Discrete event-driven model predictive control for real-time work-in-process optimization in serial production systems

Advanced technologies (e.g., distributed sensors, RFID, and auto-identification) can gather processing information (e.g., system status, uncertain machine breakdown, and uncertain job demand) accurately and in real-time. By combining this transparent, detailed, and real-time production information with production system physical properties, an intelligent event-driven feedback control can be designed to reschedule the release plan of jobs in real-time without work-in-process (WIP) explosion. This controller should obtain the operational benefits of pull (e.g., Toyota’s Kanban system) and still develop a coherent planning structure (e.g., MRPII). This paper focuses on this purpose by constructing a discrete event-driven model predictive control (e-MPC) for real-time WIP (r-WIP) optimization. The discrete e-MPC addresses three key modelling problems of serial production systems: (1) establish a max-plus linear model to describe dynamic transition behaviors of serial production systems, (2) formulate a model-based event-driven production loss identification method to provide feedback signals for r-WIP optimization, and (3) design a discrete e-MPC to generate the optimal job release plan. Based on a case from an industrial sewing machine production plant, the advantages of the discrete e-MPC are compared with the other two r-WIP control strategies: Kanban and MRPII. The results show that the discrete e-MPC can rapidly and cost-effectively reconfigure production logic. It can decrease the r-WIP without deteriorating system throughput. The proposed e-MPC utilizes the available transparent sensor data to facilitate real-time production decisions. The effort is a step forward in smart manufacturing to achieve improved system responsiveness and efficiency.     

Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model

We investigated the possibility of applying a hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID controller to a nonlinear pneumatic artificial muscle (PAM) robot arm to improve its joint angle position output performance. The proposed hybrid inverse NARX fuzzy-PID controller is implemented to control a PAM robot arm that is subjected to nonlinear systematic features and load variations in real time. First the inverse NARX fuzzy model is modeled and identified by a modified genetic algorithm (MGA) based on input/output training data gathered experimentally from the PAM system. Second the performance of the optimized inverse NARX fuzzy model is experimentally demonstrated in a novel hybrid inverse NARX fuzzy-PID position controller of the PAM robot arm. The results of these experiments demonstrate the feasibility and benefits of the proposed control approach compared to traditional PID control strategies. Consequently, the good performance of the MGA-based inverse NARX fuzzy model in the proposed hybrid inverse NARX fuzzy-PID position control of the PAM robot arm is demonstrated. These results are also applied to model and to control other highly nonlinear systems.     

A hybrid Gaussian process approach to robust economic model predictive control

This paper proposes a hybrid Gaussian process (GP) approach to robust economic model predictive control under unknown future disturbances in order to reduce the conservatism of the controller. The proposed hybrid GP is a combination of two well-known methods, namely, kernel composition and nonlinear auto-regressive. A switching mechanism is employed to select one of these methods for disturbance prediction after analyzing the prediction outcomes. The hybrid GP is intended to detect not only patterns but also unexpected behaviors in the unknown disturbances by using past disturbance measurements. A novel forgetting factor concept is also utilized in the hybrid GP, giving less weight to older measurements, in order to increase prediction accuracy based on recent disturbances values. The detected disturbance information is used to reduce prediction uncertainty in economic model predictive controllers systematically. The simulation results show that the proposed method can improve the overall performance of an economic model predictive controller compared to other GP-based methods in cases when disturbances have discernible patterns.     

