CFD-based predictive control of melt temperature in plastic injection molding

A unique method of coupling computational fluid dynamics (CFD) to model predictive control (MPC) for controlling melt temperature in plastic injection molding is presented. The methodology is based on using CFD to generate, via open-loop testing, a temperature and input dependent system model for multi-variable control of a three-heater barrel on an injection molding machine. Results clearly show the benefit of temperature and input dependent system models for MPC control, and that CFD can be used to dramatically reduce the time associated with open-loop testing through physical experiments.     

Real-time distributed control: A fuzzy and model predictive control approach for a nonlinear problem

Nowadays the study of faults and their consequences has become an issue for highly safety critical computer network systems. How to bound the effects of a fault and how to tackle them in a dynamic system is still an open field. Here, an approach to studying this problem is presented as a hybrid strategy. The use of a multi-model technique from the point of view of real-time systems and intelligent control structure is described, in order to accomplish one challenge: to overcome the problem of the presence of local faults and the respective time delays within a real-time distributed system. This approach is pursued as a reconfigurable strategy according to communication time delays within a real-time distributed system. In fact, it is pursued as a reconfigurable strategy according to communication delays and local faults where the control strategy is modified from several perspectives.     

Stochastic control for a class of random evolution models

We construct the explicit connection existing between a solvable model of the discrete velocities non-linear Boltzmann equation and the Hamilton-Bellman-Jacobi equation associated with a simple optimal control of a piecewise deterministic process. This study extends the known relation that exists between the Burgers equation and a simple controlled diffusion problem. In both cases the resulting partial differential equations can be linearized via a logarithmic transformation and hence offer the possibility to solve physically relevant non-linear field models in full generality.     

Adaptive fuzzy backstepping control for a class of MIMO switched nonlinear systems with unknown control directions

In this article, an adaptive fuzzy output tracking control approach is proposed for a class of multiple‐input and multiple‐output uncertain switched nonlinear systems with unknown control directions and under arbitrary switchings. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. A Nussbaum gain function is introduced into the control design and the unknown control direction problem is solved. Under the framework of the backstepping control design, fuzzy adaptive control and common Lyapunov function stability theory, a new adaptive fuzzy output tracking control method is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed‐loop system are bounded and the tracking error remains an adjustable neighborhood of the origin. A numerical example is provided to illustrate the effectiveness of the proposed approach. © 2015 Wiley Periodicals, Inc. Complexity 21: 155–166, 2016     

Model predictive control of nonlinear hybrid systems with discrete inputs employing a hybrid fuzzy model

The paper deals with model predictive control (MPC) of nonlinear hybrid systems with discrete inputs based on reachability analysis. In order to implement a MPC algorithm, a model of the process that we are dealing with is needed. In the paper, a hybrid fuzzy modelling approach is proposed. The hybrid system hierarchy is explained and the Takagi–Sugeno fuzzy formulation for hybrid fuzzy modelling purposes is tackled. An efficient method of identification of the hybrid fuzzy model is also discussed.

An algorithm that is–due to its MPC nature–suitable for controlling a wide spectrum of systems (provided that they have discrete inputs only) is presented.

The benefits of the algorithm employing a hybrid fuzzy model are verified on a batch reactor example. The results suggest that by suitably determining the cost function, satisfactory control can be attained, even when dealing with complex hybrid–nonlinear–stiff systems such as the batch reactor.

Finally, a comparison between MPC employing a hybrid linear model and a hybrid fuzzy model is carried out. It has been established that the latter approach clearly outperforms the approach where a linear model is used.     

Model predictive control for switched systems with a novel mixed time/event-triggering mechanism

This paper investigates observer-based model predictive control (MPC) for switched systems with a mixed time/event-triggering mechanism. The problem of predictive control that can achieve receding horizon optimization is considered and solved by minimizing an upper bound of the quadratic cost function. Since the system state may not be fully measured in practice, state observers are employed to estimate. A mixed mechanism including adaptive event-triggering and time-triggering is proposed, which can be switched determined by a threshold describing system performance to better balance system resource utilization and performance requirements. Then, a closed-loop switched system subject to networked-time-delay is modeled. Piecewise Lyapunov function technique and average dwell time approach are utilized to ensure asymptotical stability. Afterwards, MPC controller construction problem is turned into a LMIs feasibility problem. A new solving method of sufficient conditions for co-design of the state observers, feedback controllers and mixed triggering mechanism is derived. Lastly, simulation examples illustrate the correctness and advantages of research content.     

