Jaroslav Pekar

Brief paper: On the computation of linear model predictive control laws

Finite-time optimal control problems with quadratic performance index for linear systems with linear constraints can be transformed into Quadratic Programs (QPs). Model Predictive Control requires the on-line solution of such QPs. This can be obtained by using a QP solver or evaluating the associated explicit solution. The objective of this note is twofold. First, we shed some light on the computational complexity and storage demand of the two approaches when an active set QP solver is used. Second, we show the existence of alternative algorithms with a different tradeoff between memory and computational time. In particular, we present an algorithm which, for a certain class of systems, outperforms standard explicit solvers both in terms of memory and worst case computational time.

Toward a Systematic Design for Turbocharged Engine Control

The efficient development of high performance control is becoming more important and more challenging with ever tightening emissions legislation and increasingly complex engines. Many traditional industrial control design techniques have difficulty in addressing multivariable interactions among subsystems and are becoming a bottleneck in terms of development time. In this article we explore the requirements imposed on control design from a variety of sources: the physics of the engine, the embedded software limitations, the existing software hierarchy, and standard industrial control development processes. Decisions regarding the introduction of any new control paradigm must consider balancing this diverse set of requirements. In this context we then provide an overview of our work in developing a systematic approach to the design of optimal multivariable control for air handling in turbocharged engines.

On the Computation of Linear Model Predictive Control Laws

Finite-time optimal control problems with quadratic performance index for linear systems with linear constraints can be transformed into Quadratic Programs (QPs). Model Predictive Control requires the online solution of such QPs. This can be obtained by using a QP solver or evaluating the associated explicit solution. Objective of this note is to present alternative algorithms which trade off memory versus computational time differently than explicit solutions and active sets QP solvers.

Distributed MPC with parametric coordination

The paper presents an effective coordination scheme for a distributed optimization based on dual decomposition. The targeted class of optimization problems are strictly convex quadratic functions with linear constraints, where the dual function with coupling equality constraints in Lagrangian is continuous piecewise quadratic. The coordination is based on multi-parametric programming - the subproblem solvers return their solution on a polyhedron around a given Lagrange multiplier value. Centralized coordinator constructs the gradient and Hessian of a dual function and reaches exact consensus in a finite number of iterations, while only some subproblems are queried for a new solution in each iteration. The algorithm is applied to the distributed model predictive control. The efficiency, in terms of optimization time and the number of iterations, is demonstrated on the distributed model predictive control of the Barcelona water distribution network.

Soot blowing optimization system and application to oil-fired boiler

The paper presents a method for the soot blowing optimization and its pilot application to the oil fired boiler of a power plant. The important monitored parameter of each steam generator is the thermal efficiency or the heat rate. These parameters are influenced by many factors, including for example the boiler design, control strategy, fuel quality, operating conditions, etc. The quality of the heat transfer between the flue gas and steam (water, water/steam mixture) is affected by soot, ash, and slag deposit (fouling) on the individual heat exchange surfaces. To avoid the negative effect of fouling, it is necessarily to clear the surfaces periodically. The objective of the soot blowing optimization system is to optimize this cycle and to provide recommendation to the plant operator. Proposed sootblowing optimization system monitors the process variables, estimates the fouling status of heat exchange surfaces, evaluates impact on economy and triggers the sootblowing sequence.

Towards ECU-ready nonlinear model predictive control: Tip-in maneuver case study

This paper presents a study on tackling full load request in air path control of a turbocharged diesel engine. The adopted approach is based on nonlinear model predictive control that employs a specially tailored model that can be embedded and simulated at Engine Control Unit (ECU). The derived air path model of a turbocharged engine comprises of one differential equation and a set of algebraic equations with fixed polynomial structure that allow cheap evaluation of Jacobians and integration with fixed Euler step. The main advantage of this approach is that the simulation and problem construction boils down to evaluation of explicit polynomial functions which has favorable properties for real-time implementation. On top of this model, a nonlinear model predictive control problem is formulated and solved using sequential quadratic programming. The approach is demonstrated in a simulation study that focuses on abrupt load request in the boost pressure setpoint.