Y. Istefanopulos

On the stability of discrete-time sliding mode control systems

The stability of discrete-time sliding mode control systems is investigated and a new sliding mode condition is suggested. It is shown that the control must have upper and lower bounds. A numerical example is discussed as an illustration.

Wavelet analysis of oddball P300

The comparative wavelet analysis presented in details by Demiralp et al. (1999), Ademoglu (1995) and by Başar et al. (2001) will be now applied to oddball P300 results (see Başar-Eroglu et al., 2001). The results obtained basically confirm those obtained by using adaptive digital filtering: The delta response dominates the P300 potential while the theta response is prolonged in a second late window.

A new variable structure PID-controller design for robot manipulators

In this brief, a new variable structure proportional-integral-derivative (PID) controller design approach is considered for the tracking stabilization of robot motion. The work corroborates the utility of a certain PID sliding mode controller with PID sliding surface for tracking control of a robotic manipulator. Different from the general approach, the conventional equivalent control term is not used in this controller because that needs to use the matching conditions and exact full robot dynamics knowledge, which involves unavailable parameter uncertainties. Though the sliding surface includes also the integral error term, which makes the robot tracking control problem complicated, the existence of a sliding mode and gain selection guideline are clearly investigated. Moreover, different from uniformly ultimately boundedness, the global asymptotic stability of the robot system with proposed controller is analyzed. The sliding and global stability conditions are formulated in terms of Lyapunov full quadratic form and upper and lower matrix norm inequalities. Reduced design is also discussed. The proposed control algorithm is applied to a two-link direct drive robot arm through simulations. The simulation results indicate that the control performance of the robot system is satisfactory. The chattering phenomenon is handled by the use of a saturation function replaced with a pure signum function in the control law. The saturation function results in a smooth transient performance. The proposed approach is compared with the existing alternative sliding mode controllers for robot manipulators in terms of advantages and control performances. A comparative analysis with a plenty of simulation results soundly confirmed that the performance of developed variable structure PID controller is better under than those of both classical PID controller and an existing variable structure controller with PID-sliding surface.

Design of an adaptive controller for submersibles via multimodel gain scheduling

An adaptive autopilot for submarines via gain scheduling is introduced. The procedure used in the design is based on the stochastic controller and observer techniques. The autopilot is designed to operate in various sea conditions. Steady-state estimator gains corresponding to different wave heights are calculated and utilized in estimating the real wave height by switching the gain sets. Since large perturbations require large hydroplane angles which cannot be realizable due to their physical limits, different controller gain sets are employed for the appropriate operation of the hydroplanes. The presented controller is not confined to a particular sea state, and possesses robustness against possible changes in the external environment, exploiting the multimodel representation of the sea state.

Analysis of event-related potentials (ERP) by damped sinusoids

Abstract. Several researchers propose that event-related potentials (ERPs) can be explained by a superposition of transient oscillations at certain frequency bands in response to external or internal events. The transient nature of the ERP is more suitable to be modelled as a sum of damped sinusoids. These damped sinusoids can be completely characterized by four sets of parameters, namely the amplitude, the damping coefficient, the phase and the frequency. The Prony method is used to estimate these parameters. In this study, the long-latency auditory-evoked potentials (AEP) and the auditory oddball responses (P300) of 10 healthy subjects are analysed by this method. It is shown that the original waveforms can be reconstructed by summing a small number of damped sinusoids. This allows for a parsimonious representation of the ERPs. Furthermore, the method shows that the oddball target responses contain higher amplitude, slower delta and slower damped theta components than those of the AEPs. With this technique, we show that the differentiation of sensory and cognitive potentials are not inherent in their overall frequency content but in their frequency components at certain bands.

Attentional sequence-based recognition: Markovian and evidential reasoning

Biological vision systems explore their environment via allocating their visual resources to only the interesting parts of a scene. This is achieved by a selective attention mechanism that controls eye movements. The data thus generated is a sequence of subimages of different locations and thus a sequence of features extracted from those images - referred to as attentional sequence. In higher level visual processing leading to scene cognition, it is hypothesized that the information contained in attentional sequences are combined and utilized by special mechanisms - although still poorly understood. However, developing models of such mechanisms prove out to be crucial - if we are to understand and mimic this behavior in robotic systems. In this paper, we consider the recognition problem and present two approaches to using attentional sequences for recognition: Markovian and evidential reasoning. Experimental results with our mobile robot APES reveal that simple shapes can be modeled and recognized by these methods - using as few as ten fixations and very simple features. For more complex scenes, longer attentional sequences or more sophisticated features may be required for cognition.

APES: Attentively Perceiving Robot

Robot vision systems—inspired by human-like vision—are required to employ mechanisms similar to those that have proven to be crucial in human visual performance. One of these mechanisms is attentive perception. Findings from vision science research suggest that attentive perception requires a multitude of properties: A retina with fovea-periphery distinction, an attention mechanism that can be manipulated both mechanically and internally, an extensive set of visual primitives that enable different representation modes, an integration mechanism that can infer the appropriate visual information in spite of eye, head, body and target motion, and finally memory for guiding eye movements and modeling the environment. In this paper we present an attentively “perceiving” robot called APES. The novelty of this system stems from the fact that it incorporates all of these properties simultaneously. As is explained, original approaches have to be taken to realize each of the properties so that they can be integrated together in an attentive perception framework.

Statistical analysis of Pisarenko type tone frequency estimator

Abstract In this paper we investigate a tone frequency estimation method based on the Pisarenko technique. The frequency estimate is obtained in terms of the first and second autocorrelation coefficients of the input samples, in the case of a single tone in white Gaussian noise. Statistical analysis of this frequency estimate has been carried out. New expressions related to the expected value and the variance of the estimate are derived. It is shown that the Pisarenko method breaks down for low signal to noise ratio (SNR) values and achieves the Cramer Rao Lower Bound (CRLB) for high SNR values and that the estimator is biased. The performance of this method is shown to be sensitive to the frequency of the sinusoid with best results being obtained for ω1 = π/4 and 3π/4.

Nonlinear control of two-link flexible arm with adaptive internal model

Developing nonlinear adaptive and robust controller for two-link flexible robot arm is the main objective of this research. The dynamic state feedback controller is used to achieve robust regulation of the rigid modes as well as suppression of elastic vibrations. Modelling of highly nonlinear multi-link flexible arms is subject to uncertainties such that imperfect modelling, backlash, payload changes and external disturbances. Therefore, adaptive control of multi-link flexible arms is a challenging problem. In this paper, the internal model approach is adaptively tuned up for unknown disturbances, parallel with the robust stabilizer.

A new memory model for selective perception systems

Selectively perceiving systems explore their environment by fixating on and analyzing potentially interesting parts of a scene in a continual manner. During this process, a sequence of snapshots, each containing visual data with distinct spatio-temporal properties, is collected. We propose a visual memory model that enables the integration of this spatio-temporal data. This memory model, referred to as the bubble model, uses a spherical surface attached to the viewpoint in order to store and manipulate information collected during selective perception process. The bubble surface can be deformed by a number of control points to represent visual information obtained from different fixation points. Bubbles are indexed by the viewpoint vectors so that they can be recalled or updated as necessary. Different bubbles attached to the same viewpoint can store multiple simple and complex features. Using time stamps and parametric surface representations the model can also be used to extract changes and similarities in the environment.