Model Predictive Control Applied to Constraint Handling in Active Noise and Vibration Control

Abstract

The difficulties imposed by actuator limitations in a range of active vibration and noise control problems are well recognized. This paper proposes and examines a new approach of employing model predictive control (MPC). MPC permits limitations on allowable control action to be explicitly included in the computation of an optimal control action. Such techniques have been widely and successfully applied in many other areas. However, due to the relatively high computational requirements of MPC, existing applications have been limited to systems with slow dynamics. This paper illustrates that MPC can be implemented on inexpensive hardware at high sampling rates using traditional online quadratic programming methods for nontrivial models and with significant control performance dividends.Index Terms-Active noise and vibration control, active structures, model predictive control (MPC), piezoelectric actuators.

Modeling and Supervisory Control Design for a Combined Cycle Power Plant

“…Based on the state space model (A, B, C), the future values of the plant states and outputs over the prediction horizon may be obtained sequentially as follows [30].…”

“…The objective function was represented in vector form as in paper [30]. In this form, the terminal weighting obtained from solving the algebraic Riccati equation can be added to guarantee the closed loop stability.…”

“…In order to guarantee the closed loop stability when constraints are presents, a sufficiently large prediction horizon should be selected [30].…”

Model predictive control of dynamically substructured systems with application to a servohydraulically actuated mechanical plant

“…However, the computational complexity and the required memory space can increase rapidly with the growth of the problem size (see, e.g. [29,30], for a detailed discussion on a comparison of the two control strategies). In recent years, with the development of new, efficient optimization algorithms and the rapid progress of hardware computing ability, a large number of applications of the traditional on-line MPC to fast systems have been reported in areas such as aerospace, power plants and the automotive industry (see [16] for a survey).…”

“…The on-line MPC optimization was solved by using the active set algorithm programmed in C, [28]. This code was recently implemented successfully in the application of MPC, with a prediction horizon of 10, to an active structure consisting of an SISO system with 18 states, using sampling rates up to 5 kHz on a 200 MHz DSP [29]. In our implementation, the code was embedded into an S-function in SIMULINK , which was compiled and implemented by a dSPACE c ⃝real-time control system.…”