Title :
Input-constrained adaptive GPC for simple industrial plant
Author_Institution :
Electron. & Control Group, Teesside Univ., Middlesbrough, UK
Abstract :
One significant drawback of practical MPC schemes is the large computational burden, especially in adaptive and constrained situations. In this paper, previous work by the author towards a computationally efficient self-tuning Generalized Predictive Control scheme for low-order time delayed industrial plant is extended to include rate and amplitude constraints on the plant input. The paper discusses a scheme that has been optimized for real-time implementation on small, low-cost embedded processors, which avoids the need for a full QP procedure by optimizing over a short constraint horizon with a decomposed LQR terminal cost. Preliminary simulation and hardware-in-the-loop based experimental results indicate that the resulting scheme is indistinguishable from a full QP solution, and requires a fraction of the implementation overhead.
Keywords :
adaptive control; computational complexity; delays; embedded systems; industrial plants; linear quadratic control; predictive control; real-time systems; self-adjusting systems; LQR terminal cost; MPC schemes; QP procedure; adaptive situations; computationally efficient self-tuning generalized predictive control scheme; constrained situations; constraint horizon; hardware-in-the-loop based experimental results; industrial plant; input-constrained adaptive GPC; low-cost embedded processors; low-order time delayed industrial plant; Adaptation models; Computational modeling; Delay; Optimal control; Predictive control; Real-time systems; Adaptive GPC; Constrained LQR; Input Constraints; Real-Time Control;
Conference_Titel :
Automation and Computing (ICAC), 2012 18th International Conference on
Conference_Location :
Loughborough
Print_ISBN :
978-1-4673-1722-1