一类仿射非线性网络控制系统的稳定性分析

利用采样数字控制系统的方法分析了一类混杂动态系统模型描述的仿射非线性网络控制系统的稳定性问题.针对一类仿射非线性对象和线性数字控制器组成的网络控制系统,考虑了网络诱导延时对系统稳定性的影响,得到了仿射非线性网络控制系统一致渐近稳定的条件．仿真实例验证了理论分析的正确性．     

A Study on Kinematics and Workspace Determination of a General 6-<Emphasis Type="Underline">P</Emphasis> US Robot

This paper proposes the novel adaptive neural network (ADNN) compliant force/position control algorithm applied to a highly nonlinear serial pneumatic artificial muscle (PAM) robot as to improve its compliant force/position output performance. Based on the new adaptive neural ADNN model which is dynamically identified to adapt well all nonlinear features of the 2-axes serial PAM robot, a new hybrid adaptive neural ADNN-PID controller was initiatively implemented for compliant force/position controlling the serial PAM robot system used as an elbow and wrist rehabilitation robot which is subjected to not only the internal coupled-effects interactions but also the external end-effecter contact force variations (from 10[N] up to critical value 30[N]). The experiment results have proved the feasibility of the new control approach compared with the optimal PID control approach. The novel proposed hybrid adaptive neural ADNN-PID compliant force/position controller successfully guides the upper limb of subject to follow the linear and circular trajectories under different variable end-effecter contact force levels.     

Predictive human operator model to be utilized as a controller using linear,neuro-fuzzy and fuzzy-ARX modeling techniques

Modeling human operator's behavior as a controller in a closed-loop control system recently finds applications in areas such as training of inexperienced operators by expert operator's model or developing warning systems for drivers by observing the driver model parameter variations. In this research, first, an experimental setup has been developed for collecting data from human operators as they controlled a nonlinear system. Appropriate reference signals and scenarios were designed according to the system identification and human operator modeling theory, to collect data from subjects. Different modeling schemes, namely ARX models as linear approach, and adaptive-network-based fuzzy inference system (ANFIS) as intelligent modeling approach have been evaluated. A hybrid modeling method, fuzzy-ARX (F-ARX) model, has been developed and its performance was found to be better in terms of predicting human operator's control actions as well as replacing the operator as a stand-alone controller. It has been concluded that F-ARX models can be a good alternative for modeling the human operator.     

Fuzzy model predictive control

A fuzzy model predictive control (FMPC) approach is introduced to design a control system for a highly nonlinear process. In this approach, a process system is described by a fuzzy convolution model that consists of a number of quasi-linear fuzzy implications. In controller design, prediction errors and control energy are minimized through a two-layered iterative optimization process. At the lower layer, optimal local control policies are identified to minimize prediction errors in each subsystem. A near optimum is then identified through coordinating the subsystems to reach an overall minimum prediction error at the upper layer. The two-layered computing scheme avoids extensive online nonlinear optimization and permits the design of a controller based on linear control theory. The efficacy of the FMPC approach is demonstrated through three examples     

Theoretical analysis of unconstrained nonlinear model predictive control

Model predictive control (MPC) is a well-established controller design strategy for linear process models. Because many chemical and biological processes exhibit significant nonlinear behaviour, several MPC techniques based on nonlinear process models have recently been proposed. The most significant difference between these techniques is the computational approach used to solve the nonlinear model predictive control (NMPC) optimization problem. Consequently, analysis of NMPC techniques is often connected to the computational approach employed. In this paper, a theoretical analysis of unconstrained NMPC is presented that is independent of the computational approach. A nonlinear discrete-time, state-space model is used to predict the effects of future inputs on future process outputs. It is shown that model inverse, pole-placement, and steady-state controllers can be obtained by suitable selection of the control and prediction horizons. Moreover, the NMPC optimization problem can be modified to yield nonlinear internal model control (NIMC). The computational requirements of NIMC are considerably less than NMPC, but the NIMC approach is currently restricted to nonlinear models with well-defined and stable inverses. The NIMC controller is shown to provide superior servo and regulatory performance to a linear IMC controller for a continuous stirred tank reactor.     

