Showing papers presented at "American Control Conference in 1990"

Stable Adaptive Teleoperation

Abstract: Telerobotics, the body of science and technology which bridges human control and purely autonomous machines, is expected to be a merging point of modern developments in robotics, control theory, cognitive science, machine design, and computer science. Besides traditional applications in space, subsea, and handling of hazardous material, many new potential uses of advanced telerobotic systems have recently been suggested or explored, such as safety applications or microsurgery. This paper is a preliminary study of what role recent results in adaptive robot control and the understanding of time-delays may play in the development of effective telerobotics systems, which would best exploit the presence of the human operator while making full use of available robot control technology and computing power. The key paradigm it explores is that of simplifying, transforming, or enhancing the remote dynamics perceived by the operator by proper use of adaptive robot control techniques and tools from passivity theory.

Sensor Placement for On-Orbit Modal Identification and Correlation of Large Space Structures

Abstract: A method is presented for the selection of a set of sensor locations from a larger candidate set for the purpose of on-orbit identification and correlation of Large Space Structures. The method ranks the candidate sensor locations according to their contribution to the linear independence of the target modal partitions. In an iterative maner, the locations which do not contribute significantly are removed. The final sensor configuration tends to maximize determinant of the corresponding Fisher Information Matrix.

Longitudinal Control of a Platoon of Vehicles

Abstract: This paper presents a systematic analysis of a longitudinal control law for a platoon of non-identical vehicles using a non-linear model to represent the vehicle dynamics of each vehicle within the platoon. The basic idea is to take full advantage of recent advances in communication and measurement and using these advances in longitudinal control of a platoon of vehicles: in particular, we assume that for i = 1,2,... vehicle i knows at all times v l and al (the velocity and acceleration of the lead vehicle) in addition to the distance between vehicle i and the preceding vehicle, i - 1.

Vehicle Lateral Control for Highway Automation

Abstract: The objectives of lateral control for highway automation are to let vehicles track the center of a lane with small error and to maintain good ride quality under different vehicle speeds, loads, wind gust disturbances, and road conditions. In this paper, the lateral feedback and feedforward controllers are designed to satisfy these objectives by utilizing the frequency-shaped linear quadratic (FSLQ) control theory. This design method allows that the ride quality be included in the performance index explicitly, and the high-frequency robustness characteristic be improved by properly choosing the weighting factors. It is shown that a controller with fixed gains does not perform satisfactorily under all conditions. Therefore, an estimator for comering stiffness of the tires is proposed to enhance performance. Simulation results show that this adaptive control approach works satisfactorily under a variety of conditions including intermittent measurement of lateral tracking error.

An Intelligent Roadway Reference System for Vehicle Lateral Guidance/Control

Abstract: An Intelligent Vehicle/Highway System (IVHS) may require a multifunctional roadway reference system. This roadway reference system should help the vehicle locate its lateral and longitudinal position, and provide other information required for vehicle control. This reference system is defined as an Intelligent Roadway Reference System (IRRS). The California Program on Advanced Technology for the Highway (PATH) is developing an IRRS, consisting of roadway reference and vehicle sensing elements which can transmit necessary information for vehicle lateral guidance/control. The reference system, passive discrete markers that may code one or more bits of information, is installed in the center of the traffic lane. An on-board sensing system acquires the information when the vehicle passes over the reference and determines the vehicle deviation and the upcoming road geometry. This sensing/reference system may also provide information for other IVHS functions.

On the Modeling and Simulation of Friction

Abstract: Two new models for "slip-stick" friction are presented. One, called the "bristle model," is an approximation designed to capture the physical phenomenon of sticking. This model is relatively inefficent numerically. The other model, called the "reset integrator model," does not capture the details of the sticking phenomenon, but is numerically efficient and exhibits behavior similar to the model proposed by Karnopp in 1985. All three of these models are preferable to the classical model which poorly represents the friction force at zero velocity. Simulation experiments show that the new models and the Karnopp model give similar results in two examples. In a closed-loop example, the classical model predicts a limit cycle which is not observed in the laboratory. The new models and the Karnopp model, on the other hand, agree with the experimental observation.

Robust Control for Automatic Steering

Abstract: Robust control problems in automatic steering are due to the wide range of velocity, mass and road conditions under which such vehicles operate. In earlier design studies and road tests for a bus it has been shown that it is possible to design a fixed gain controller such that the automatic steering operates satisfactorily over the entire range of parameters. In the present paper it is shown that the performance of such a robust automatic steering system can be considerably improved by the addition of a gyro measuring the yaw rate and feeding it back into the controller.

Nonlinear controllers for non-integrable systems: the Acrobot example

Abstract: Recent developments in the theory of geometric nonlinear control provide powerful methods for controller design for a large class of nonlinear systems. Many systems, however, do not satisfy the restrictive conditions necessary for either full state linearization [6, 5] or input-output linearization with internal stability [2]. In this paper, we present an approach to controller design based on finding a linearizable nonlinear system that well approximates the true system over a desirable region. We outline an engineering procedure for constructing the approximating nonlinear system given the true system. We demonstrate this approach by designing a nonlinear controller for a simple mechanical system patterned after a gymnast performing on a single parallel bar.

