainties is investigated by computer simulation. The results show the feasibility of using unpredictable changes in the dynamics of the vehicle and its environment. >

Anytime synthetic projection: maximizing the probability of goal satisfaction

Abstract: This paper presents a projection algorithm for incremental control rule synthesis. The algorithm synthesizes an initial set of goal-achieving control rules using a combination of situation probability and estimated remaining work as a search heuristic. This set of control rules has a certain probability of satisfying the given goal. The probability is incrementally increased by synthesizing additional control rules to handle "error" situations the execution system is likely to encounter when following the initial control rules. By using situation probabilities the algorithm achieves a computationally effective balance between the limited robustness of triangle tables and the absolute robustness of universal plans.

Toward a fully decentralized architecture for multi-sensor data fusion

Abstract: A fully decentralized architecture is presented for data fusion problems. This architecture takes the form of a network of sensor nodes, each with its own processing facility, which together do not require any central processor or any central communication facility. In this architecture, computation is performed locally and communication occurs between any two nodes. Such an architecture has many desirable properties, including robustness to sensors failure and flexibility to the addition or loss of one or more sensors. This architecture is appropriate for the class of extended Kalman filter (EKF)-based geometric data fusion problems. The starting point for this architecture is an algorithm which allows the complete decentralization of the multisensor EKF equations among a number of sensing nodes. This algorithm is described, and it is shown how it can be applied to a number of different data-fusion problems. An application of this algorithm to the problem of multicamera, real-time tracking of objects and people moving through a room is described. >

Robustness of a minimal controller synthesis algorithm

Abstract: Recently published empirical results (Stoten and Benchoubane 1990) show that a minimal controller synthesis (MCS) adaptive scheme is an extremely effective control strategy. The MCS algorithm appeared to be robust in the face of totally unknown plant dynamics, external disturbances and parameter variations within the plant. Here, an analysis is given of MCS robustness for single-input/single-output and multivariable plants of certain structure.

Application of sliding-mode control to air-air interception problem

Abstract: A sliding-mode control (SMC) law is applied to a nonlinear system representing an air-air missile-target interception process. Promising results are obtained for a simple switching surface based on proportional navigation (PN). Partial robustness of the controller is demonstrated, i.e. the control is robust only with respect to uncertainties present in the control dynamics. This is illustrated by a breakdown in the control actuator for which SMC was found to be superior to PN. >

Text independent speaker identification using automatic acoustic segmentation

Abstract: An acoustic-class-dependent technique for text-independent speaker identification on very short utterances is described. The technique is based on maximum-likelihood estimation of a Gaussian mixture model representation of speaker identity. Gaussian mixtures are noted for their robustness as a parametric model and their ability to form smooth estimates of rather arbitrary underlying densities. Speaker model parameters are estimated using a special case of the iterative expectation-maximization (EM) algorithm, and a number of techniques are investigated for improving model robustness. The system is evaluated using a 12 reference speaker population from a conversational speech database. It achieves 80% average text-independent speaker identification performance for a 1-s test utterance length. >

Globally stable robust tracking of nonlinear systems using variable structure control and with an application to a robotic manipulator

Abstract: The globally stable robust output tracking for a class of nonlinear systems is considered. Based only on the knowledge of the bounds on the uncertainties, a variable structure control (VSC) law is developed under the structure matching assumption. It is shown that the outputs of the closed-loop system asymptotically track given output trajectories despite the uncertainties while maintaining the boundedness of all signals inside the loop. All signals inside the loop are shown to be bounded for all time. To illustrate the efficiency of the controller, the approach is applied to the case of a two degree-of-freedom (DOF) robotic manipulator with variable payload. Numerical simulation results are also provided. >

Robust stabilization with positive real uncertainty: beyond the small gain theorem

Abstract: The properties of positive real transfer functions are used to guarantee robust stability in the presence of positive real (but otherwise unknown) plant uncertainty. These results are then used for controller synthesis to address the problem of robust stabilization in the presence of positive real uncertainty. One of the principal motivations for these results is to utilize phase information in guaranteeing robust stability. In this sense these results go beyond the usual limitations of the small gain theorem and quadratic Lyapunov functions, which may be conservative when phase information is available. The results of this study are based upon a Riccati equation formulation of the positive real lemma and thus resemble certain Riccati-based approaches to bounded real (H/sub infinity /) control. >

Computation of mu with real and complex uncertainties

Abstract: The robustness analysis of system performance is one of the key issues in control theory, and one approach is to reduce this problem to that of computing the structured singular value, mu . When real parametric uncertainty is included, then mu must be computed with respect to a block structure containing both real and complex uncertainties. It is shown that mu is equivalent to a real eigenvalue maximization problem, and a power algorithm is developed to solve this problem. The algorithm has the property that mu is (almost) always an equilibrium point of the algorithm, and that whenever the algorithm converges a lower bound for mu results. This scheme has been found to have fairly good convergence properties. Each iteration of the scheme is very cheap, requiring only such operations as matrix-vector multiplications and vector inner products, and the method is sufficiently general to handle arbitrary numbers of repeated real scalars, repeated complex scalars, and full complex blocks. >

On the design of path delay fault testable combinational circuits

Abstract: A theoretical framework for investigating the design for the path-delay-fault testability problem is provided. Necessary and sufficient conditions for the existence of general robust tests in a multioutput, multilevel circuit are given. The conditions for the existence of a more restricted class of robust tests are derived from those for general robust tests. A design procedure is given for the synthesis of multioutput, multilevel combinational logic circuits in which all path delay faults are robustly detectable. A powerful factorization method, that of extended factorization, was exploited for this purpose. >

Comparison of fuzzy and neural truck backer-upper control systems

Abstract: A simple fuzzy control system and a simple neural control system for backing up a truck in an open parking lot are developed. The choice of control problem was prompted by the recent, successful, neural network truck backer-upper simulation of Nguyen and Widrow (Proc. Int. Joint Conference on Neural Networks, vol.2, p.357-363, June, 1989). The authors were unable to exactly replicate the neural network they used. Instead the authors built the best backpropagation network they could with essentially the same kinematics and compared it to the best fuzzy controller they could develop. The fuzzy controller compares favorably with the neural controller in terms of black-box computation load, smoothness of truck trajectories, and robustness. Robustness of the fuzzy controller is studied by deliberately adding confusing FAM (fuzzy associative memory), rules-sabotage rules-to the system and by randomly removing different subsets of FAM rules. Robustness of the neural controller is studied by randomly removing different portions of the training data. It is concluded that fuzzy control shows optimal truck backing-up performance

Empirical studies of an MRAC algorithm with minimal controller synthesis

Abstract: An adaptive control algorithm based upon a well-known model reference adaptive control (MRAC) strategy is described. The main feature of this new algorithm is that minimal synthesis is required to implement the strategy—hence the appellation minimal controller synthesis (MCS). Specifically, no plant model is required (apart from a knowledge of the state dimension) and no controller gains have to be calculated. We also present two implementation studies and one simulation study of the MCS strategy to demonstrate the robustness and excellence of control that can be achieved. Robustness proofs are left to sequels of this paper.

Model reference robust adaptive control without a priori knowledge of the high frequency gain

Abstract: A novel model-reference robust adaptive controller that does not require a priori knowledge of the high-frequency-gain sign is proposed. The scheme is applicable to minimum-phase plants of arbitrary relative degree and ensures that in the absence of unmodeled dynamics the tracking error converges to zero and all the signals remain bounded. The control law uses a particular projected parameter vector instead of the current estimates to avoid division by zero. >