A state dependant switching law that obeys dwell time constraints and guarantees the stability of the system is designed. Sufficient conditions are given for the stability of the linear switched systems where the switching law is applied in presence of of polytopic type parameter uncertainty. A Lyapunov function, in quadratic form, which us non-increasing at the switching instants is assigned to each subsystem. During the dwell time, this function varies piecewise linearly in time after switching occurs. After the dwell time, the system switches only if the switching will result in a decrease in Lyapunov function. The method presented is extended to guarantee a prescribed L2-gain bound on the switched system. It is then applied to stabilization via state-feedback and to H-infinity control control design.

The reduction of infinite-horizon optimization to periodic optimization is desired, both for the simplicity of the solution obtained, and for the richer tools available in the latter field. We consider a general class of infinite-horizon optimization problems in the plane, where the set of feasible curves satisfies general conditions similar to, but weaker than, the solution set of a control system. We verify the existence of an optimal periodic solution for this class of problems. Applying this result we verify two results in this field: A known result for infinite-horizon control systems, and a new result generalizing a previous theorem for discounted infinite-horizon control problems.

A Maximum Principle for Positive Bilinear Control Systems We consider a positive bilinear control system, i.e. a control system for which the first orthant is an invariant set. We derive a maximum principle that provides a necessary condition for a control to be a "most destabilizing" control. We use this to prove stability under arbitrary switching laws for several classes of positive switched linear systems.

We develop an Ito-like stochastic calculus for a wide class of Gaussian stationary increment processes which include in particular the fractional Brownian motion. and colored Gaussian noise. We use an approach based on infinite dimensional analysis, and on Hida's white noise space theory. Using the Bochner-Minlos theorem we associate to each of these processes a probability space which is the analog of the white noise space. This allows us to study state space equations for linear systems perturbed by these processes, and solve the classical linear quadratiic optimal control problem in this setting.

A Boolean network consists of a set of Boolean variables whose state is determined by other variables in the network. Cellular automata, with two possible states per cell, are a particular case of Boolean networks. Recently, Boolean networks gained considerable interest as models for biological systems composed of elements that can be in one of two possible states. Examples include genetic regulation networks, where the ON (OFF) state corresponds to the transcribed (quiescent) state of a gene, and cellular networks where the two possible logic states may represent the open/closed state of an ion channel, basal/high activity of an enzyme, two possible conformational states of a protein, etc. Daizhan Cheng and his colleagues developed an algebraic state-space representation for Boolean control networks using the semi-tensor product of matrices. This representation proved quite useful for studying Boolean control networks in a control-theoretic framework. Using this representation, we consider a Mayer-type optimal control problem for Boolean control networks. Our main result is a necessary condition for optimality. This provides a analog of Pontrayagin's maximum principle to Boolean control networks

Many daily tasks, such as moving a cap of coffee or walking holding a suitcase, involve controlling and manipulating objects. While simple reaching movements are easily controlled, the control of objects with complex dynamics and additional degrees of freedom is much more challenging. In a simple unconstrained reaching movement it was demonstrated that subjects implicitly minimize the hand smoothness and a minimum Jerk model well account for the hand trajectory during such reaching movements. A minimum acceleration with constraints model also fits typical reaching movement trajectories with interesting prediction about the intermittent nature of the controller. Previous studies investigated the strategies used by humans to control one degree of freedom objects which was simulated by mass attached to a spring. While considering the new mechanical setup of the environment, that differ from unconstrained reaching movements, new computational models were suggested to account for subject’s and object’s trajectories. These strategies were based on solving an optimization problem that minimizes the mean-squared force change of the hand trajectory or the mean square crackle of object trajectory (crackle being the fifth time derivative of the object position). Although providing logical solution for object manipulation, both criteria could not account for subject performance in some cases. In addition when trying to explain reaching movements with multiple objects, the minimum crackle criteria is not applicable, while for simple reaching movement, the hand force change criteria can be solved only numerically by iterative scheme. Knowing the limitations of the two proposed criteria, we suggest another criterion. Instead of defining the optimization problem while focusing entirely on the hand or on the object we suggest optimizing the trajectory of the system center of mass. More specifically we propose minimizing the acceleration of the center of mass with the same constrains used for reaching movement optimization by Ben-Itzhak and Karniel (2008). We show that the minimum acceleration criteria with constrains can be expended to account for both reaching movements and object manipulation task without the need for two separate models. In the one degree of freedom object manipulation task we show that the extension of the model provide the same predictions for hand velocity profile as the other models for some values of mass and spring constant as reported in the literature. Moreover, it explains results from previous studies where other models fail to do so. This model not only fits the observed behavior, it also suggests that the brain use intermittence control and a single cost function as it controls reaching as well as manipulating complex objects. The simplicity of explaining both simple movements and movements in complex environments without altering between models opens new possibilities in research on control of robotic systems.

