Efficient Solving of Quantified Inequality Constraints over the Real Numbers

Let a quantified inequality constraint over the reals be a formula in the first-order predicate language over the structure of the real numbers, where the allowed predicate symbols are $\leq$ and $<$. Solving such constraints is an undecidable problem when allowing function symbols such $\sin$ or $\cos$. In the paper we give an algorithm that terminates with a solution for all, except for very special, pathological inputs. We ensure the practical efficiency of this algorithm by employing constraint programming techniques

A Unified Framework for the H∞ Mixed-Sensitivity Design of Fixed Structure Controllers through Putinar Positivstellensatz

In this paper, we present a novel technique to design fixed structure controllers, for both continuous-time and discrete-time systems, through an H∞ mixed sensitivity approach. We first define the feasible controller parameter set, which is the set of the controller parameters that guarantee robust stability of the closed-loop system and the achievement of the nominal performance requirements. Then, thanks to Putinar positivstellensatz, we compute a convex relaxation of the original feasible controller parameter set and we formulate the original H∞ controller design problem as the non-emptiness test of a set defined by sum-of-squares polynomials. Two numerical simulations and one experimental example show the effectiveness of the proposed approach

Robust nonlinear controller based on set propagation

Bibliography: leaves 74-[76.]A novel control method, based on interval analysis, that optimises the control surface (or u-surface) for sampled systems with output disturbances is demonstrated on a driven pendulum with actuator constraints. The fitness function to be maximized is the probability of each state of the system being controlled to the setpoint without being perturbed to regions that are more iterations away from the setpoint. The u-surface is designed by finding all the states that could go to the setpoint in an interval and optimising these states. This process is repeated (backwards in time) by optimising states that go to the previously optimised states until no more states that have not been optimised are found. The proposed control method has been applied to the problem of swinging up a driven pendulum from rest to the inverted position with constraints on the torque of the motor. This method is computationally intensive and time constraints limit its current application to systems of low order

Robust analysis and design of control systems using interval arithmetic

Several robustness problems such as stability and performance robustness analysis of feedback systems and robust design of control systems in the presence of mixed nonlinear parametric and nonparametric perturbations can be solved by means of algorithms based on interval-arithmetic computation. Some of the main algorithms available in the literature are presented, and their efficiency is tested on some examples of robustness analysis and design of control systems