CSI technology validation on an LSS ground experiment facility

The test bed developed at JPL for experimental evaluation of new technologies for the control of large flexible space structures is described. The experiment consists of a flexible spacecraft dynamic simulator, sensors, actuators, a microcomputer, and an advanced programming environment. The test bed has been operational for over a year, and thus far nine experiments were completed or are currently in progress. Several of these experiments were reported at the 1987 CSI conference, and several recent ones are documented in this paper, including high order adaptive control, non-parametric system identification, and mu-synthesis robust control. An aggressive program of experiments is planned for the forseeable future

Adaptive Discrete Second Order Sliding Mode Control with Application to Nonlinear Automotive Systems

Sliding mode control (SMC) is a robust and computationally efficient model-based controller design technique for highly nonlinear systems, in the presence of model and external uncertainties. However, the implementation of the conventional continuous-time SMC on digital computers is limited, due to the imprecisions caused by data sampling and quantization, and the chattering phenomena, which results in high frequency oscillations. One effective solution to minimize the effects of data sampling and quantization imprecisions is the use of higher order sliding modes. To this end, in this paper, a new formulation of an adaptive second order discrete sliding mode control (DSMC) is presented for a general class of multi-input multi-output (MIMO) uncertain nonlinear systems. Based on a Lyapunov stability argument and by invoking the new Invariance Principle, not only the asymptotic stability of the controller is guaranteed, but also the adaptation law is derived to remove the uncertainties within the nonlinear plant dynamics. The proposed adaptive tracking controller is designed and tested in real-time for a highly nonlinear control problem in spark ignition combustion engine during transient operating conditions. The simulation and real-time processor-in-the-loop (PIL) test results show that the second order single-input single-output (SISO) DSMC can improve the tracking performances up to 90%, compared to a first order SISO DSMC under sampling and quantization imprecisions, in the presence of modeling uncertainties. Moreover, it is observed that by converting the engine SISO controllers to a MIMO structure, the overall controller performance can be enhanced by 25%, compared to the SISO second order DSMC, because of the dynamics coupling consideration within the MIMO DSMC formulation.Comment: 12 pages, 7 figures, 1 tabl

Combined MIMO adaptive and decentralized controllers for broadband active noise and vibration control

Recent implementations of multiple-input multiple-output adaptive controllers for reduction of broadband noise and vibrations provide considerably improved performance over traditional adaptive algorithms. The most significant performance improvements are in terms of speed of convergence, the \ud amount of reduction, and stability of the algorithm. Nevertheless, if the error in the model of the relevant transfer functions becomes too large then the system may become unstable or lose performance. On-line adaptation of the model is possible in principle but, for rapid changes in the model, necessitates \ud a large amount of additional noise to be injected in the system. It has been known for decades that a combination of high-authority control (HAC) and low-authority control (LAC) could lead to improvements with respect to parametric uncertainties and unmodeled dynamics. In this paper a full digital implementation of such a control system is presented in which the HAC (adaptive MIMO control) is implemented on a CPU and in which the LAC (decentralized control) is implemented on a high-speed Field Programmable Gate Array. Experimental results are given in which it is demonstrated that the HAC/LAC combination leads to performance advantages in terms of stabilization under parametric uncertainties and reduction of the error signal

Integrated system identification/control design with frequency weightings.