In this talk I will present an L2-optimal FIR reconstructor, or interpolator. The well known sinc interpolator was shown to be L2-optimal also for non-bandlimited signals. However, the slow decay rate of the sinc function prompted the use of suboptimal, fast decaying cardinal B-splines. These splines are non-causal IIR filters, which sometimes are less adequate for applications where the access to "future" information is limited. Recently, causal IIR interpolators, with a prescribe amount of preview, were derived by incorporating causality constraints into the optimization problem. Some applications may benefit from a finite memory interpolator, which, arguably, might handle better with abrupt changes in the signal and round-off errors.

I will present the closed-form expression for the optimal hold, having a finite window length and arbitrary number of preview steps. The interpolator can meet asymptotic constraints by the use of unstable weighing functions.

A quasi-analytical Describing Function (DF) for the Karnopp stick-slip friction model is presented. The proposed DF is appropriate to describe a quasi-linear stick-slip friction behavior in the frequency domain and to predict limit cycling. It is due to the additional phase lag (vs. Coulomb model) that is incorporated in this DF at low frequencies. It is shown that this additional phase lag is dictated by the static-to-dynamic friction ratio and it is generally limited by a constant value of about 32.5 deg. This fact can facilitate robust control strategy to avoid low-frequency limit cycling. The reliability of the DF was verified by the common sine-scan technique which is based on direct computation of the first Fourier coefficients. The capability of the Karnopp DF to predict limit cycles was confirmed by time-domain simulation. The proposed DF with rapid computation can be functional in real-time control algorithms.

Two nearly optimal control algorithms for the charge and discharge of a grid connected photovoltaic system with storage are proposed. These algorithms are based on Dynamic Programming (DP). The first uses a single state model which describes a capacitor-like storage device. The second uses a two states storage model which approximates a chemical battery. The control input is the storage device current. The cost function is based on the profit resulting from operating the system, taking into account the cost of replacing batteries. Two problems are discussed: 1) the continuous-time problem where the storage device current can change continuously in order to realize the best charging policy. 2) The discrete-time problem where the storage device current must be constant over pre-defined time intervals (the grid is managed via short period contracts). If the storage device has negligible resistance and is of the capacitor-like form, the solution of the first problem is shown to be Bang-Off-Bang control. The sub-optimality of the solution is due to (a) Quantization of the states for DP. The quantization can be chosen such that the difference between the solution and the actual optimum is less than a pre-defined bound. (b) The difference between the actual storage device used and the storage device model.

The

Next, we study stabilization of NCSs with communication constraints, variable
sampling interval and delay. We focus on static output feedback controllers
for linear systems. The system sensors nodes are supposed to be distributed
over a network. Data transmission over the network is subject to the *Round-Robin
scheduling protocol.* We present the closed-loop system as a switched system
with multiple delayed samples. By constructing an appropriate time-dependent
Lyapunov functional, which takes into account the switched system model and
the sawtooth delays induced by sampled-data control, we derive the exponential
stability conditions in terms of LMIs. We note that time-independent Lyapunov
functionals under Round-Robin scheduling protocol lead to the overlap of the
delay intervals and, thus, to overdesign. Polytopic uncertainties in the system
model can be easily included in the analysis. The efficiency of the method is
illustrated on the classical cart-pendulum benchmark problem.

