UNIVERSITÁ DEGLI STUDI DI SALERNO Other more complex feedback control structures Prof. Ing. Michele MICCIO Dip. Ingegneria Industriale (Università di Salerno) Prodal Scarl (Fisciano) rev. 2.1 of May 15, 2017

Adaptive control or Self-Tuning Ingegneria e Tecnologia dei Sistemi di Controllo Adaptive control or Self-Tuning Lezione 2 see § 7.1.1 see § 22.1 A control technique in which one or more parameters of the PID controller are sensed and used to vary the feedback control signals in order to satisfy the performance criteria. There are many situations where the changes in process dynamics are so large that a constant linear feedback controller will not work satisfactorily. For example, the dynamics of a supersonic aircraft. Adaptive control is also useful for industrial process control. Since delay and holdup times depend on production, it is desirable to retune the regulators when there is a change in production. Adaptive control can also be used to compensate for changes due to aging and wear. G. Magnani

Ingegneria e Tecnologia dei Sistemi di Controllo Lezione 2 Inferential Control  Problem: The controlled variable cannot be measured (or has a too large sampling period). Possible solutions: Control another related variable (e.g., temperature instead of composition). Inferential control:  Control is based on a suitable estimate of the controlled variable. see § 22.2  Seborg, Process_Dynamics_and_Control_2nd-ed_2003 G. Magnani

Ingegneria e Tecnologia dei Sistemi di Controllo Lezione 2 Inferential Control see § 22.2 G. Magnani

Inferential Control Soft sensor Ingegneria e Tecnologia dei Sistemi di Controllo Inferential Control Soft sensor Lezione 2 Soft sensor or virtual sensor is a common name for software where several measurements are processed together and then properly used for calculating new quantities, which need not be measured. Soft sensors are used when hardware sensors are unavailable or unsuitable Soft sensors are inferential estimators, taking advantage of available measurements and drawing conclusions from process observations Well-known software algorithms that can be seen as soft sensors include Kalman filters. More recent implementations of soft sensors use neural networks or fuzzy computing. Examples: Kalman filters for estimating the geographical location Soft sensors in the biotech industry: to determine total cell mass in a bioreactor to estimate the concentration of product proteins inside microorganisms  Seborg, Process_Dynamics_and_Control_2nd-ed_2003  Fortuna, L., Graziani, S., Rizzo, A., Xibilia, M.G., Soft Sensors for Monitoring and Control of Industrial Processes, 2007 G. Magnani

Robust control Robustness Robust control aims at designing a fixed (non–adaptive) controller such that some defined level of performance of the controlled system (e.g., closed– loop stability, reference tracking performance and disturbance rejection performance) is guaranteed, irrespective of changes in plant dynamics within a predefined (typically compact) set Robust control is a branch of control theory that explicitly deals with uncertainty in its approach to controller design. The early methods of Bode and others were fairly robust; the theory of Robust Control took shape in the 1980s and 1990s and is still active today. In contrast with an adaptive control policy, a robust control policy is static; rather than adapting to measurements of variations, the controller is designed to work assuming that certain variables will be unknown but, for example, bounded.

Ingegneria e Tecnologia dei Sistemi di Controllo Lezione 2 Cascade control d1 2 actual process see § 8.2 at pag. 245 see § 20.1 at pag. 395 Two feedback control loops are "nested”: primary or master or external loop secondary or slave or internal loop only one manipulated variable u, but more than one measured variable w must be a measurable (auxiliary) variable the output from the "master” controller R1 becomes the set point for the "slave” controller R2 usually, G2 is a minimum phase system G1 can be a non-minimum phase system G. Magnani

Cascade control Example of a Heat Exchanger with measuring delay Ingegneria e Tecnologia dei Sistemi di Controllo Lezione 2 Cascade control Example of a Heat Exchanger with measuring delay Secondary temperature T0 loop See §8.2 in Magnani, Ferretti e Rocco G. Magnani

Cascade control Example of a Cascade Jacketed Reactor Controlled variables: Reactor temperature Coolant output temperature

Cascade control Example of a Cascade Jacketed Reactor Two process measurements: the primary or outer measured process variable is still the product stream temperature the secondary measured process variable is coolant temperature out of the jacket Two controllers Only one final control element Notes: As with all cascade architectures, the output of the primary controller is the set point of the secondary controller. The secondary or inner loop controller manipulates the cooling jacket flow rate.  The benefit of a cascade architecture is improved disturbance rejection.

Ingegneria e Tecnologia dei Sistemi di Controllo Lezione 2 Ratio Control see § 8.5.3 at pag. 260 see § 21.5 at pag. 427 Definition A control scheme the objective of which is to maintain the ratio of two variables at a pre-specified value.  A ratio control system is characterized by the fact that variations in the secondary variable don’t reflect back on the primary variable. G. Magnani

Ingegneria e Tecnologia dei Sistemi di Controllo Lezione 2 Ratio Control Typical applications see § 8.5.3 at pag. 260 Mixing Diluition P&ID schemes . . . Holding the fuel-air ratio in a burner to the optimum. Maintaining a stoichiometric ratio of reactants of a reactor. Keeping a specified reflux ratio for a distillation column, etc.  http://instrumentationandcontrollers.blogspot.it G. Magnani

Ratio Control Control strategy see § 8.5.3 at pag. 260 see § 21.5 at pag. 427 Ratio Control Control strategy There are at least two possible ways to implement the ratio control strategy: Ratio computation "wild" stream A stream B where: r = wB/wA is the setpoint  The flow rate of one of the streams feeding the mixed flow, designated as the wild feed, can change freely. The ratio controller manipulates a second variable to maintain the desired ratio between the first and the second variable. Set-point (wB) computation where: K = wB/wA Introduction to Process Control Romagnoli & Palazoglu

Ingegneria e Tecnologia dei Sistemi di Controllo Multivariable control Lezione 2 Example: 2x2 (DIDO) open loop system see § 8.6 at pag. 265 see ch. 23 and 24 G. Magnani

Ingegneria e Tecnologia dei Sistemi di Controllo Lezione 2 Multivariable control Example: 2x2 (DIDO) closed loop system  Loop interaction may enhance instability G. Magnani

Ingegneria e Tecnologia dei Sistemi di Controllo Multivariable control Lezione 2 Example of a DIDO system: three way mixing valve see § 8.6 at pag. 265 Controlled variables: output flowrate output composition P&ID scheme  Loops are separated: no loop interaction! G. Magnani

Multivariable control Example of a DIDO system: Binary Distillation Column Controlled variables: top composition bottom composition