Actuator Fault Tolerance in Control Systems with Predictive Constrained Set-Point Optimizers

Actuator Fault Tolerance in Control Systems with Predictive Constrained Set-Point OptimizersMechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach.

Shape Optimization of a Knuckle using the Design of Experiments

DOE (design of experiments) was applied to the design of a knuckle as a part of a suspension system. Specifically, knuckle made of aluminum alloy was optimized considering the strength. On the other hand, design variables were set as shape variables. During structural optimization using DOE, an orthogonal array strategy was developed to determine the optimum design. The relevant discrete variables were treated as levels. Since the conventional orthogonal array did not consider the constraint, however, the characteristic function was defined to include the effect of constraint feasibility. The general DOE was expanded to include problems with constraints related to the new characteristic function.

Decision Making and Constraint Tradeoff Visualization for Design Under Uncertainty

In design optimization problems under uncertainty, two conflicting issues are generally of interest to the designer: feasibility and optimality. In this research, we adopt the philosophy that design, especially under uncertainty, is a decision making process, where the associated tradeoffs can be conveniently understood using multiobjective optimization. The importance of constraint feasibility and the associated tradeoffs, especially in the presence of equality constraints, is examined in this paper. We propose a three-step decision making framework that facilitates effective decision making under uncertainty: (1) formulating a multiobjective problem that effectively models the tradeoffs under uncertainty, (2) generating design alternatives by solving the proposed multiobjective robust design formulation, and (3) choosing a final design using filtering and constraint uncertainty visualization schemes. The proposed framework can be used to systematically explore the design space from a constraint tradeoff perspective. A tolerance synthesis example is used to illustrate the proposed decision making process.

A Constraint Solving-Based Approach to Analyze 2D Geometric Problems With Interval Parameters

Many applications of geometric nature can be modeled by geometric problems defined by constraints in which the constraint parameters have interval uncertainty. In a previous work, we developed a method for solving geometric constraint problems where parameters are narrow intervals in the domain of the geometric problem. Based on this work, we present a new approach to solve more general problems with non-trivial-width interval parameters that may not necessarily be in the domain of the problem. We show how our approach is successfully applied to a number of problems like solving geometric problems with tolerances, checking constraint feasibility and analyzing link motion of planar mechanisms.