The principle of robustness in generalized predictive control

This paper deeply analyzes the closed-loop nature of GPC in the framework of internal model control (IMC) theory. A new sort of relation lies in the feedback structure so thatrobust reason can be satisfactorily explained. The result is significant because the previous conclusions are only applied to open-loop stable plant (or model).     

Event-triggered dual-mode predictive control for constrained nonlinear systems with continuous/intermittent detection

This paper investigates the problem of event-triggered model predictive control for constrained nonlinear systems. A dual-mode control strategy combined with two different event-triggered mechanisms are introduced to reduce computational and communication loads. For the event-triggered mechanisms, two cases, continuous detection and intermittent detection, are considered, respectively. In order to avoid the transmission of continuous predicted control input trajectories, the actual control signals are generated under a sample-and-hold manner. A decreasing prediction horizon is introduced to reduce the complexity of optimization problems and a tightened state constraint is designed to achieve robust constraint satisfaction. The sufficient conditions are derived to guarantee the feasibility and stability of the closed-loop system. The performance of the proposed strategy is illustrated by a simulation example.     

ON THE ROBUSTNESS OF A GENERALIZED PREDICTIVE CONTROLLER

In this paper, we present a quantitative analysis of the robustness of a generalized predictive controller. The result of stability analysis shows that, under a specific bounded modelling error, the closed-loop system is BIBO stable in the presence of unmedelled dynamics.     

Robust memory state feedback model predictive control for discrete-time uncertain state delayed systems

In this paper, we propose a memory state feedback model predictive control (MPC) law for a discrete-time uncertain state delayed system with input constraints. The model uncertainty is assumed to be polytopic, and the delay is assumed to be unknown, but with a known upper bound. We derive a sufficient condition for cost monotonicity in terms of LMI, which can be easily solved by an efficient convex optimization algorithm. A delayed state dependent quadratic function with an estimated delay index is considered for incorporating MPC problem formulation. The MPC problem is formulated to minimize the upper bound of infinite horizon cost that satisfies the sufficient conditions. Therefore, a less conservative sufficient conditions in terms of linear matrix inequality (LMI) can be derived to design a more robust MPC algorithm. A numerical example is included to illustrate the effectiveness of the proposed method.     

Resource limited event-triggered model predictive control for continuous-time nonlinear systems based on first-order hold

This paper investigates a resources-limited situation in the event-triggered model predictive control (ETMPC) for continuous-time nonlinear system with first-order hold fashion. In consideration of limited bandwidth in data transmission through wireless network under actual operation, our strategy divides the prediction horizon, and applies linear interpolation instead of zero-order hold fashion to obtain a better system performance, so that the reduction of resources and the optimization of strategy can be guaranteed. Furthermore, in actual industry processes, quadratic cost function cannot be implemented in all operations, then general cost function is adopted in this paper. Based on the first-order hold method and general cost function, the feasibility of the ETMPC algorithm and the stability of dynamical systems are analyzed. At last, a practical example is given to show the advantages of our method.     

Terminal constrained robust hybrid iterative learning model predictive control for complex time-delayed batch processes

This work mainly addresses terminal constrained robust hybrid iterative learning model predictive control against time delay and uncertainties in a class of complex batch processes with input and output constraints. In this work, an equivalently novel extended two-dimensional switched system is first constructed to represent the process model by introducing state difference, output error and new relaxation variable information. Then, a hybrid predictive updating controller is proposed and an optimal performance index function including terminal constraints is designed. Under the condition that the switching signal meets certain conditions, the solvable problem of model predictive control is realized by Lyapunov stability theory. Meanwhile, the design scheme of controller parameters is also given. In addition, the robust constraint set is adopted to overcome the disadvantage that the traditional asymptotic stability cannot converge to the origin when it involves disturbances, such that the system state converges to the constraint set and meets its expected value. Finally, the effectiveness of the proposed algorithm is verified by controlling the speed and pressure parameters of the injection molding process.     