Identification of Systems with Parametric and Nonparametric Uncertainty

Abstract: A method is presented for parameter set estimation for a system wich contains both parametric and nonparametric uncertainty Prior nformation is available about both types of uncertainty, but only the parametric type is further refined from the measured data

Vehicle Modelling and Control for Automated Highway Systems

Abstract: This study evaluates the feasibility of longitudinal control of a platoon of automated vehicles. As a prerequisite to controller design, a nine state nonlinear model including an internal combustion engine, engine transmission dynamics, and tire friction characteristics has been developed. Parametric studies revealed that simple linear models cannot predict the vehicle transients. Due to this highly nonlinear nature of the vehicle, the use of a nonlinear controller was proposed. A nonlinear control strategy using throttle engine control for a platoon of two vehicles was studied using the technique of Sliding Control. In our analysis, the quantities of primary concern are position and velocity tracking errors. Simulation results demonstrate excellent tracking even in the presence of sizable modelling errors and disturbance inputs.

Automated Tracking with Target Amplitude Information

Abstract: In this paper we present a data association technique that utilizes the strength of target returns to improve tracking in a cluttered environment. The approach generalizes the Probabilistic Data Association Filter (PDAF) to include the target amplitude, a feature which is available from the detection system that provides measurements for tracking. The probabilistic modelling of target and clutter intensities is based upon collected real data. The corresponding generalized probabilistic data association is derived and improved tracking performance is demonstrated for targets with several signal to noise ratio values.

An Inverse Dynamic Method Yielding Flexible Manipulator State Trajectories

Abstract: An inverse dynamic equation for a flexible manipulator is derived in a state form By dividing the inverse system into the causal part and the anticausal part, we can calculate torque in the time domain for a certain end point trajectory, as well as trajectories of all state variabls The open loop control of the inverse dynamic method shows an excellent result in simulation For practical applications, a control strategy adapting feedback tracking control to the inverse dynamic feedforward control is illustrated, and its good experimental result is presented

Robust Control for the Tracking of Robot Motion

Abstract: In this paper, we examine the stability of a Proportional Derivative (PD) controller for the trajectory following problem of a robot manipulator. We use Lyapunov's second method to derive a uniform boundness result for the PD controller. We show that if the PD controller gains are chosen greater than a specific bound and if the Initial tracking error is zero, the velocity and position tracking errors are uniformly bounded. We then develop two additional controllers that use auxiliary control inputs along with the PD controller. Both of these controllers are shown to yield a uniform ultimate boundness property for the tracking error.

A new approach to the discretization of continuous-time controllers

Abstract: A new method for discretising continuous-time controllers is derived, using principles of controller approximation (Anderson & Yi [1]). It focuses on the closed-loop use of the discrete-time controller. The resulting approximation criterion is a measure for stability of the control system, provides an upper bound on the sampling time for which stability can be guaranteed and because it is based on continuous-time controller approximation it indicates the cost of discretization in terms of performance degradation. The discrete-time controller is obtained through minimization of this criterion, which can be performed with standard software used in H ? controller design.

Identification in H ∞ : a robustly convergent, nonlinear algorithm

Abstract: In this paper a system identification technique is developed which is compatible with current robust controller design methodologies. This technique is applicable to a broad class of stable, distributed, linear, shift-invariant systems. The information necessary for the application of this technique consists of a priori estimates on the relative stability and "steady state" gain of the unknown system together with a finite number of possibly corrupt frequency response estimates. Given this information an algorithm is specified which yields both an identified model and explicit H∞ norm error bounds. Several interesting properties of this algorithm are also discussed. Among them, the fact the algorithm is a nonlinear function of the frequency response data, and that it is robustly convergent with respect to the a priori information on relative stability and gain are singled out as characteristics which distinguish this algorithm from others currently under development by the authors.

Quantification of Uncertainty in Estimation

Abstract: Models of physical processes rarely give an exact description of the system's response. Thus an important issue is the quantification of errors in model estimation due to model inadequacy. We show that this problem can be formulated using a Bayesian approach leading to simple formulae for model uncertainty. Techniques for minimizing the amount of computation are also discussed.

Analysis and Design of Repetitive Control Systems using the Regeneration Spectrum

Abstract: The absolute and relative stability of continuous-time SISO repetitive control systems is examined here using a function of frequency termed the regeneration spectrum. The regeneration spectrum is easily computed and is related to important of the characteristic root distribution of such systems, for large values of the time delay. The regeneration spectrum is combined with other frequency domain measures of control system performance such as the sensitivity and complementary sensitivity functions to obtain improved insight into the trade-offs in repetitive control system design. The result is a more rational approach to repetitive control system design and is illustrated by an example.

