Controlling mechanical systems with backlash—a survey

Backlash is one of the most important non-linearities that limit the performance of speed and position control in industrial, robotics, automotive, automation and other applications. The control of systems with backlash has been the subject of study since the 1940s. This survey reveals that surprisingly few control innovations have been presented since the early path breaking papers that introduced the describing function analysis of systems with backlash. Promising developments are however taking place using adaptive and non-linear control strategies.     

Wiener models of direction-dependent dynamic systems

Analysis of systems with direction-dependent dynamics is currently limited to cases in which the dynamics in the two directions of the output are first order; results for such systems have been published for both pseudo-random maximum-length binary (MLB) and inverse-repeat maximum-length binary (IR-MLB) inputs. These relatively limited analytical results make it useful to examine alternative ways of modelling such systems and in this paper, Wiener models are considered for this purpose. Methods for optimising the Wiener model parameters by matching the system and model cross-correlation functions, outputs, and discrete Fourier transforms of the outputs are considered, and the results are compared. These methods are also applied to a first-order direction-dependent system with a maximum-length ternary (MLT) input, for which no analytical results are currently available, and to a second-order system with an IR-MLB input.     

Vote counting measures for ensemble classifiers

Various measures, such as Margin and Bias/Variance, have been proposed with the aim of gaining a better understanding of why Multiple Classifier Systems (MCS) perform as well as they do. While these measures provide different perspectives for MCS analysis, it is not clear how to use them for MCS design. In this paper a different measure based on a spectral representation is proposed for two-class problems. It incorporates terms representing positive and negative correlation of pairs of training patterns with respect to class labels. Experiments employing MLP base classifiers, in which parameters are fixed but systematically varied, demonstrate the sensitivity of the proposed measure to base classifier complexity.     

Partial scan design and test sequence generation based on reduced scan shift method

This paper presents a partial scan algorithm, calledPARES (PartialscanAlgorithm based onREduced Scan shift), for designing partial scan circuits. PARES is based on the reduced scan shift that has been previously proposed for generating short test sequences for full scan circuits. In the reduced scan shift method, one determines proch FFs must be controlled and observed for each test vector. According to the results of similar analysis, PARES selects these FFs that must be controlled or observed for a large number of test vectors, as scanned FFs. Short test sequences are generated by reducing scan shift operations using a static test compaction method. To minimize the loss of fault coverage, the order of test vectors is so determined that the unscanned FFs are in the state required by the next test vector. If there are any faults undetected yet by a test sequence derived from the test vectors, then PARES uses a sequential circuit test generator to detect the faults. Experimental results for ISCAS'89 benchmark circuits are given to demonstrate the effectiveness of PARES.     

Fault-accommodation with intelligent sensors

Optimal sensor fault-accommodation is considered for faults modelled by an increase in measurement noise. This noise is taken to be bounded and its probabilistic properties unknown. It is assumed that intelligent (e.g. self-validating) instrumentation is in use and estimates of the noise bounds are available. The fault-tolerant controller is designed to optimize a noise rejection and a nominal reference tracking index and leads to a mixed norm minimization problem (l₁/l₂). We exploit known results and a particular feedback configuration to show that it is possible to optimize simultaneously without a trade-off the two performance indices. The results are applied to systems where the presence of auxiliary measurements allows for an optimal fault-accommodation strategy. Using properties of the optimal solution, we define a factorization for the optimal controller alternative to the Youla parametrization, leading to an algorithm which is optimal, transparent and efficient.     

Tracking trajectories of the cart-pendulum system

The problem of tracking a periodic trajectory of the well-known cart-pendulum system is solved. After a change of coordinates and a change of feedback, the equations of this system are nonlinear but feedforward. This property is extensively used to carry out for this system the design of uniformly asymptotically stabilizing time-varying state feedbacks by using the forwarding approach.     

A time aggregation approach to Markov decision processes

We propose a time aggregation approach for the solution of infinite horizon average cost Markov decision processes via policy iteration. In this approach, policy update is only carried out when the process visits a subset of the state space. As in state aggregation, this approach leads to a reduced state space, which may lead to a substantial reduction in computational and storage requirements, especially for problems with certain structural properties. However, in contrast to state aggregation, which generally results in an approximate model due to the loss of Markov property, time aggregation suffers no loss of accuracy, because the Markov property is preserved. Single sample path-based estimation algorithms are developed that allow the time aggregation approach to be implemented on-line for practical systems. Some numerical and simulation examples are presented to illustrate the ideas and potential computational savings.