Embedded Model Predictive Control in ABB’s Distributed Control System: A qpOASES-Based Approach

In the field of automatic control, embedded Model Predictive Control (MPC) presents significant advantages, including efficiency, constraint incorporation, and non-causal setpoint handling. Despite its potential, the implementation of MPC in resource-constrained environments remains challenging due to its computational demands. This thesis explores the design and implementation of MPC within ABB’s distributed control system ABB AbilityTM System 800xA and with focus on ABB’s new control platform. A prototype was developed employing Quadratic Programming Online Active Set Strategy (qpOASES), an active-set algorithm, to efficiently solve the quadratic programming problem inherent to MPC. This solver is optimized for high performance in real-time applications and incorporates fail-safe functionality to handle solver failures. In this thesis, the development process began with an initialMATLAB prototype for validation, allowing for iterative refinement and verification of control features. Subsequently, the algorithm was transformed into a fully integrated C++ solution, capable of running as a hardware agnostic Execution Service within ABB’s new control platform. The developed prototype was validated through real-world tests and benchmarks, demonstrating the feasibility and effectiveness of the prototype in an industrial context. This thesis effectively bridges the gap between academic research and practical industrial applications within the controller in ABB’s new control platform, laying the groundwork for future advancements in embedded MPC within this specific environment.