An Adaptive Nonlinear Predictive Controller

Abstract: The design and implementation of a new adaptive nonlinear predictive controller is presented using a general nonlinear model and variable transformations. The resulting controller is similar in form to standard linear model predictive controllers and can be tuned analogously. This design allows the controller to be updated on-line without recalculating the controller gain matrix, which involves a matrix inversion. The new controller is compared to a PI controller and to an adaptive linear predictive controller through simulations.

Nonlinear Speed Control for Automotive Engines

Abstract: Previous research has shown that high performance engine controllers could be used in conjunction with electronic controls on automatic transmissions to improve shift quality. Due to the highly nonlinear nature of automotive engines and the need for robustness of the design, sliding control is a likely choice. However, a standard sliding derivation for an automotive engine results in an unpractical control law. An alternate method of deriving sliding controls can be used that defines one of the states to be a synthetic control. A control law is derived assuming the synthetic control as the input. A second law must then be derived to control the synthetic state. For automotive engines, the two surface controller is be much simpler and more robust than the standard control law. By taking advantage of the fact that the engine only has one output, it is possible to develop a method of coordinating the spark and throttle inputs in such a way as to take advantage of their respective strengths. Experimental implementation of the two surface control law with combined spark/throttle control has verified the performance and robustness of this algorithm.

A Property of Stick-Slip Friction Models which Promotes Limit Cycle Generation

Abstract: The effects of various "Stick-Slip" friction models on Proportional+Integral+Derivative (PID) controller stability for a simple system are investigated. Some of the more common models lack the ability to produce the limit cycles commonly observed for servos subject to stick-slip friction. A property of stick-slip models is identified which promotes limit cycle generation, and the effects are demonstrated through numerical simulations. This property is shown to be required for a stick-slip friction model to be useful in predicting limit cycle behavior in the simplest systems under PID control. Models lacking this property may lead to the inaccurate prediction of system dynamics.

Robust autopilot design using μ-synthesis

Abstract: This paper examines the applicability of H∞ and μ-synthesis control to the design of automatic flight control systems for highly maneuverable, tail-controlled missiles. The impact on performance, of the degree of conservatism inherent to each approach, is examined. It is shown that μ-synthesis provides a superior framework for the design of missile autopilots which exhibit robust performance.

Translational and Rotational Manipulability of Robotic Manipulators

Abstract: Based on the idea of separating the total manipulability measure into translational and rotational manipulability measures, several results that are useful for analyzing robotic mechanisms from the viewpoint of manipulability measure are presented

Neural Network Modeling and an Extended DMC Algorithm to Control Nonlinear Systems

Abstract: In this paper we propose a solution to the model predictive control problem for the case when the model is given by a nonlinear neural network. The solution follows the algorithm proposed by Peterson et al.[11] where the linear DMC is extended to handle nonlinear systems by updating the linear model with a `disturbance due to nonlinearities' term. Simulation results of a reaction in a CSTR are included. Results show the improvement in control of the proposed algorithm over linear DMC.

Geometric Phases, and Optimal Reconfiguration for Multibody Systems

Abstract: Relative Motion in a system of coupled rigid bodies can yield global reorientation (or phase shift). We give a formula to compute such a phase shift and interpret the same in geometric terms. The theory of connections in principal bundles provides the proper setting for questions of the type addressed in this paper. A related optimal control problem leads to singular riemannian geometry.

Generalized Nyquist Stability Criterion for Linear Time Periodic Systems

Abstract: A generalized Nyquist stability criterion for linear time periodic systems is presented. This stability criterion is similar in many respects to the generalized Nyquist stability criterion for linear time invariant multi-input multi-output systems. The criterion is graphical and is based on the eigenloci of the transfer function operator that describes the input/output behavior of the system. The criterion has the advantage that it can determine the stability of a feedback system for a range of gains, whereas Floquet theory determines stability for only a specific gain.

A Tighter Relative-Error Bound for Balanced Stochastic Truncation

Abstract: The properties of balanced stochastic truncation (BST) model reduction for stable multi-input multi-output systems are reviewed and analysed. A relative-error bound for BST is obtained as 2?n i=p+1 ? i /(1-? i ). This error bound is tighter than the relative-error bound previously derived by Green.

Extreme Point Results for Robust Stabilization of Interval Plants with First Order Compensators

Abstract: It has recently been shown that a first order compensator robustly stabilizes an interval plant family if and only if it stabilizes all of the extreme plants. That is, if the plant is described by m-th order numerator and monic n-th order denominator with coefficients lying in prescribed intervals, it is necessary and sufficient to stabilize the set of 2m+n+l extreme plants. These extreme plants are obtained by considering all possible combinations for the extreme values of the numerator and denominator coefficients. In this paper, we prove a stronger result. Namely, it is necessary and sufficient to stabilize only sixteen of the extreme plants. These sixteen plants are generated using the Kharitonov polynomials associated with the numerator and denominator. Furthermore, when additional apriori information about the compensator is specified (sign of the gain and signs and relative magnitudes of the pole and zero), then in some cases, it is necessary and sufficient to stabilize eight critical plants while in other cases, it is necessary and sufficient to stabilize twelve critical plants.