Roberto Horowitz

Bio: Roberto Horowitz is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Control theory & Adaptive control. The author has an hindex of 56, co-authored 436 publications receiving 12030 citations. Previous affiliations of Roberto Horowitz include University of California & IBM.

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...

Control design of an automated highway system

Abstract: Describes the design of an automated highway system (AHS) developed over the past ten years in the California PATH program. The AHS is a large, complex system, in which vehicles are automatically controlled. The design and implementation of the AHS required advances in actuator and sensor technologies, as well as the design, analysis, simulation, and testing of large-scale, hierarchical hybrid control systems. The paper focuses on the multilayer AHS control architecture and some questions of implementation. It discusses in detail the design and safety verification of the on-board vehicle control system and the design of the link-layer traffic-flow controller.

Optimal freeway ramp metering using the asymmetric cell transmission model

Abstract: The onramp metering control problem is posed using a cell transmission-like model called the asymmetric cell transmission model (ACTM). The problem formulation captures both freeflow and congested conditions, and includes upper bounds on the metering rates and on the onramp queue lengths. It is shown that a near-global solution to the resulting nonlinear optimization problem can be found by solving a single linear program, whenever certain conditions are met. The most restrictive of these conditions requires the congestion on the mainline not to back up onto the onramps whenever optimal metering is used. The technique is tested numerically using data from a severely congested stretch of freeway in southern California. Simulation results predict a 17.3% reduction in delay when queue constraints are enforced.

A new adaptive learning rule

Abstract: A method is presented for nonlinear function identification and application to learning control. The control objective is to identify and compensate for a nonlinear disturbance function. The nonlinear disturbance function is represented as an integral of a predefined kernel function multiplied by an unknown influence function. Sufficient conditions for the existence of such a representation are provided. Similarly, the nonlinear function estimate is generated by an integral of the predefined kernel multiplied by an influence function estimate. Using the time history of the plant, the learning rule indirectly estimates the unknown function by updating the influence function estimate. It is shown that the estimate function converges to the actual disturbance asymptotically. Consequently, the controller achieves the disturbance cancellation asymptotically. The method is extended to repetitive control applications. It is applied to the control of robot manipulators. Simulation and actual real-time implementation results using the Berkeley/NSK robot arm show that the proposed learning algorithm is more robust and converges at a faster rate than conventional repetitive controllers. >

Traffic density estimation with the cell transmission model

Abstract: A macroscopic traffic flow model, called the switching-mode model (SMM), has been derived from the cell transmission model (CTM) and then applied to the estimation of traffic densities at unmonitored locations along a highway. The SMM is a hybrid system that switches among different sets if linear difference equations, or modes, depending on the mainline boundary data and the congestion status of the cells in a highway section. Using standard linear systems techniques, the observability and controllability properties of the SMM modes have been determined. Both the SMM and a density-based version of the CTM have been simulated over a section of I-210 West in Southern California, using several days of loop detector data collected during the morning rush-hour period. The simulation results show that the SMM and CTM produce density estimates that are both similar to one another and in good agreement with measured densities on I-210. The mean percentage error averaged over all the test days was approximately 13% for both models.

Linear and nonlinear waves, by G. B. Whitham. Pp.636. £50. 1999. ISBN 0 471 35942 4 (Wiley).

Abstract: to be done in this area. Face recognition is a problem that scarcely clamoured for attention before the computer age but, having surfaced, has involved a wide range of techniques and has attracted the attention of some fine minds (David Mumford was a Fields Medallist in 1974). This singular application of mathematical modelling to a messy applied problem of obvious utility and importance but with no unique solution is a pretty one to share with students: perhaps, returning to the source of our opening quotation, we may invert Duncan's earlier observation, 'There is an art to find the mind's construction in the face!'.

A survey of iterative learning control

Abstract: This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

Gaussian networks for direct adaptive control

Abstract: A direct adaptive tracking control architecture is proposed and evaluated for a class of continuous-time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture uses a network of Gaussian radial basis functions to adaptively compensate for the plant nonlinearities. Under mild assumptions about the degree of smoothness exhibit by the nonlinear functions, the algorithm is proven to be globally stable, with tracking errors converging to a neighborhood of zero. A constructive procedure is detailed, which directly translates the assumed smoothness properties of the nonlinearities involved into a specification of the network required to represent the plant to a chosen degree of accuracy. A stable weight adjustment mechanism is determined using Lyapunov theory. The network construction and performance of the resulting controller are illustrated through simulations with example systems. >

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.