Outline Control structure design (plantwide control) A procedure for control structure design I Top Down Step 1: Degrees of freedom Step 2: Operational objectives (optimal operation) Step 3: What to control ? (self-optimizing control) Step 4: Where set production rate? II Bottom Up Step 5: Regulatory control: What more to control ? Step 6: Supervisory control Step 7: Real-time optimization Case studies

II. Bottom-up Determine secondary controlled variables and structure (configuration) of control system (pairing) A good control configuration is insensitive to parameter changes Step 5. REGULATORY CONTROL LAYER 5.1 Stabilization (including level control) 5.2 Local disturbance rejection (inner cascades) What more to control? (secondary variables) Step 6. SUPERVISORY CONTROL LAYER Decentralized or multivariable control (MPC)? Pairing? Step 7. OPTIMIZATION LAYER (RTO)

Step 5. Regulatory control layer Purpose: “Stabilize” the plant using a simple control configuration (usually: local SISO PID controllers + simple cascades) Enable manual operation (by operators) Main structural issues: What more should we control? (secondary cv’s, y2, use of extra measurements) Pairing with manipulated variables (mv’s u2) y1 = c y2 = ?

Objectives regulatory control layer Allow for manual operation Simple decentralized (local) PID controllers that can be tuned on-line Take care of “fast” control Track setpoint changes from the layer above Local disturbance rejection Stabilization (mathematical sense) Avoid “drift” (due to disturbances) so system stays in “linear region” “stabilization” (practical sense) Allow for “slow” control in layer above (supervisory control) Make control problem easy as seen from layer above The key decisions here (to be made by the control engineer) are: Which extra secondary (dynamic) variables y2 should we control? Propose a (simple) control configuration (select input-output pairings)

Objectives regulatory control layer Allow for manual operation Simple decentralized (local) PID controllers that can be tuned on-line Take care of “fast” control Track setpoint changes from the layer above Local disturbance rejection Stabilization (mathematical sense) Avoid “drift” (due to disturbances) so system stays in “linear region” “stabilization” (practical sense) Allow for “slow” control in layer above (supervisory control) Make control problem easy as seen from layer above Implications for selection of y2: Control of y2 “stabilizes the plant” y2 is easy to control (favorable dynamics)

1. “Control of y2 stabilizes the plant” A. “Mathematical stabilization” (e.g. reactor): Unstable mode is “quickly” detected (state observability) in the measurement (y2) and is easily affected (state controllability) by the input (u2). Tool for selecting input/output: Pole vectors y2: Want large element in output pole vector: Instability easily detected relative to noise u2: Want large element in input pole vector: Small input usage required for stabilization B. “Practical extended stabilization” (avoid “drift” due to disturbance sensitivity): Intuitive: y2 located close to important disturbance Maximum gain rule: Controllable range for y2 is large compared to sum of optimal variation and control error More exact tool: Partial control analysis

Recall maximum gain rule for selecting primary controlled variables c: Controlled variables c for which their controllable range is large compared to their sum of optimal variation and control error Restated for secondary controlled variables y2: Control variables y2 for which their controllable range is large compared to their sum of optimal variation and control error controllable range = range y2 may reach by varying the inputs optimal variation: due to disturbances control error = implementation error n Want large Want small

What should we control (y2)? Rule: Maximize the scaled gain General case: Maximize minimum singular value of scaled G Scalar case: |Gs| = |G| / span |G|: gain from independent variable (u2) to candidate controlled variable (y2) IMPORTANT: The gain |G| should be evaluated at the (bandwidth) frequency of the layer above in the control hierarchy! If the layer above is slow: OK with steady-state gain as used for selecting primary controlled variables (y1=c) BUT: In general, gain can be very different span (of y2) = optimal variation in y2 + control error for y2 Note optimal variation: This is often the same as the optimal variation used for selecting primary controlled variables (c). Exception: If we at the “fast” regulatory time scale have some yet unused “slower” inputs (u1) which are constant then we may want find a more suitable optimal variation for the fast time scale.

2. “y2 is easy to control” (controllability) Statics: Want large gain (from u2 to y2) Main rule: y2 is easy to measure and located close to available manipulated variable u2 (“pairing”) Dynamics: Want small effective delay (from u2 to y2) “effective delay” includes inverse response (RHP-zeros) + high-order lags

Rules for selecting u2 (to be paired with y2) Avoid using variable u2 that may saturate (especially in loops at the bottom of the control hieararchy) Alternatively: Need to use “input resetting” in higher layer (“mid-ranging”) Example: Stabilize reactor with bypass flow (e.g. if bypass may saturate, then reset in higher layer using cooling flow) “Pair close”: The controllability, for example in terms a small effective delay from u2 to y2, should be good.

Partial control Cascade control: y2 not important in itself, and setpoint (r2) is available for control of y1 Decentralized control (using sequential design): y2 important in itself

Partial control analysis Primary controlled variable y1 = c (supervisory control layer) Local control of y2 using u2 (regulatory control layer) Setpoint y2s : new DOF for supervisory control Assumption: Perfect control (K2 -> 1) in “inner” loop Derivation: Set y2=y2s-n2 (perfect control), eliminate u2, and solve for y1

Partial control: Distillation Supervisory control: Primary controlled variables y1 = c = (xD xB)T Regulatory control: Control of y2=T using u2 = L (original DOF) Setpoint y2s = Ts : new DOF for supervisory control u1 = V

Limitations of partial control? Cascade control: Closing of secondary loops does not by itself impose new problems Theorem 10.2 (SP, 2005). The partially controlled system [P1 Pr1] from [u1 r2] to y1 has no new RHP-zeros that are not present in the open-loop system [G11 G12] from [u1 u2] to y1 provided r2 is available for control of y1 K2 has no RHP-zeros Decentralized control (sequential design): Can introduce limitations. Avoid pairing on negative RGA for u2/y2 – otherwise Pu likely has a RHP-zero

Selecting measurements and inputs for stabilization: Pole vectors Maximum gain rule is good for integrating (drifting) modes For “fast” unstable modes (e.g. reactor): Pole vectors useful for determining which input (valve) and output (measurement) to use for stabilizing unstable modes Assumes input usage (avoiding saturation) may be a problem Compute pole vectors from eigenvectors of A-matrix

Example: Tennessee Eastman challenge problem