by Ka-lun Tung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 168-[175]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Control with Uncertainties --- p.1Chapter 1.1.1 --- Adaptive Control --- p.2Chapter 1.1.2 --- H∞ Robust Control --- p.3Chapter 1.2 --- A Unified Framework: Adaptive Robust Control --- p.4Chapter 1.3 --- System Identification for Robust Control --- p.6Chapter 1.3.1 --- Choice of input signal --- p.7Chapter 1.4 --- Objectives and Contributions --- p.8Chapter 1.5 --- Thesis Outline --- p.9Chapter 2 --- Background on Robust Control --- p.11Chapter 2.1 --- Notation and Terminology --- p.12Chapter 2.1.1 --- Notation --- p.12Chapter 2.1.2 --- Linear System Terminology --- p.13Chapter 2.1.3 --- Norms --- p.15Chapter 2.1.4 --- More Terminology: A Standard Feedback Configuration --- p.17Chapter 2.2 --- Norms and Power for Signals and Systems --- p.18Chapter 2.3 --- Plant Uncertainty Model --- p.20Chapter 2.3.1 --- Multiplicative Unstructured Uncertainty --- p.21Chapter 2.3.2 --- Additive Unstructured Uncertainty --- p.22Chapter 2.3.3 --- Structured Uncertainty --- p.23Chapter 2.4 --- Motivation for H∞ Control Design --- p.23Chapter 2.4.1 --- Robust stabilization: Multiplicative Uncertainty and Weight- ing function W3 --- p.24Chapter 2.4.2 --- Robust stabilization: Additive Uncertainty and Weighting function W2 --- p.25Chapter 2.4.3 --- Tracking Problem --- p.26Chapter 2.4.4 --- Disturbance Rejection (or Sensitivity Minimization) --- p.27Chapter 2.5 --- The Robust Control Problem Statement --- p.28Chapter 2.5.1 --- The Mixed-Sensitivity Approach --- p.29Chapter 2.6 --- An Augmented Generalized Plant --- p.30Chapter 2.6.1 --- The Augmented Plant --- p.30Chapter 2.6.2 --- Adaptation of Augmented Plant to Sensitivity Minimiza- tion Problem --- p.32Chapter 2.6.3 --- Adaptation of Augmented Plant to Mixed-Sensitivity Prob- lem --- p.33Chapter 2.7 --- Using MATLAB Robust Control Toolbox --- p.34Chapter 3 --- Statistical Plant Set Estimation for Robust Control --- p.36Chapter 3.1 --- An Overview --- p.37Chapter 3.2 --- The Schroeder-phased Input Design --- p.39Chapter 3.3 --- The Statistical Additive Uncertainty Bounds --- p.40Chapter 3.4 --- Additive Uncertainty Characterization --- p.45Chapter 3.4.1 --- "Linear Programming Spectral Overbounding and Factor- ization Algorithm (LPSOF) [20,21]" --- p.45Chapter 4 --- Basic System Identification and Model Reduction Algorithms --- p.48Chapter 4.1 --- The Eigensystem Realization Algorithm --- p.49Chapter 4.1.1 --- Basic Algorithm --- p.49Chapter 4.1.2 --- Estimating Markov Parameters from Input/Output data: Observer/Kalman Filter Identification (OKID) --- p.51Chapter 4.2 --- The Frequency-Domain Identification via 2-norm Minimization --- p.54Chapter 4.3 --- Balanced Realization and Truncation --- p.55Chapter 4.4 --- Frequency Weighted Balanced Truncation --- p.56Chapter 5 --- Plant Model Reduction and Robust Control Design --- p.59Chapter 5.1 --- Problem Formulation --- p.59Chapter 5.2 --- Iterative Reweighting Scheme --- p.60Chapter 5.2.1 --- Rationale Behind the Scheme --- p.62Chapter 5.3 --- Integrated Model Reduction/ Robust Control Design with Iter- ated Reweighting --- p.63Chapter 5.4 --- A Design Example --- p.64Chapter 5.4.1 --- The Plant and Specification --- p.64Chapter 5.4.2 --- First Iteration --- p.65Chapter 5.4.3 --- Second Iteration --- p.67Chapter 5.5 --- Approximate Fractional Frequency Weighting --- p.69Chapter 5.5.1 --- Summary of Past Results --- p.69Chapter 5.5.2 --- Approximate Fractional Frequency Weighting Approach [40] --- p.70Chapter 5.5.3 --- Simulation Results --- p.71Chapter 5.6 --- Integrated System Identification/Control Design with Iterative Reweighting Scheme --- p.74Chapter 6 --- Controller Reduction and Robust Control Design --- p.82Chapter 6.1 --- Motivation for Controller Reduction --- p.83Chapter 6.2 --- Choice of Frequency Weightings for Controller Reduction --- p.84Chapter 6.2.1 --- Stability Margin Considerations --- p.84Chapter 6.2.2 --- Closed-Loop Transfer Function Considerations --- p.85Chapter 6.2.3 --- A New Way to Determine Frequency Weighting --- p.86Chapter 6.3 --- A Scheme for Iterative Frequency Weighted Controller Reduction (IFWCR) --- p.87Chapter 7 --- A Comparative Design Example --- p.90Chapter 7.1 --- Plant Model Reduction Approach --- p.90Chapter 7.2 --- Weighted Controller Reduction Approach --- p.94Chapter 7.2.1 --- A Full Order Controller --- p.94Chapter 7.2.2 --- Weighted Controller Reduction with Stability Considera- tions --- p.94Chapter 7.2.3 --- Iterative Weighted Controller Reduction --- p.96Chapter 7.3 --- Summary of Results --- p.101Chapter 7.4 --- Discussions of Results --- p.101Chapter 8 --- A Comparative Example on a Benchmark problem --- p.105Chapter 8.1 --- The Benchmark plant [54] --- p.106Chapter 8.1.1 --- Benchmark Format and Design Information --- p.106Chapter 8.1.2 --- Control Design Specifications --- p.107Chapter 8.2 --- Selection of Performance Weighting function --- p.108Chapter 8.2.1 --- Reciprocal Principle --- p.109Chapter 8.2.2 --- Selection of W1 --- p.110Chapter 8.2.3 --- Selection of W2 --- p.110Chapter 8.3 --- System Identification by ERA --- p.112Chapter 8.4 --- System Identification by Curve Fitting --- p.114Chapter 8.4.1 --- Spectral Estimate --- p.114Chapter 8.4.2 --- Curve Fitting Results --- p.114Chapter 8.5 --- Robust Control Design --- p.115Chapter 8.5.1 --- The selection of W1 weighting function --- p.115Chapter 8.5.2 --- Summary of Design Results --- p.116Chapter 8.6 --- Stress Level 1 --- p.117Chapter 8.6.1 --- System Identification Results --- p.117Chapter 8.6.2 --- Design Results --- p.119Chapter 8.6.3 --- Step Response --- p.121Chapter 8.7 --- Stress Level 2 --- p.124Chapter 8.7.1 --- System Identification Results --- p.124Chapter 8.7.2 --- Step Response --- p.125Chapter 8.8 --- Stress Level 3 --- p.128Chapter 8.8.1 --- System Identification Results --- p.128Chapter 8.8.2 --- Step Response --- p.129Chapter 8.9 --- Comparisons with Other Designs --- p.132Chapter 9 --- Conclusions and Recommendations for Further Research --- p.133Chapter 9.1 --- Conclusions --- p.133Chapter 9.2 --- Recommendations for Further Research --- p.135Chapter A --- Design Results of Stress Levels 2 and3 --- p.137Chapter A.1 --- Stress Level 2 --- p.137Chapter A.2 --- Stress Level 3 --- p.140Chapter B --- Step Responses with Reduced Order Controller --- p.142Chapter C --- Summary of Results of Other Groups on the Benchmark Prob- lem --- p.145Chapter C.1 --- Indirect and implicit adaptive predictive control [45] --- p.146Chapter C.2 --- H∞ Robust Control [51] --- p.150Chapter C.3 --- Robust Stability Degree Assignment [53] --- p.152Chapter C.4 --- Model Reference Adaptive Control [46] --- p.154Chapter C.5 --- Robust Pole Placement using ACSYDE (Automatic Control Sys- tem Design) [47] --- p.156Chapter C.6 --- Adaptive PI Control [48] --- p.157Chapter C.7 --- Adaptive Control with supervision [49] --- p.160Chapter C.8 --- Partial State Model Reference (PSRM) Control [50] --- p.162Chapter C.9 --- Contstrainted Receding Horizon Predictive Control (CRHPC) [52] --- p.165Bibliography --- p.16

