Xisong Chen

Generalized Extended State Observer Based Control for Systems With Mismatched Uncertainties

The standard extended state observer based control (ESOBC) method is only applicable for a class of single-input-single-output essential-integral-chain systems with matched uncertainties. It is noticed that systems with nonintegral-chain form and mismatched uncertainties are more general and widely exist in practical engineering systems, where the standard ESOBC method is no longer available. To this end, it is imperative to explore new ESOBC approach for these systems to extend its applicability. By appropriately choosing a disturbance compensation gain, a generalized ESOBC (GESOBC) method is proposed for nonintegral-chain systems subject to mismatched uncertainties without any coordinate transformations. The proposed method is able to extend to multi-input-multi-output systems with almost no modification. Both numerical and application design examples demonstrate the feasibility and efficacy of the proposed method.

Disturbance Observer-Based Control: Methods and Applications

Due to its abilities to compensate disturbances and uncertainties, disturbance observer based control (DOBC) is regarded as one of the most promising approaches for disturbance-attenuation. One of the first books on DOBC, Disturbance Observer Based Control: Methods and Applications presents novel theory results as well as best practices for applications in motion and process control that have already benefited numerous organizations.Supplying authoritative guidance in the areas of disturbance estimation and compensation for practical engineering systems, the book includes coverage of theoretic methods and practical applications of disturbance estimation and compensation for control systems through a DOBC approach. It considers applications in flight control systems, motion control systems, and process control systems. Supplies an authoritative overview of disturbance observer based control approaches Reports on recent developments in disturbance estimation techniques Considers matched and mismatched disturbance/uncertainty attenuation for DOBC Illustrates applications of the methods covered with detailed engineering case studies Filled with valuable insights gathered over decades of research by the authors, this book provides time- and stress-saving guidance for anyone interested in the theory and method research of DOBC. Using typical engineering examples, the text provides readers with an understanding of recent developments in DOBC as well as the tools required to make the most of this promising approach to disturbance-attenuation.

Supervisory expert control for ball mill grinding circuits

Stable control of particle size in ball mill grinding circuits is of great importance for the recovery of the valuable minerals, and the maximization of mill capacity is another main consideration in concentration plants. A supervisory expert control strategy is proposed to control product particle size and to improve fresh ore feed tonnage in grinding circuits. Expert control in supervisory level is developed to optimize the set-points for controllers in regulatory level. Mill solid concentration variable-ratio control, sump dilution water flowrate single-loop control and sump level loose control are carried out in regulatory level to reach the control targets. While the set-point modification by expert control for dilution water controller demonstrates good dynamic performance, the set-point adjustment for feed rate controller ensures a long-term stableness of particle size even ore hardness has changed. An increase of 10% in feed tonnage was achieved relative to other grinding circuits of similar capacity, running in parallel. More than four years operation demonstrates the control strategys practicality and reliability.

Expert system based adaptive dynamic matrix control for ball mill grinding circuit

Ball mill grinding circuit is a multiple-input multiple-output (MIMO) system characterized with couplings and nonlinearities. Stable control of grinding circuit is usually interrupted by great disturbances, such as ore hardness and feed particle size, etc. Conventional model predictive control usually cannot capture the nonlinearities caused by the disturbances in real practice. Multiple models based adaptive dynamic matrix control (ADMC) is proposed for the control of ball mill grinding circuit. The novelty of the strategy lies in that intelligent expert system is developed to identify the current ore hardness and then select a proper model for ADMC. Compared with the various nonlinear DMC strategies, the approach can synthesize and analyze as many variables and status as possible to adequately and reliably identify the process conditions, and it does not introduce additional computational complexity, which makes it readily available to the industrial practitioner. Simulation results and industrial applications demonstrate the effectiveness and practicality of this control strategy.