The subject of on-line and off-line navigating an autonomous mobile robot through a maze of obstacles has been well discussed in the literature. The most common approach to solving this problem is by modeling the robot's configuration space of permitted positions and orientations, followed by a search for a free collision path in this space. The method can be readily realized using a control scheme which is base on position and distance to obstacles sensors. However, motion planning that takes into account the dynamic characteristics of the motion is relatively an unexplored area in robotics control. This paper presents a novel method for modeling the robot's dynamics, based on the position-velocity space geometry, in a way that facilitates finding a free collision path in an augmented configuration space that takes into account the dynamic constraints. Dynamic constraint such as braking distance is modeled in the augmented configuration space, called Velocity Configuration Space. The Velocity Configuration Space is an extension to the classic configuration space by adding the velocity magnitude and direction to the normal position and orientation. A method for modeling the dynamics is presented, followed by a proposal of an algorithm to find the pseudo-optimal path using arc pricing. The path search is not the main object of this paper. Rather explaining the modeling of the geometry of the dynamic constraints. Two motion-with-velocity planning problems are discussed. A simple approach, which adds the magnitude of the velocity to the robot's coordinates. And a more complex one, where both magnitude and direction are considered in a 4D space. The Robot is assumed to be flat , like a disc robot, therefore its classic configuration space is a 2D space.

The theory of distributions and theory of linear system has a long history. In particular, it is possible to define a linear system as a continuous linear operator, S->S', where S is the Schwartz space, or any other nuclear space of test functions. A disadvantage of the Schwartz and other nuclear spaces used in this theory is the fact that there is no natural continuous multiplication action in this space, and thus, for example, there is no possible to consider analytic calculus, or to have implementation theory, in some sense. Moreover, in this manner, the stochastic and deterministic cases are examined separately, as two different theories. We present a new family of topological rings, which defined by a family of positive numbers. While one specific such a family represents the Schwartz space (and its dual), another one represents Kondratiev Space of stochastic test functions (and its dual), which is used to define the White Noise space, and another one represents the Kondratiev spaces of Poissonian test functions (and its dual). Examining these families, we prove a theorem which gives sufficient and necessary condition, to the existence of multiplicative action (in an appropriate manner) on these spaces. We also give some examples and applications. -->

Recent studies suggest that certain cellular sensory systems display fold-change detection (FCD): a responsewhose entire shape, including amplitude and duration, depends only on fold-changes in input, and not on absolute changes. Thus, a step change in input from, say, level 1 to 2, gives precisely the same dynamical output as a step from level 2 to 4, since the steps have the same fold-change. We ask what is the benefit of FCD, and show that FCD is necessary and sufficient for sensory search to be independent of multiplying the inputfield by a scalar. Thus the FCD search pattern depends only on the spatial profile of the input, and not on its amplitude. Such scalar symmetry occurs in a wide range of sensory inputs, such as source strength multiplying diffusing/convecting chemical fields sensed in chemotaxis, ambient light multiplying the contrast field in vision, and protein concentrations multiplying the output in cellular signalingsystems. Furthermore, we demonstrate that FCD entails two features found across sensory systems, exact adaptation and Weber s law, but that these two features are not sufficie nt for FCD. Finally, we present a wide class of mechanisms that have FCD, including certain nonlinear feedback and feedforward loops. We find that bacterial chemotaxis displays feedback within the present class, and hence is expected to show FCD. This can explain experiments in which chemotaxis searches are insensitive to attractant source levels. This study thus suggests a connection between properties of biological sensory systems and scalar symmetry stemming from physical properties of their inputfields.

State estimation in dynamical systems with randomly switching coefficients is an important problem with a variety of applications. Some natural examples are maneuvering target tracking, and fault detection and isolation (FDI), featured in, e.g., navigation systems. The standard modeling presumes a discretely evolving mode variable that is accompanied by a continuously varying state vector. This is the well known formulation of hybrid systems. Various problems have been formulated in the past using state space modeling with a randomly evolving mode process. In problems involving uncertain observations the mode comprises the matrices of the measurement equation. In maneuvering target tracking applications the mode is usually defined to consist of the matrix parameters of the dynamics equation. We consider a more general state space representation of dynamical systems with switching coefficients. First, we assume that the process equation depends also on the latest estimate of the state vector, representing the behavior of a closed loop control system featuring a state estimator. Second, the measurement equation is allowed to depend also on the current state estimate, representing, e.g., a tracking system whose observations are acquired in a small validation window that is set around some predicted state of the target. Third, we broaden the class of problems that may be treated under the hybrid systems paradigm by allowing the mode to assume values in a continuous rather than in a discrete domain. Finally, the random (matrix) coefficients may change not only in their values, but also in their dimensions. As is well known, the MMSE optimal estimate of the state of a hybrid system cannot be practically obtained. Significant efforts have therefore been dedicated to obtaining suboptimal solutions. In this work we consider optimality within a narrower family of linear filters and derive a linear MMSE algorithm that may be conveniently implemented in a recursive form, circumventing the theoretical need for unbounded memory, as required by the unrestricted optimal filter. We also show that some well known results in estimation of hybrid systems are conveniently obtained as special cases of our linear optimal filtering algorithm, via an appropriate adjustment of the problem's parameters. To illustrate the applicability of the proposed framework we show how one can model and solve to optimality, in the linear MMSE sense, the problem of tracking a target in clutter. The derived filter is shown to attain comparable performance when compared to popular nonlinear approaches. "