Multi - objective sliding mode control of active magnetic bearing system

Active Magnetic Bearing (AMB) system is known to inherit many nonlinearity effects due to its rotor dynamic motion and the electromagnetic actuators which make the system highly nonlinear, coupled and open-loop unstable. The major nonlinearities that are associated with AMB system are gyroscopic effect, rotor mass imbalance and nonlinear electromagnetics in which the gyroscopics and imbalance are dependent to the rotational speed of the rotor. In order to provide satisfactory system performance for a wide range of system condition, active control is thus essential. The main concern of the thesis is the modeling of the nonlinear AMB system and synthesizing a robust control method based on Sliding Mode Control (SMC) technique such that the system can achieve robust performance under various system nonlinearities. The model of the AMB system is developed based on the integration of the rotor and electromagnetic dynamics which forms nonlinear time varying state equations that represent a reasonably close description of the actual system. Based on the known bound of the system parameters and state variables, the model is restructured to become a class of uncertain system by using a deterministic approach. In formulating the control algorithm to control the system, SMC theory is adapted which involves the formulation of the sliding surface and the control law such that the state trajectories are driven to the stable sliding manifold. The surface design involves the transformation of the system into a special canonical representation such that the sliding motion can be characterized by a convex representation of the desired system performances. Optimal Linear Quadratic (LQ) characteristics and regional pole-clustering of the closed-loop poles are designed to be the objectives to be fulfilled in the surface design where the formulation is represented as a set of Linear Matrix Inequality optimization problem. For the control law design, a new continuous SMC controller is proposed in which asymptotic convergence of the system’s state trajectories in finite time is guaranteed. This is achieved by adapting the equivalent control approach with the exponential decaying boundary layer technique. The newly designed sliding surface and control law form the complete Multi-objective SMC (MO-SMC) and the proposed algorithm is applied into the nonlinear AMB in which the results show that robust system performance is achieved for various system conditions. The findings also demonstrate that the MO-SMC gives better system response than the reported ideal SMC (I-SMC) and continuous SMC (C-SMC)

A study on LQG/LTR control for damping inter-area oscillations in power systems

Published versio