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The nonlinear adaptive algorithm of Kanellakopoulos et al. (1991) was modified to produce an error-based algorithm. This permits global stabilizability for a large subset of pure-feedback nonlinear systems. The algorithm was demonstrated on the single-input stabilization problem, but extends easily to the multiple input tracking problems.
Controllability and linear closed-loop controls in linear periodic systems
Linear and nonlinear control systems via state variable feedback with applications in nuclear reactor control
Time optimal control of linear systems with constraints on control amplitude and rate
Nonlinear system controllability by application of linear control vectors
Synthesis of optimal controls for linear problems with retarded controls
Synthesis of optimal controls for linear systems with retarded controls
Linear computer-controlled closed loop systems sensitivity analysis, deriving estimation error incremental covariance to demonstrate quality deterioration under perturbed initial conditions, parameters, etc
A small compact magnetic bearing design developed and tested features a bearing capable of supporting over ten times its own weight, dimensioned 8 cm diam by 3.75 cm, with rare-earth cobalt magnets. Only 1% of the device payload figures as part of the magnetic suspension. The design is servoed in two axes and exhibits inherent stability in three more degrees of freedom, with full rotational freedom in the desired axis. Capacitive radial gap sensing allows stiff servoing of the rotation axis. Differential sensing and EM control linearize control functions. Low power drain, simple fabrication and assembly, and larger clearances than in air bearings or ball bearings are reported
Optimal stochastic control investigated for linear systems with driving noise intensity proportional to control input
For linear delay systems with limited controls the notion of a proper control system is introduced. If the uncontrolled system x(t) = Ax(t) + Bx(t - h) is uniformly asymptotically stable and the control equation x(t) = Ax(t) + Bx(t-) + Cu(t) is proper, then the control system is Euclidean null-controllable. It is noted that controllability is equivalent to the system being proper for delay systems with unlimited power.
Control-theory design package, called Optimal Regulator Algorithms for Control of Linear Systems (ORACLS), aids in design of controllers and optimal filters for systems modeled by linear, time invariant differential and difference equations. ORACLS is particularly attractive rigorous tool for dealing with multi-input and multi-output dynamic systems in both continuous and discrete forms.
Controllability for linear and nonlinear systems
The problem is posed with the additional constraints that the dynamic controller uses only noise-corrupted outputs, and that its dimension is significantly lower than that of a Kalman filter. The unknown disturbance is viewed as an adversary which tries to maximize a performance criterion: a criterion that the controller gains attempt to minimize. The optimal controller gains are determined by solving a nonlinear matrix two-point boundary value problem.
The application of linear optimal control to the design of systems with integral control action on specified outputs is considered. Using integral terms in a quadratic performance index, an asymptotic analysis is used to determine the effect of variable quadratic weights on the eigenvalues and eigenvectors of the closed loop system. It is shown that for small integral terms the placement of integrator poles and gain calculation can be effectively decoupled from placement of the primary system eigenvalues. This technique is applied to the design of integral controls for a STOL aircraft outer loop guidance system.
Linear optimal control in systems with uncertain parameters, noting application to design of compensating network for flexible booster for uncertain value of first bending mode
Control theory design package offers engineer full range of subroutines to manipulate and solve Linear-Quadratic-Gaussian types of problems. ORACLS is rigorous tool, intended for multi-input and multi-output dynamic systems in both continuous and discrete form. Written in FORTRAN.
The linear quadratic optimal control method is used today to solve many complex systems problems. As system complexity increases, and as linear quadratic optimal control is used in more demanding situations, the extension of the design methodology to cover system failures, robustness and reliability is of crucial importance. This paper documents the progress toward a theory which incorporates reliability in the performance index; a linear quadratic control problem is formulated which accounts for system effectiveness and gives an offline procedure for comparing two linear quadratic control systems on the basis of both reliability and performance.