Flexible structures are governed by Partial Differential Equations (PDE), hence have infinite dimension. Since the PDE is not the standard starting point for control oriented modeling, a common practice is to use a finite dimension approximation, e.g. Finite Element or truncated modal model. However there are several problems associated with that approach. First, for reasonable accuracy the approximate models need typically a very high order, which affects the order of model based control methods. Moreover, some important properties, and most of the physical insight are lost in the approximation process. A different modeling, and consequently control approach uses the accurate, infinite dimension Laplace transfer function. The building blocks of that transfer function are time delays, representing the wave motion, and low order rational expressions, representing the boundary phenomena. The approach was used to derive the absolute vibration suppression (AVS) controller, which is a dedicated, low order control law that completely suppresses the residual vibrations in the structure. While the controller was derived using mathematical considerations, its physical interpretation is eliminating reflections of the waves from the boundary. The AVS control law was designed for a system with zero initial conditions (I.C.). This is a common assumption in control applications, especially in tracking. The main goal of this presentation is to investigate the performance of the AVS controller in the presence of nonzero I.C. We suggest a method of obtaining an explicit form of the solution for a the general case of nonzero I.C.. In this solution, the response is given in terms of propagating waves and their reflections, hence its physical character is emphasized. Having the explicit solution, the effect of the wave based AVS controller on the response to I.C. can be rigorously investigated. It is shown that the vibration suppression properties of this controller can be extended to non-zero initial conditions. In some cases the motion stops completely in finite time, and in others it decays exponentially without vibration.

According to manufacturer specification, the maximal allowable payload of most open chained robotic manipulators ranges between 5 to 20 percent of the manipulator's self weight. Human beings are able to lift weights greater than their own; therefore, it is encouraged to investigate the possibility of improving weightlifting ability of industrial robotic manipulators. A good example for the human ability is the record set by Charles Rigoulot (France) in 1930. The weightlifter had lifted 115 kg, while weighing only 103 Kg. The weightlifting was performed using the One Hand Snatch technique. Utilizing this technique, the human body acts similar to the open chained robotic manipulator. Present research has examined possibilities to improve weightlifting ability of industrial robotic manipulators. Three approaches to improve manipulators weightlifting ability were examined: mimicking the Olympic weightlifter's strategy; weightlifting along the minimal energy trajectory and manipulators motors overloading. To obtain the minimal energy trajectory, three optimization approaches were tested: analytical approach (Euler-Lagrange equation, Calculus of Variations); adaptive algorithm (Genetic Algorithm) and gradient based iterative approach (Line-Search). The study was performed on a simple pendulum, i.e. a rod with electrical motor connected to its upper tip. All three approaches lead to the same solution: oscillatory trajectory with growing amplitude up to the weight lifting completion. The analytical approach is the most accurate, but there is significant problems to apply the operational envelop constraints. The adaptive algorithm enables implementation of any constraints; although solution converges slowly (computation duration is very long). Gradient based iterative approach converges the fastest and enables implementation of operational envelop constraints by adding a barrier function (Interior-Point method), although being very sensitive to the initial guess. In order to use Interior-Point method specifically for industrial manipulator, it was suggested that the initial guess would be the reversal of falling trajectory. In contrast to most of the researches dealing with manipulators weightlifting ability, the RV-M2 Movemaster (selected for the demonstration in this research) is subjected to operational envelops constraints and is unable to perform oscillating movements using its most powerful joints. Implementation of techniques for improving weightlifting capabilities for a manipulator with such limitation indicates that the suggested techniques are applicable for a wide range of industrial manipulators, especially for those that have lesser limitation. This research has shown that implementation of any of the suggested techniques substantially improves the weightlifting capabilities of the open chained robotic manipulator. Best result is achieved by combination of minimal energy trajectory with electrical motors overload.

