Hailei Jiang

Adaptive Stable PID Controller with Parallel Feedforward Compensator

In this paper, a new design method of adaptive PID controller is proposed. The method utilizes the plant characteristics called almost strict positive realness (ASPR) so that the stability of the adaptive PID control system is always guaranteed. The design scheme is derived for the design of adaptive tracking PID control system. Here it is also proposed a new design procedure of the parallel feedforward compensator (PFC) which actually guarantees the ASPRness of the controlled plant. The result is examined by applying it to the design of the first order with time delay process system. The effectiveness of the method is examined through simulations and experiments using thermal pilot plant

Detection and isolation of model-plant mismatch for multivariate dynamic systems

Abstract This paper proposes a new scheme to detect and isolate model-plant mismatch (MPM) for multivariate dynamic systems. The background of our study is the increasing demands on MPM detection and isolation for performance assessment of model predictive controllers (MPCs). In this paper, the MPM problem is formulated in terms of discrete-time state space models which are widely used in MPCs. Three MPM detection indices (MDIs) are proposed to detect the MPM. Also a logic framework is proposed to isolate the system matrices that have MPM. A numerical example is presented to demonstrate the applicability of the proposed scheme.

Performance Assessment and Model Validation of Two Industrial MPC Controllers

Abstract This paper presents two case studies on the performance evaluation and model validation of two industrial multivariate model predictive control (MPC) based controllers at Suncor Energy Inc., Fort McMurray, Canada: (1) a 7 controlled variable (CV), 3 manipulated variables (MV) kerosene hydrotreating unit (KHU) with three measured disturbance variables that are used for feedforward control; and (2) an 8 CV, 4 MV naphtha hydrotreating unit (NHU) with 5 measured disturbances. The NHU and KHU controllers are implemented on the product stripping distillation towers. The first case study focuses on potential limits to control performance due to constraints and limits set at the time of controller commissioning. The root causes of sub-optimal performance of KHU are successfully isolated. Data from the NHU unit with MPC on and with MPC off are analyzed to obtain and compare several different measures of multivariate controller performance. Model quality assessment for the two MPCs are performed. A new model index is proposed to have a measure of simulation ability and prediction ability of a model. Open-loop identification of KHU and closed-loop identification of NHU are conducted using the asymptotic method (ASYM).

Detection and Isolation of Model-Plant Mismatch for Multivariate Dynamic Systems

: This paper proposes a new scheme to detect and isolate model-plant mismatch (MPM) for multivariate dynamic systems. The background of our study is the increasing demands on MPM detection and isolation for performance assessment of model predictive controllers (MPCs). In this paper, the MPM problem is formulated in terms of discrete-time state space models which are widely used in MPCs. Three MPM detection indices (MDIs) are proposed to detect the MPM. Also a logic framework is proposed to isolate the system matrices that have MPM. A numerical example is presented to demonstrate the applicability of the proposed scheme.

DETECTION AND DIAGNOSIS OF PLANT-WIDE OSCILLATIONS USING THE SPECTRAL ENVELOPE METHOD

Abstract Plant-wide oscillations are common in many processes. Their effects propagate to many units and may impact the overall process performance. It is important to detect and diagnose the oscillations early in order to rectify the situation. This paper proposes a new procedure to detect and diagnose plant-wide oscillations. A technique called spectral envelope is used to detect the oscillations. Two kinds of plots - scaling and power plots - are proposed to identify the variables exhibiting common oscillation(s). These plots are also useful in isolating the key variables as the candidates of the root cause. An industrial case study is presented to demonstrate the applicability of the proposed procedure.