Qibing Jin

Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties

In this paper, an improved constrained tracking control design is proposed for batch processes under uncertainties. A new process model that facilitates process state and tracking error augmentation with further additional tuning is first proposed. Then a subsequent controller design is formulated using robust stable constrained MPC optimization. Unlike conventional robust model predictive control (MPC), the proposed method enables the controller design to bear more degrees of tuning so that improved tracking control can be acquired, which is very important since uncertainties exist inevitably in practice and cause model/plant mismatches. An injection molding process is introduced to illustrate the effectiveness of the proposed MPC approach in comparison with conventional robust MPC.

Recursive least squares identification of hybrid Box–Jenkins model structure in open-loop and closed-loop

Abstract Inspired by the fact that, in order to obtain a global optimal solution, a continuous plant should be identified simultaneously with the noise model, a simple but effective identification method is firstly proposed for hybrid Box–Jenkins structure in open-loop and close-loop. Two recursive generalized extended least squares algorithms are developed for different plant models. In recursive computations, the idea of auxiliary model has been applied to make the global recursive identification possible, and the idea of delay compensation has been introduced to handle the identification of SOPDT plant model effectively. Meanwhile, the online implementation issues of recursive algorithms are discussed. The two proposed algorithms can be further extended to closed-loop systems by an appropriate closed-loop setup. The simulation examples demonstrate the accuracy and effectiveness of the proposed method in open-loop and closed-loop.

Iteratively reweighted correlation analysis method for robust parameter identification of multiple-input multiple-output discrete-time systems

In the engineering practices, the distributions of measurements are non-Gaussian as they contain outliers. As some slight deviations from the Gaussian assumption would probably cause the performance of an estimator to degrade significantly, a novel iteratively reweighted correlation analysis method is proposed for robust parameter estimation of multiple-input multiple-output (MIMO) systems, in the presence of Students t-noises. The iterative method achieves good robustness and high efficiency by the combination of multivariable correlation analysis and t-distribution based M-estimators. The appropriate updating weights are able to enter into the sample cross-correlation function, so that the heavy tails are lowered, and the impact of outliers is weakened to the greatest extent. Based on the robust finite impulse response models, the identification procedure is then to reconstruct the noise-free estimates to identify the parameters of an MIMO system. The theoretical discussions and simulation results demonstrate that the proposed method works well.

Improved fuzzy PID controller design using predictive functional control structure

In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control.

Multivariable design of improved linear quadratic regulation control for MIMO industrial processes

In this study, a multivariable linear quadratic control system using a new state space structure was developed for the chamber pressure in the industrial coke furnace. Such processes typically have complex and nonlinear dynamic behavior, which causes the performance of controllers using conventional design and tuning to be poor or to require significant effort in practice. The process model is first treated into a new state space form and the implementation of linear quadratic control is designed using this new model structure. Performance in terms of regulatory/servo, disturbance rejection and measurement noise problems were all compared with the recent model predictive control strategy. Results revealed that the control system showed more robustness and improved the closed-loop process performance under model/process mismatches.

A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise

In this paper, we study the system identification of multi-input multi-output (MIMO) Hammerstein processes under the typical heavy-tailed noise. To the best of our knowledge, there is no general analytical method to solve this identification problem. Motivated by this, we propose a general identification method to solve this problem based on a Gaussian-Mixture Distribution intelligent optimization algorithm (GMDA). The nonlinear part of Hammerstein process is modeled by a Radial Basis Function (RBF) neural network, and the identification problem is converted to an optimization problem. To overcome the drawbacks of analytical identification method in the presence of heavy-tailed noise, a meta-heuristic optimization algorithm, Cuckoo search (CS) algorithm is used. To improve its performance for this identification problem, the Gaussian-mixture Distribution (GMD) and the GMD sequences are introduced to improve the performance of the standard CS algorithm. Numerical simulations for different MIMO Hammerstein models are carried out, and the simulation results verify the effectiveness of the proposed GMDA.

Improved Minmax Control for Industrial Networked Systems Over Imperfect Communication

To cope with control issues of the networked control system under packet losses and uncertainty, an improved state space model-based linear quadratic (LQ) control is proposed in this paper. By extending the state vector with the set-point tracking error, the novel state space model provides more degrees of freedom for the relevant controller design through adjusting the corresponding weighting coefficients of the state variables and tracking error separately. Under such advantages, the proposed LQ scheme provides improved control performance compared with conventional LQ strategy in which only the original state variables can be weighted. A case study on the temperature regulation of an industrial coke furnace under uncertainty and packet losses is introduced to verify the effectiveness of the proposed control scheme in comparison with conventional LQ strategy.