How to Implement Advanced Process Control (APC) and Model Predictive Control (MPC) in Simulated PCS 7

In SIMATIC PCS 7, academic research and master's thesis projects in advanced control engineering can be executed entirely in simulation without physical hardware by coupling S7-PLCSIM or SIMIT with the native Advanced Process Control (APC) blocks in the Advanced Process Library (APL). Engineers can evaluate multivariable Model Predictive Control using ModPreCon (FB1860) for coupled systems with constraints, adaptive Gain Scheduling using GainSched (FB1862) for non-linear plants, and real-time loop health diagnostics using ConPerMon (FB1861), modeling complex process physics directly in SIMIT.

Executing Advanced Control Engineering Without Physical Hardware

For control engineering researchers and postgraduate students, SIMATIC PCS 7 provides a comprehensive industrial platform for designing, benchmarking, and validating advanced control paradigms.

Through modern virtual simulation tools, entire chemical and power production facilities can be emulated on a single engineering workstation:
* S7-PLCSIM / SIMIT Virtual Controller: Emulates the S7-400 / CPU 410 controller execution engine with microsecond cycle fidelity.
* Internal Block Simulation: Standard APL blocks feature an internal simulation mode (SimOn = 1), allowing setpoint manipulation and internal feedback generation without wiring I/O.
* SIMIT Process Simulation: Solves differential equations for multi-tank hydraulic networks, distillation columns, and continuous stirred tank reactors (CSTR) in real time, feeding realistic dynamic process variables back into the simulated controller.

Multivariable Model Predictive Control with ModPreCon (FB1860)

The flagship Advanced Process Control block in PCS 7 is ModPreCon (Model Predictive Controller - FB1860):

Multivariable Architecture: Controls complex multi-input multi-output (MIMO) processes featuring up to 4 Manipulated Variables (MVs) and up to 4 Controlled Variables (CVs), while simultaneously accounting for up to 4 Measurable Disturbance Variables (DVs).
Mathematical Core: Utilizes an internal finite impulse response (FIR) step-response model to predict future process trajectories over a defined prediction horizon.
Constraint Optimization: Computes a quadratic optimization problem online at every execution cycle, honoring hard and soft constraints:
Maximum and minimum actuator positions ($MV_{min} \le MV \le MV_{max}$).
Actuator slew rate limits (maximum % travel per second).
Controlled variable tolerance bands.
Research Application: Ideal for distillation column dual-composition control, boiler steam drum level/pressure decoupling, and non-linear neutralization basins.

Adaptive and Non-Linear Control with GainSched (FB1862)

When controlling highly non-linear systems where process gain, time constants, or dead times vary with the operating point:

Gain Scheduling (GainSched - FB1862):
Operates in tandem with PIDConL.
Monitors a scheduling variable (e.g., reactor throughput, burner firing rate, or fluid temperature).
Continuously interpolates optimal PID tuning parameters ($K_p$, $T_i$, $T_d$) along a piece-wise linear polygon curve.
Control Performance Monitoring (ConPerMon - FB1861):
Continuously assesses control loop health online.
Calculates statistical performance metrics: Harris Index, settling time, standard deviation, and stiction detection.
Generates an alarm on the WinCC operator station when process friction, sensor degradation, or poor tuning causes persistent loop oscillation.

Simulating Realistic Process Dynamics in SIMIT

To benchmark advanced control algorithms rigorously, the simulated process model must exhibit realistic physical behavior:

Building Physical Component Models:
* In SIMIT, construct a multi-variable process using standard hydraulic, thermodynamic, and mechanical component libraries.
Coupling SIMIT to PCS 7:
* Create a Virtual Controller (VC) coupling. SIMIT imports the PCS 7 project symbols and hardware addresses automatically.
Simulating Disturbances and Sensor Noise:
* In SIMIT, introduce random Gaussian measurement noise onto analog process variables.
* Inject unmeasured step load disturbances (e.g., sudden changes in influent feed temperature or valve sticking) to evaluate the robustness and disturbance rejection capabilities of ModPreCon versus classical PID.

Comparison of Advanced Control Algorithms in SIMATIC PCS 7

The table below outlines the advanced control blocks available in the SIMATIC PCS 7 Advanced Process Library.

Block NameControl ParadigmProcess SuitabilityInputs / OutputsOptimization ObjectivePIDConLClassical Single-Loop PIDStandard linear SISO loops1 PV, 1 SP / 1 MVError elimination via feedbackModPreConMultivariable Predictive (MPC)Highly coupled MIMO systems with dead timeUp to 4 CVs, 4 MVs, 4 DVsQuadratic cost function with constraintsGainSchedNon-Linear Gain SchedulingPlants with operating-point dependent gain1 Input / Interpolated PID parametersAdaptive parameter schedulingSplRangeSplit-Range ControlDual opposing actuators (Heating/Cooling)1 Input / 2 Output rangesCoordinated dual-valve travelConPerMonPerformance AnalyticsSupervisory loop monitoring1 PV, 1 SP, 1 MVHarris Index / Oscillation tracking

Verification

To confirm that the advanced control simulation operates properly:
1. Start S7-PLCSIM and download the compiled CFC charts containing ModPreCon and SIMIT plant models.
2. Launch WinCC Runtime: open the ModPreCon standard APL faceplate.
3. Verify that predicted process trajectories match future process movements.
4. Introduce a 20% step load disturbance in SIMIT: observe how ModPreCon modulates manipulated variables to decouple and stabilize controlled variables.
5. In the ConPerMon faceplate, verify that the control performance index indicates good control quality without persistent oscillations.

Limitations and Operational Precautions

Model Predictive Control blocks (ModPreCon) consume significant CPU execution time; execute them in slower cyclic tasks (e.g., OB32 @ 1000ms or OB33 @ 500ms) to avoid CPU cycle overruns.
Process step-response models must be accurately identified (using open-loop step tests or PRBS excitation) before loading model matrices into ModPreCon.
S7-PLCSIM executes instructions as fast as host PC CPU power allows, which can cause slight timing discrepancies compared to physical S7-400 hardware clocks; use SIMIT Virtual Controller for cycle-synchronous real-time emulation.
Always validate final license requirements for APC toolkits against the applicable Siemens catalog and authorized distributor quotation.

Engineer FAQ

Can I run a complete PCS 7 project without any physical hardware?

Yes. By combining S7-PLCSIM (or SIMIT Virtual Controller) with WinCC Runtime on an Engineering Station, an engineer can compile, download, execute, and visualize an entire industrial automation project entirely in software.

Where is the MPC tuning tool located in PCS 7?

PCS 7 includes the 'PCS 7 APC Tool' and SIMIT Model Identification tools, which allow engineers to import step-response test data and automatically compute the mathematical prediction matrices required by ModPreCon.

What is the difference between adaptive control and gain scheduling?

Adaptive control dynamically calculates new tuning parameters in real time based on observed error signals. Gain scheduling uses a pre-calculated look-up table or curve that shifts controller tuning based on a measured operating variable (such as production rate).