Fuzzy control of an ANFIS model representing a nonlinear liquid-level system

This paper aims to serve two main objectives; one is to demonstrate the modelling capabilities of a neuro-fuzzy approach, namely ANFIS (adaptive-network based fuzzy inference system) to a nonlinear system; and the other is to design a fuzzy controller to control such a system. The nonlinear system, which is a liquid-level system, is represented first by its mathematical model and then by ANFIS architecture. The ANFIS model is formed by means of input–output data set taken from the mathematical model. Then a PID-type fuzzy controller, which linguistically approximates the classical three-term compensation, was designed to control the system represented by both its mathematical and ANFIS models in order to perform an agreement comparison between them. It is shown that the ANFIS architecture can model a nonlinear system very accurately by means of input–output pairs obtained either from the actual system or its mathematical model. It is also shown that such a system can be controlled effectively by a fuzzy controller.     

A hybrid model predictive control strategy for nonlinear plant-wide control

A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy flows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlinear subsystems. A simple controller coordination strategy that counteracts interaction effects is proposed for the case of one linear subsystem and one nonlinear subsystem. A reactor/separator process with recycle is used to compare the hybrid method to conventional LMPC and NMPC techniques.     

Observer‐based control of a photovoltaic DC–DC buck converter: HDS approach

In this paper, a new observer‐based controller is proposed for a photovoltaic DC – DC buck converter; both photovoltaic (PV) voltage and current regulation are considered. In order to deal with the complex and nonlinear PV mathematical model and adapt it to the control purpose, a hybrid PV current observer model is proposed; three modes are defined and the stability of the observer is discussed using the hybrid dynamical system approach (HDS). The observer‐based controller is designed for both voltage and current regulation of the PV system; the closed loop of the full system stability is demonstrated through Lyapunov analysis. Experimental results are also presented showing the feasibility of the proposed observer‐based controller.     

Nonlinear internal model controller design for wastegate control of a turbocharged gasoline engine

Internal Model Control (IMC) has a great appeal for automotive powertrain control in reducing the control design and calibration effort. Motivated by its success in several automotive applications, this work investigates the design of nonlinear IMC for wastegate control of a turbocharged gasoline engine. The IMC design for linear time-invariant (LTI) systems is extended to nonlinear systems. To leverage the available tools for LTI IMC design, the quasi-linear parameter-varying (quasi-LPV) models are explored. IMC design through transfer function inverse of the quasi-LPV model is ruled out due to parameter variability. A new approach for nonlinear inversion, referred to as the structured quasi-LPV model inverse, is developed and validated. A fourth-order nonlinear model which sufficiently describes the dynamic behavior of the turbocharged engine is used as the design model in the IMC structure. The controller based on structured quasi-LPV model inverse is designed to achieve boost-pressure tracking. Finally, simulations on a validated high-fidelity model are carried out to show the feasibility of the proposed IMC. Its closed-loop performances are compared with a well-tuned PI controller with extensive feedforward and anti-windup built in. Robustness of the nonlinear IMC design is analyzed using simulations.     

Linear parameter varying PID controller design for charge control of a spark-ignited engine

This paper presents the design and experimental test of a fixed-structure LPV controller for the charge control of a spark-ignition engine. A nonlinear model of the plant is transformed into an affine LPV model in the form of an LFT representation. Using a hybrid evolutionary-algebraic synthesis approach that combines LMI techniques based on K-S iteration with evolutionary search, a scheduled PID controller is designed. To reduce conservatism, the technique of quadratic separators is used in the analysis step. To improve tracking behavior, the gain scheduled feedback controller is supported by an LTI feedforward controller. The controller has been implemented on a standard electronic control unit, and experimental results on a test car illustrate that it meets the performance requirements in a wide range of operation.     

Optimal control of a nonlinear fed-batch fermentation process using model predictive approach

Bioprocesses are involved in producing different pharmaceutical products. Complicated dynamics, nonlinearity and non-stationarity make controlling them a very delicate task. The main control goal is to get a pure product with a high concentration, which commonly is achieved by regulating temperature or pH at certain levels. This paper discusses model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor. The novel approach used here is to use the inverse of penicillin concentration as a cost function instead of a common quadratic regulating one in an optimization block. The result of applying the obtained controller has been displayed and compared with the results of an auto-tuned PID controller used in previous works. Moreover, to avoid high computational cost, the nonlinear model is substituted with neuro-fuzzy piecewise linear models obtained from a method called locally linear model tree (LoLiMoT).     