Stabilization for a class of nonlinear networked control systems via polynomial fuzzy model approach

This article is concerned with the stabilization problem for nonlinear networked control systems which are represented by polynomial fuzzy models. Two communication features including signal transmission delays and data missing are taken into account in a network environment. To solve the network‐induced communication problems, a novel sampled‐data fuzzy controller is designed to guarantee that the closed‐loop system is asymptotically stable. The stability and stabilization conditions are presented in terms of sum of squares (SOS), which can be numerically solved via SOSTOOLS. Finally, a simulation example is provided to demonstrate the feasibility of the proposed method. © 2014 Wiley Periodicals, Inc. Complexity 21: 74–81, 2015     

一类时滞非线性系统的自适应模糊控制

对于一类SISO输入时滞已知,状态时滞不确定但有上界的能采用后推设计方法的非线性系统提出一种基于后推设计、自适应模糊控制和滑模控制的控制方案.通过状态变换,把输入时滞系统转化为无输入时滞的系统.用模糊系统来估计系统的未知连续函数,对转化后的新系统设计自适应滑模控制器,使得新系统的状态有界,通过递推证得原系统的状态半全局一致有界.     

An improved generalized predictive control algorithm based on the difference equation CARIMA model for the SISO system with known strong interference

ABSTRACT

The single input single output (SISO) system with known strong interference is widely used in various occasions. Due to its strong interference, the control accuracy is hard to guarantee. To solve this problem, an improved generalized predictive control (IGPC) algorithm is developed. The IGPC firstly builds the difference equation CARIMA (Controlled Auto-Regressive Integrated Moving-Average) model of the SISO system and then treats the system as a two input single output (TISO) system and calculates its predictive vector, then transforms it into a SISO system and uses the TISO system predictive vector to calculate the SISO system control increment. A new parameter called phase coefficient is added to inhibit the control lag. Simulations are performed to make the comparison among the traditional GPC, PID control, velocity synchronization control (VSC), fuzzy adaptive PID control (FAPID), model-based robust PID control (BPID) and the IGPC. Results show that IGPC has best performance compared to the others. Finally, experiments are developed which proved that the IGPC algorithm has a higher accuracy in the SISO system with known strong interference than that of VSC.     

Regularized Adaptation of Fuzzy Inference Systems. Modelling the Opinion of a Medical Expert about Physical Fitness: An Application

This study presents a new approach to adaptation of Sugeno type fuzzy inference systems using regularization, since regularization improves the robustness of standard parameter estimation algorithms leading to stable fuzzy approximation. The proposed method can be used for modelling, identification and control of physical processes. A recursive method for on-line identification of fuzzy parameters employing Tikhonov regularization is suggested. The power of approach was shown by applying it to the modelling, identification, and adaptive control problems of dynamic processes. The proposed approach was used for modelling of human-decisions (experience) with a fuzzy inference system and for the fuzzy approximation of physical fitness with real world medical data.     

基于T-S模型的Lurie混沌系统的模糊控制

针对Lurie混沌控制系统,进行了T-S模糊建模和模糊控制器设计,从而实现了Lurie混沌系统的稳定.在用T-S模糊模型精确重构Lurie系统结构的基础上,利用反馈同步思想,基于并行分布补偿(PDC)技术,得到了简单且易实现的控制器.仿真结果验证了该控制方法的有效性.     

Bifurcation analysis and control of a class of hybrid biological economic models

This paper systematically studies a hybrid predator–prey economic model, which is formulated by differential-difference-algebraic equations. It shows that this model exhibits two bifurcation phenomena at the intersampling instants. One is saddle–node bifurcation, and the other is singular induced bifurcation which indicates that economic profit may bring impulse at some critical value, i.e., rapid expansion of biological population in terms of ecological implications. On the other hand, for the sampling instants, the system undergoes Neimark–Sacker bifurcation at a critical value of economic profit, i.e., the increase of economic profit destabilizes the system and generates a unique closed invariant curve. Moreover, the state feedback controller is designed so that singular induced bifurcation and Neimark–Sacker bifurcation can be eliminated and the population can be driven to steady states by adjusting harvesting costs and the economic profit. At the same time, by using Matlab software, numerical simulations illustrate the effectiveness of the results obtained here.     

时变滞后系统的一种自校正混合模糊PID控制

普通模糊控制不能对时变滞后系统进行有效控制 ,甚至使系统失去稳定 .在 W.L.Bialkowski 1 983年提出的混合模糊 PID控制器的基础上 ,提出了一种自补偿混合模糊 PID控制器 ,并在此基础上提出了一种对积分系数 KI进行自校正的算法 .经 MATLAB仿真验证 ,该算法具有良好的控制品质 ,适应对象参数大范围变化的时滞系统 ,且易于工程实现 .     

水泥回转窑的模糊控制模型

水泥回转窑由于其控制量的迟滞性及不敏感性难以用传统的方法进行控制 .本文讨论用模糊控制的方法来模拟水泥回转窑的人工控制