N. K. Loh

Optimal Model-Following Suspension with Microcomputerized Damping

The problem of vibration isolation is investigated from the standpoint of modern control and optimization theory. The proposed optimal model-following suspension design was verified experimentally by means of a microcomputerized suspension model. The experimental results are very encouraging and indicate promising potential application of the proposed scheme to real-world systems. This paper describes the modeling and formulation of an optimal control suspension reference model, and the implementation algorithm of the proposed microcomputerized optimal model-following suspension scheme.

A robust optimal model matching control

A deductive design approach is proposed to develop an optimal feedback control, while preserving quantitative robustness and noise rejection properties. The design procedure involves the minimization of a suitably selected performance index to reflect a required model matching (following) objective. Gradient projection schemes, applied to the performance index and feasible constraints, are used to accomplish the design tradeoff. A mechanical manipulator control system example is employed as a vehicle to illustrate the overall design selection and optimization. Uncertainty of the system description is included as a feature of the example.

Discrete-time frequency-shaping parametric LQ control with application to active seat suspension control

The theory of a discrete-time parametric linear quadratic (PLQ) control is extended to a class of frequency-shaped performance measures. The incorporation of frequency-dependent weighting matrices allows the emphasis or de-emphasis of the importance of the system variables being penalized over specific bands of frequencies. Results are presented for constant-gain and dynamic output feedback configurations of frequency-shaping optimal control. The resultant control is applied to the design of active seat suspension control. The active suspension maximizes ride comfort by discriminatory minimization of average whole-body absorbed power over a band of frequencies that causes the most discomfort to a human being. >

Discrete-time optimal feedback controllers with time-multiplied performance indexes

A class of optimal state and output feedback control laws for discrete-time time-invariant linear systems which minimizes a class of discrete-time time-multiplied performance indexes is presented. A necessary condition, an existence condition, and a sufficient condition for the control laws are derived. A simple example is given to illustrate the effectiveness of the proposed control laws.

A joint frequency-position domain structure identification of nonlinear discrete-time systems by neural networks

A new technique is proposed to identify the structure and the parameters of nonlinear discrete-time system models. The structure is represented in a frequency-position domain of Gabor basis functions (GBFs). A simplification to the GBF is also presented, where the spatial Gaussian envelope of GBF is replaced with a triangular one. A modification to the GBF has also been introduced in order to suppress the effects of noise on the procedure. A three-layered neural network, augmented with nonuniform sampling, is described for solving the system identification problem. >

Robust Constrained Eigensystem Assignment

A robust control algorithm based on eigensystem assignment in a large class of regions in the complex plane is presented. The algorithm allows pole placement as a function of the desired coefficients of the characteristic equation and reduces their sensitivity to perturbation by selecting robust eigenvectors as well. A quantitative robustness measure is developed to estimate the bound on parametric uncertainty possible while still remaining in the target region. The algorithm is tested on a numerical example and results are discussed.

An application of expert hierarchical control to piecewise linear systems

A hierarchical control structure is presented for the output feedback control of nonlinear systems approximated by piecewise linear models. At the lowest level, traditional output feedback control techniques are used to design robust local controllers for each operating segment. The low-level control is then enhanced by the use of a supervisory level, which combines clustering and binary tree classification techniques from artificial intelligence to identify the transitions between the models. The controller is trained on typical operating scenarios. The structure is applied to the case of a nonlinear DC servomotor with a piecewise linear model of the thyristor converter dynamics.<<ETX>>

Design of observers for two-dimensional systems

This paper extends the familiar 1-D concepts of observer designs to the design of observers for 2-D systems described by Roessers model. Both full-order and reduced-order observers are considered. Extensions of 1-D concepts to 2-D is non-trivial in view of the requirement that state transformations for 2-D systems have to be block diagonal in order to preserve their input-output properties. Another issue addressed in this paper is the required asymptotic stability property of the 2-D observers. To maintain tractability in asymptotic stability analysis, we consider the class of 2-D observers with separable characteristic polynomials. Illustrative examples are provided.

Unified Design of Robust Discrete-Time Control Systems

The problem of robust control of discrete-time uncertain systems is considered in this paper. By formulating different control objectives into an error system, all problems of interest are reduced to a robust stabilization problem of the error system. The approach is based on quadratic stability. A generalized Riccati equation is obtained by quadratic bound method and its properties are investigated. A much less conservative control law is found by recursively solving the generalized Riccati equation with the stability of the closed-loop system checked at each step. The design technique is applied to a.d.c. motor to illustrate the effectiveness and advantages of the method developed.

Self-training cognitive preview control for autonomous vehicle path navigation

A description is given of a cognitive preview control strategy for autonomous-vehicle steering and cruise guidance by combining optimal preview control theory with rule-based perceptive cruise command generation. The authors also propose a self-training cognition procedure for determining a suitable perceptive schedule for cognitive cruise and steering control. The control yields humanlike driving action in path navigation. It is an intelligent control that decides the cruising speed, plans its control action, and learns the limitation of its steering control. The strategy is being simulated and tested on an autonomous robotic vehicle testbed which is designed for intelligent control experimentation.<<ETX>>