We consider a problem encountered when trying to estimate a Gaussian random field using a distributed estimation approach based on Gaussian graphical models. Because of constraints imposed by estimation tools used in Gaussian graphical models, the a priori covariance of the random field is restricted to embed conditional independence constraints among a significant number of variables. The problem is, then: given the unrestricted a priori covariance of the random field, and the conditional independence constraints, how should one select the restricted covariance, optimally representing the (given) a priori covariance, but also satisfying the constraints? In 1972, Dempster provided a solution, optimal in the maximum likelihood sense, to the above problem. Since then, many works have used Dempster's optimal covariance, but none has addressed the issue of suitability of this covariance for estimation problems. We show that Dempster's covariance is not optimal in most minimum mean squared error (MMSE) estimation problems. We propose a method for finding the MMSE optimal covariance, and study its properties. We then illustrate the analytical results via a numerical simulation, which demonstrates the benefits of using the optimal covariance instead of Dempster's covariance.

In extremum seeking control we try to bring a nonlinear function of dynamically varying parameters to a maximum. Typical applications are controllers that seek to optimize the voltage on a battery of photovoltaic cells, or the speed of a wind turbine. In these applications the objective is to extract the maximum power from the device. When the aim is to extract maximum power from a device, them extremum seeking control is usually called maximum power point tracking (MPPT). We shall focus on the simple particular case of an (almost) static nonlinear plant. Broadly, extremum seeking control can be done by sweeping (searching the entire range of possible control inputs) or by gradient methods, each with its advantages and shortcomings. Here we concentrate on the gradient method based on adding small sinusoidal oscillations to the control variables and "listening" to the effect of these oscillations. This was introduced and analyzed in Drapper and Li (1951), Blackman (1962), Krasovskii (1963), Meerkov (1967) and others. Careful mathematical analysis has been carried out in Krstic (2000) and Krstic and Wang (2000). The literature on practical electronic implementations is huge, we mention Leyva et al (2006) and Branton et al (2010). We explain a shortcoming of the method and a very simple idea to overcome it.

This research explores techniques to control a servo-pneumatic actuator designed to drive a 300kg quadruped robot. The main advantages of pneumatic actuators are low cost, cleanliness, reliability, high power to weight ratio, a simple mechanism and natural damping as a result of compressibility of the working fluid (Air). In contrast, the high order non-linearity that characterize the pneumatic actuators' dynamics, significantly restrict industry implementations to open loop systems. The main goal of this research is to develop a control law of a servo-pneumatic actuator, which will demonstrate high accuracy and quick response, while resisting a continuously varying external load, significantly greater than those mentioned in previous papers. The system was assembled from widespread components and has a simple configuration. It consists of a longitudinal reciprocating pneumatic actuator, a proportional servo valve and a standard micro-controller. A non-linear model of the system dynamics was developed as a basic step toward the control law. The model deals with special phenomena such as compressibility of the air, chocked flow and valve dynamics, mass flow attenuation caused by tube friction and response delays due to relatively slow expansion of air pressure. The derivation of the model was based on the mass and energy conservation law, the ideal gas law and Newton's equation of motion. The control law was designed according to Lyapunov criterion which relates the energy of the system with its stability. The Integrator backstepping methodology that was chosen, defines the system's energy function in a recursive manner. The control sign (the system input), aimed to reduce system energy to the minimum, and is computed according to the state feedback that comprise the actuator's chambers' pressures and the piston's position and velocity. A comprehensive simulation has been written to display the system's behavior under the influence of external load. Closed-loop test showed excellent results stabilizing and tracking a reference input and reaching the desired position within one second. An important conclusion, obtained from simulation, is that the non-linear control law greatly reduces dependency of the system's response on external load magnitude and mechanical friction magnitude. Further research will focus on implementing the control law on a real experimental system. The control law capability will be tested by exposing the system to a continuously changing external load varying from 0 to 150kg, which resembles the robot's legs' working range.