Edoardo Mosca

Nonlinear control of constrained linear systems via predictive reference management

A method based on conceptual tools of predictive control is described for solving set-point tracking problems wherein pointwise-in-time input and/or state inequality constraints are present. It consists of adding to a primal compensated system a nonlinear device, called command governor (CG), whose action is based on the current state, set-point, and prescribed constraints. The CG selects at any time a virtual sequence among a family of linearly parameterized command sequences, by solving a convex constrained quadratic optimization problem, and feeds the primal system according to a receding horizon control philosophy. The overall system is proved to fulfill the constraints, be asymptotically stable, and exhibit an offset-free tracking behavior, provided that an admissibility condition on the initial state is satisfied. Though the CG can be tailored for the application at hand by appropriately choosing the available design knobs, the required online computational load for the usual case of affine constraints is well tempered by the related relatively simple convex quadratic programming problem.

Robust H2 and H∞ filtering for uncertain linear systems

An apparatus for reducing wastage during the start-up phase of extrusion device used to produce extruded profiles such as tread strips for vehicle tires from a plurality of mixtures. Each of a plurality of extruders extrudes one component of the profile and is provided with its own drive motor. The outlets from the extruders communicate with a template member having flow channels formed therein which communicate with the outlet ends of the extruders and which lead into a common extrusion port. Pressure sensors are positioned adjacent to the outlet of each extruder. These pressure sensors and the drive motors for each extruder are operatively connected to a control device which has stored therein predetermined desired pressure levels for each extruder. The drive motors for the several extruders are actuated and the pressure of each is monitored and compared with the predetermined desired pressure for each extruder. When the pressure in a particular extruder reaches such desired pressure, the drive motor for that extruder is shut off. When the desired pressures have been reached in all of the extruders, the drive motor for all extruders are re-actuated simultaneously by the control device. Alternatively, the last extruder to reach the desired pressure can continue to operate and the drive motors for the other extruders re-actuated. In either event, wastage of material is greatly reduced.

Stable redesign of predictive control

The paper deals with I/O versions of receding horizon controllers based on the minimization of multistep quadratic costs with the constraint that the terminal state goes to zero. The resulting control law yields stable closed-loop systems under sharp conditions. Simulation results are presented to both verify the theoretical analysis and relate the new control law with GPC

Fulfilling hard constraints in uncertain linear systems by reference managing

A method based on conceptual tools of predictive control is described for tackling tracking problems of uncertain linear systems wherein pointwise-in-time input and/or state inequality constraints are present. The method consists of adding to a primal compensated system a nonlinear device called predictive reference filter which manipulates the desired reference in order to fulfill the prescribed constraints. Provided that an admissibility condition on the initial state is satisfied, the control scheme is proved to fulfill the constraints, as well as stability and set-point tracking requirements, for all systems whose impulse/step responses lie within given uncertainty ranges.

Robust command governors for constrained linear systems

Robust command governors are designed for discrete-time linear time-variant (LTV) systems with polytopic uncertainty models and subject to unknown bounded disturbances and pointwise-in-time input and state-related constraints.

Unfalsified Virtual Reference Adaptive Switching Control of Plants with Persistent Disturbances

Abstract This paper addresses virtual reference adaptive switching control whereby a data-driven supervisor aims at stabilizing an unknown time-invariant dynamic system by switching at any time in feedback with system one element from a finite family of candidate controllers. Under the only assumption of problem feasibility, viz. the controller family contains a stabilizing controller, the resulting switched system is shown to be stable against arbitrary exogenous persistent bounded disturbances.

Paper: Performance improvements of self-tuning controllers by multistep horizons: The MUSMAR approach

The problem of adaptively controlling a linear multivariable plant according to a quadratic cost functional defined over a control horizon of arbitrary length is discussed. In this context, the proposed algorithm, referred to by the acronym MUSMAR, is shown to be a natural generalization of standard self-tuning controllers. By increasing the control horizon length, the MUSMAR closely approximates a steady-state LQG controller inheriting the intrinsic robustness of LQG design. Analysis and simulations give evidence of several attractive features of the MUSMAR self-tuner when applied to plants for which standard adaptive controllers fail to converge or yield an unacceptable performance.

Model-Free Adaptive Switching Control of Uncertain Time-Varying Plants

Abstract This paper addresses the problem of controlling an uncertain time-varying plant by means of a finite family of linear candidate controllers supervised by an appropriate switching logic. It is shown that global stability of the closed-loop system can be guaranteed provided that (i) at every time there is at least one candidate controller that would stabilize the current time-invariant “frozen” plant model, and (ii) the changes in the plant model are infrequent.

An ellipsoidal off-line MPC scheme for uncertain polytopic discrete-time systems

An off-line Model Predictive Control (MPC) method based on ellipsoidal calculus and viability theory is described in order to address control problems in the presence of state and input constraints for uncertain polytopic linear plants subject to persistent disturbances. In order to reduce the computational burdens and conservativeness of traditional polytopic MPC schemes, the present approach carries out off-line most of the computations and it makes use of closed-loop predictions to improve the control performance. This is done by recursively pre-computing suitable ellipsoidal inner approximations of the exact controllable sets and solving on-line a simple and numerically low-demanding optimization problem subject to a set-membership constraint. Comparisons with three other recent off-line MPC approaches are also provided in the final example.

Command governors for constrained nonlinear systems

A method is described for set-point tracking in nonlinear systems when pointwise-in-time input and/or state inequality constraints are to be enforced. It consists of adding to a primal compensated system a nonlinear device called command governor (CG) whose action is based on the current state, set-point, and prescribed constraints. The CG selects at any time the system input via a receding-horizon strategy from a virtual sequence amongst all possible command sequences by solving a constrained quadratic optimization problem. Provided that the initial state is admissible, the overall system is proved to fulfil the constraints and have desirable performance stability properties.