Application of gap metric to model bank determination in multilinear model approach

This paper presents a gap metric based method which aims to perform the operating range decomposition and the minimum linear model bank determination of a nonlinear system when multilinear model approach is employed to design a controller for this nonlinear system. For a prescribed distance level, the minimum linear model bank determined by the proposed method can provide sufficient information for multilinear model controller design of the nonlinear system. To illustrate the usefulness of the proposed method, two examples of nonlinear systems are presented. Moreover, a mixed logical dynamical model-based MPC (MLD–MPC) controller is designed based on the minimum model bank. Simulations confirm the method for selecting linear model bank in multilinear model approach.     

Adaptive restricted trajectory tracking for a non-minimum phase hypersonic vehicle model

The design of a nonlinear robust controller for a non-minimum phase model of an air-breathing hypersonic vehicle is presented in this work. When flight-path angle is selected as a regulated output and the elevator is the only control surface available for the pitch dynamics, longitudinal models of the rigid-body dynamics of air-breathing hypersonic vehicles exhibit unstable zero-dynamics that prevent the applicability of standard inversion methods for control design. The approach proposed in this paper uses a combination of small-gain arguments and adaptive control techniques for the design of a state-feedback controller that achieves asymptotic tracking of a family of velocity and flight-path angle reference trajectories belonging to a given class of vehicle maneuvers, in spite of model uncertainties. The method reposes upon a suitable redefinition of the internal dynamics of a control-oriented model of the vehicle dynamics, and uses a time-scale separation between the controlled variables to manage the peaking phenomenon occurring in the system. Simulation results on a full nonlinear vehicle model that includes structural flexibility illustrate the effectiveness of the methodology.     

Multiple model adaptive control for a class of nonlinear systems with unknown control directions

This study introduces an improved multiple model adaptive control (MMAC) algorithm for a class of nonlinear discrete-time systems. The controller consists of a linear direct adaptive controller, a neural network-based nonlinear direct adaptive controller and a switching mechanism. The assumption of the nonlinear term is relaxed by incorporating a parameter estimator with an augmented error. The control direction of the system is not required to be known by employing a linear direct adaptive controller with the discrete Nussbaum gain and future output predictions. The stability of the closed-loop systems applying the proposed MMAC method is proved and the improved transient performance of the system is illustrated by the simulation results.     

间歇精馏过程的模糊逻辑与增益自调整PID混合控制

针对间歇精馏过程的强非线性和非平稳时变特性,结合模糊逻辑控制和增益自调整PID控制的优点,提出了一种模糊逻辑和增益自调整PID混合控制的先进控制策略,详细推导了其控制算法,设计了相应的控制器,并在EuroBEEB工控机上用实时BASIC语言编程实现,对一套甲醇/水二元间歇精馏塔的塔顶浓度进行了推断控制实验,获得了比单独采用模糊逻辑控制时更好的控制结果。这说明,模糊逻辑和增益自调整PID混合控制是强非线性和非平稳时变过程的一种有效控制策略。     

Hybrid adaptive fuzzy identification and control of nonlinearsystems

We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems. We then propose a hybrid adaptive (HA) law for adjusting the parameters. The HA law utilizes two types of errors in the adaptive system, the tracking error and the modeling error. Performance analysis using a Lyapunov synthesis approach proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence. Furthermore, this is achieved at negligible increased implementation cost or computational complexity. We prove a theorem that shows the properties of this hybrid adaptive fuzzy control system, i.e., bounds for the integral of the squared errors, and the conditions under which these errors converge asymptotically to zero are obtained. Finally, we apply the hybrid adaptive fuzzy controller to control a chaotic system, and the inverted pendulum system