Adaptive speed control of a PMSM servo system using an RBFN disturbance observer

In order to improve the disturbance rejection property of Permanent-Magnet Synchronous Motor servo systems, a novel adaptive composite control method for the speed regulation problem is proposed in this paper. The composite controller is composed of a proportional feedback controller and a feedforward compensation based on a Radial Basis Function Network Disturbance Observer (P+RBFNDOB). The RBFNDOB is designed to estimate the lumped disturbances, including external disturbances and internal disturbances caused by parameter variations, and the estimation value is used for feedforward compensation design. Different from traditional Linear DOB (LDOB), an RBFN is used to approximate the inverse model of the system instead of selecting the inverse of the nominal model as in LDOB. By using an on-line learning algorithm, the identified inverse model can track the variations of the real plant. Thus, the RBFNDOB can still observe the disturbances when the parameters of the system vary in a wide range, while conventional DOB may not be suitable in such situations. So the composite controller obtained is inherently robust against parameter variations and external disturbances. A nearest neighbour clustering algorithm combining crude regulation and fine regulation is introduced as the on-line learning method to simplify the network structure and accelerate the learning speed. Rigorous analysis is also given to show why the RBFNDOB can effectively suppress the lumped disturbances of a closed-loop system. Simulation comparisons with two other methods, the composite control method with proportional feedback plus feedforward compensation based on LDOB (P+LDOB) and the conventional PI control method, verify the effectiveness of the proposed method.

RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances

An adaptive control strategy combining neural network inverse controller (NNIC) with RBFN disturbance observer (RBFNDOB) is developed for a multi-input-multi-output (MIMO) system with non-minimum phase, internal and external disturbances in this paper. Since the inverse model of system is unstable due to the non-minimum phase, a pseudo-plant is constructed, then the RBFN is used to identify the inverse model of pseudo-plant, which can track the parameter variations of system. By copying the structure and parameters of the identifier, the NNIC is obtained. Cascading the NNIC with the original plant, the MIMO system can be decoupled and linearized into independent SISO systems. For the independent decoupled system, the RBFNDOB employs a RBFN to observe the external disturbances and this estimate value is used as a feed-forward compensation term in controller. The case study on ball mill grinding circuit is presented. The effectiveness of the proposed method is demonstrated by simulation results and comparisons.

Fuzzy Logic Based On-Line Efficiency Optimization Control of a Ball Mill Grinding Circuit

Grinding circuit must provide stable particle size distribution and should also operate in a way to maximize mill efficiency. Fuzzy logic based on-line optimization control integrated in an expert system was developed to control product particle size while enhancing mill efficiency in a ball mill grinding circuit. In the supervisory level, fuzzy logic control determined the optimum set-points for the controllers in the regulatory level. The whole system not only ensured a long-term stableness of particle size even ore hardness has changed but also increased the mill efficiency more than 8 percent in practical application compared with conventional control. More than half a years industrial operation demonstrates the practicality, reliability and effectiveness of the suggested control strategy.

Disturbance observer enhanced model predictive control with experimental studies

A disturbance observer enhanced model predictive control approach is addressed in this paper for the typical industrial process control systems. The proposed method is applied to a practical level tank system subject to severe disturbances. Both simulation and experimental results show that the proposed method significantly improves the disturbance attenuation property of the model predictive control scheme.

Neural-network-based composite disturbance rejection control for a distillation column

Binary distillation columns are essentially multi-variable systems with couplings, non-minimum phase characteristics, model mismatches and various external disturbances. To get the desired top (distillate) and bottom product composition, a composite disturbance rejection control strategy using a radial basis function network (RBFN) is proposed in this paper. The composite controller includes neural network inverse controller (NNIC) and neural network disturbance observer (NNDOB) both using the inverse model of system which is identified by the RBFN. The stability of the identified inverse model is proved, and a rigorous analysis is also given to show why the NNDOB can effectively suppress the disturbances. Performances of the proposed scheme are compared with PID and NNIC without disturbance compensation in three cases by simulation studies. The simulations demonstrate the feasibility, effectiveness and disturbance rejection property of the proposed method in controlling the product composition of the binary distillation columns.

An improved control strategy for ball mill grinding circuits

An improved control strategy is proposed to control ball mill grinding circuits for energy saving and pollution reduction. A two-layer optimization architecture combined by particle size optimization layer and energy optimization layer is developed, where the optimal particle size set-point is calculated first, followed by the energy optimization step. A control method adaptive to the wear of liner in ball mills is also proposed. Simulation studies demonstrate that the fresh ore feed rate has been increased and the energy efficiency has been increased.