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Many important engineering systems accomplish their purpose using cyclic processes whose characteristics are under feedback control. Examples involving thermodynamic cycles and electromechanical energy conversion processes are particularly noteworthy. Likewise, cyclic processes are prevalent in nature and the idea of a pattern generator is widely used to rationalize mechanisms used for orchestrating movements such as those involved in locomotion and respiration. In this paper, we develop a linkage between the use of cyclic processes and the control of nonholonomic systems, emphasizing the problem of achieving stable regulation. The discussion brings to the fore characteristic phenomena that distinguish the regulation problem for such strongly nonlinear systems from the more commonly studied linear feedback regulators. Finally, we compare this approach to controlling nonholonomic systems to another approach based on the idea of an open-loop approximate inverse as discussed in the literature. Index Terms--Inverse systems, Lie brackets, nonlinear control, pattern generation, regulation, stabilization.

In this paper, we show that when it is possible to use feedback in the specification of "motor programs," the length of the descriptions of the instruction sequences for carrying out a given task can be reduced by a factor that reflects the richness of the available feedback signals. The model on which this work is based is that of a finite automaton, modified in such a way that instruction processing is akin to the way in which difference or differential equations "process" piecewise constant inputs. In terms of such "free-running" automata, we show that when feedback is available the length of the shortest description can be reduced by a factor depending on the ratio of the size of the entire state space to the size of the set of states for which feedback is locally effective. Index Terms--Automata, complexity theory, feedback, motion control.

In this paper a new class of feedback control problems is introduced. Unlike classical models, the systems considered here have communication channel constraints. As a result, the issue of coding and communication protocol becomes an integral part of the analysis. Since these systems cannot be asymptotically stabilized if the underlying dynamics are unstable, a weaker stability concept called containability is introduced. A key result connects containability with an inequality equation involving the communication data rate and the rate of change of the state. Index Terms - Asymptotic stability, containability, feedback control, Kraft inequality.

In this paper, we investigate a state estimation problem involving finite communication capacity constraints. Unlike classical estimation problems where the observation is a continuous process corrupted by additive noises, there is a constraint that the observations must be coded and transmitted over a digital communication channel with finite capacity. This problem is formulated mathematically, and some convergence properties are defined. Moreover, the concept of a finitely recursive coder-estimator sequence is introduced. A new upper bound for the average estimation error is derived for a large class of random variables. Convergence properties of some coder-estimator algorithms are analyzed. Various conditions connecting the communication data rate with the rate of change of the underlying dynamics are established for the existence of stable and asymptotically convergent coder-estimator schemes. Index Terms - Finitely recursive coder-estimator sequence, hybrid systems, prefix code, state estimation.

Nonlinear partial differential equations help simulate the scale evolution of continuous-space multiscale morphological erosions, openings, dilations and closing. The equations link the nonlinear operator that functions in the signal space and the rate of change of multiscale signal ensemble. The nonlinear operator includes the dimensionality and shape of the structuring element.

This paper addresses feedback stabilization problems for linear time-invariant control systems with saturating quantized measurements. We propose a new control design methodology, which relies on the possibility of changing the sensitivity of the quantizer while the system evolves. The equation that describes the evolution of the sensitivity with time (discrete rather than continuous in most cases) is interconnected with the given system (either continuous or discrete), resulting in a hybrid system. When applied to systems that are stabilizable by linear time-invariant feedback, this approach yields global asymptotic stability. Index Terms--Feedback stabilization, hybrid system, linear control system, quantized measurement.

In this paper we describe a class of nonlinear feedback systems perturbed by white noise for which explicit formulas for steady-state probability densities can be found. We show that this class includes what has been called monotemperaturic systems in earlier work and establish relationships with Lyapunov functions for the corresponding deterministic systems. We also treat a number of stochastic optimal control problems in the case of quantized feedback, with performance criteria formulated in terms of the steady-state probability density. Index Terms--Nonlinear feedback system, quantizer, steady-state probability density, stochastic optimal control, white noise.