Multiplexed Model Predictive Control Research Articles

Multiplexed model predictive control for active vehicle suspensions

Multiplexed model predictive control (MMPC) is a recently proposed efficient model predictive control (MPC) algorithm, which can effectively reduce the computational burden of the online optimisation in MPC implementation by updating the control inputs in an asynchronous manner. This paper investigates the application of MMPC in active vehicle suspension design. An MMPC controller integrated with soft constraints and a Kalman filter is proposed based on a full-car model. Ride comfort, roadholding and suspension deflection are considered in this paper, where ride comfort and roadholding are formulated as a quadratic cost function in terms of sprung mass accelerations and tyre deflections, while suspension deflection performance is formulated as a hard constraint. The saturation of the actuator force is also considered and formulated as a hard constraint as well. Numerical simulation is performed with respect to different choices of weighting factors, vehicle speeds and control horizons. The results show that the overall performance of ride comfort and roadholding can be improved significantly by employing MMPC and the average time taken by MMPC to solve the individual quadratic programming problem is considerably smaller than that of the conventional MPC, which effectively demonstrate the effectiveness of the proposed method.

Integral-Square-Error Performance of Multiplexed Model Predictive Control

It is well-known that faster sampling increases computational load but gives better performance. Multiplexed Model Predictive Control (MMPC) has been proposed recently. Its motivation was to reduce real-time computational load. The reduction in computational load can be used gainfully to increase sampling rate and improve performance. Hence, in this paper, we derive a formula to compute the Integral-Square-Error (ISE) performance of a MMPC controlled system. Given the plant and disturbance models, the ISE formula derived allows one to investigate how the ISE changes with control design parameters, such as the sampling interval and control weighting. This enables one to select, for example, a suitable sampling interval for the MMPC design to achieve the desired ISE performance. In addition, we validated the ISE formula on a multizone semiconductor manufacturing thermal process.

Robust Decentralized Formation Flight Control

Motivated by the idea of multiplexed model predictive control (MMPC), this paper introduces a new framework for unmanned aerial vehicles (UAVs) formation flight and coordination. Formulated using MMPC approach, the whole centralized formation flight system is considered as a linear periodic system with control inputs of each UAV subsystem as its periodic inputs. Divided into decentralized subsystems, the whole formation flight system is guaranteed stable if proper terminal cost and terminal constraints are added to each decentralized MPC formulation of the UAV subsystem. The decentralized robust MPC formulation for each UAV subsystem with bounded input disturbances and model uncertainties is also presented. Furthermore, an obstacle avoidance control scheme for any shape and size of obstacles, including the nonapriorily known ones, is integrated under the unified MPC framework. The results from simulations demonstrate that the proposed framework can successfully achieve robust collision-free formation flights.

Multiplexed MPC for Multizone Thermal Processing in Semiconductor Manufacturing

It is known that a faster sampling rate allows a controller to react to unmeasurable disturbances faster. Adding feed- forward could decrease the effect of disturbance significantly, but feedback control is still required to cope with modelling errors and unknown disturbances. A variant of model predictive control (MPC), called multiplexed MPC (MMPC), has been proposed recently. The motivation for MMPC was to reduced real-time computational load and hence allow faster sampling on multivariable systems. In this brief, we report the first successful application of MMPC on a semiconductor manufacturing process. MMPC with feedforward has been demonstrated experimentally on a multizone bake plate application. While most of the temperature drop will be compensated by feedforward control, feedback provided by MMPC will cope with the errors due to warped wafers. This result is important for the semiconductor wafer baking process, because temperature nonuniformity will affect critical dimension of the wafer.