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1.
Complex processes are naturally suitable to be controlled in a decentralized framework: centralized control solutions are often unfeasible in dealing with large scale plants and they are computationally prohibitive when the processes are too fast for the existing computational resources. In these cases, the resulting control problem is usually split into many smaller subproblems and the global requirements are guaranteed by means of a proper coordination. A coordination strategy based on a networked decentralized Model Predictive Control is proposed in this paper for improving the global control performances. The innovative solution is based on independent agents and on a local area network used for exchanging a reduced set of information. The proposed architecture guarantees satisfactory performance under strong interactions among subsystems. A stability analysis is presented for the unconstrained decentralized case and the provided stability results are employed for tuning the decentralized controller. Numerical simulations are given for testing and validating the proposed technique.  相似文献   

2.
An approximation based adaptive neural decentralized output tracking control scheme for a class of large-scale unknown nonlinear systems with strict-feedback interconnected subsystems with unknown nonlinear interconnections is developed in this paper. Within this scheme, radial basis function RBF neural networks are used to approximate the unknown nonlinear functions of the subsystems. An adaptive neural controller is designed based on the recursive backstepping procedure and the minimal learning parameter technique. The proposed decentralized control scheme has the following features. First, the controller singularity problem in some of the existing adaptive control schemes with feedback linearization is avoided. Second, the numbers of adaptive parameters required for each subsystem are not more than the order of this subsystem. Lyapunov stability method is used to prove that the proposed adaptive neural control scheme guarantees that all signals in the closed-loop system are uniformly ultimately bounded, while tracking errors converge to a small neighborhood of the origin. The simulation example of a two-spring interconnected inverted pendulum is presented to verify the effectiveness of the proposed scheme.  相似文献   

3.
L. Magni  R. Scattolini 《Automatica》2006,42(7):1231-1236
This note presents a stabilizing decentralized model predictive control (MPC) algorithm for nonlinear discrete time systems. No information is assumed to be exchanged between local control laws. The stability proof relies on the inclusion of a contractive constraint in the formulation of the MPC problem.  相似文献   

4.
A closed-loop system is developed to control the weld fusion, which Is specified by the top-side and back-side bead widths of the weld pool. Because in many applications only a top-side sensor is allowed, which is attached to and moves with the welding torch, an image processing algorithm and neurofuzzy model have been incorporated to measure and estimate the top-side and back-side bead widths based on an advanced top-side vision sensor. The welding current and speed are selected as the control variables. It is found that the correlation between any output and input depends on the value of another input. This cross coupling implies that a nonlinearity exists in the process being controlled. A neurofuzzy model is used to model this nonlinear dynamic process. Based on the dynamic fuzzy model, a predictive control system has been developed to control the welding process. Experiments confirmed that the developed control system is effective in achieving the desired fusion state despite the different disturbances  相似文献   

5.
A model-based predictive control methodology in the space of the latent variables for continuous processes is presented. Implementing identification and control in the latent variable space eases identification in the case of correlation in the data set, acts as a prefilter reducing the effect of noisy data, and reduces computational complexity. The proposed data-driven LV-MPC approach deals with setting the control horizon different to the prediction horizon, improves Hessian conditioning, and attains offset-free tracking. Additionally, a weighting matrix is introduced in the identification stage so that the performance of the predictor in the near horizon can be enhanced. A MIMO example shows how the proposed methodology can outperform conventional data-driven MPC in terms of computational complexity and reference tracking.  相似文献   

6.
We study the problem of decentralization of flow control in packet-switching networks under the isarithmic scheme. An incoming packet enters the network only if there are permits available at the entry port when it arrives. The actions of the controllers refer to the routing of permits in the network and the control variables are the corresponding probabilities. We study the behavior of adaptive algorithms implemented at the controllers to update these probabilities and seek optimal performance. This problem can be stated as a routing problem in a closed queueing network. The centralized version of a learning automation is a general framework presented along with the proof of asymptotic optimality. Decentralization of the controller gives rise to non-uniqueness of the optimal control parameters. Non-uniqueness can be exploited to construct asymptotically optimal learning algorithms that exhibit different behavior. We implement two different algorithms for the parallel operation and discuss their differences. Convergence is established using the weak convergence methodology. In addition to our theoretical results, we illustrate the main results using the flow control problem as a model example and verify the predicted behavior of the two proposed algorithms through computer simulations, including an example of tracking.The work of this author was partially supported by a grant from the Canadian Institute for Telecommunications Research under the NCE program of the Government of Canada, and partially supported by NSERC grant WFA 0139015 and FCAR-Québec grant 95-NC-1375.The work of this author was supported by a grant from the CITR under the NCE program of the Government of Canada.  相似文献   

7.
This paper presents an adaptive neural control design for nonlinear pure-feedback systems with an input time-delay. Novel state variables and the corresponding transform are introduced, such that the state-feedback control of a pure-feedback system can be viewed as the output-feedback control of a canonical system. An adaptive predictor incorporated with a high-order neural network (HONN) observer is proposed to obtain the future system states predictions, which are used in the control design to circumvent the input delay and nonlinearities. The proposed predictor, observer and controller are all online implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed. The conventional backstepping design and analysis for pure-feedback systems are avoided, which renders the developed scheme simpler in its synthesis and application. Practical guidelines on the control implementation and the parameter design are provided. Simulation on a continuous stirred tank reactor (CSTR) and practical experiments on a three-tank liquid level process control system are included to verify the reliability and effectiveness.  相似文献   

8.
The convergence property of constrained model-based predictive control for batch processes (BMPC) is investigated. BMPC is a recently developed control technique that combines iterative learning control with real-time predictive control. It is proven for a general class of linear constrained systems that the tracking error converges to zero as the run number increases.  相似文献   

9.
In this paper a linear model-based predictive control (MPC) algorithm is presented, for which nominal closed-loop stability is guaranteed. The input is obtained by minimizing a quadratic performance index over a finite horizon plus an end-point state (EPS) penalty, subject to input, state and output constraints. Under certain conditions, the weighting matrix in the EPS penalty enables one to specify an invariant ellipsoid in which the input, state and output constraints are satisfied. In existing MPC algorithms this weighting matrix is calculated off-line. The main contribution of this paper is to incorporate the calculation of the EPS-weighting matrix into the on-line optimization problem of the controller. The main advantage of this approach is that a natural and automatic trade-off between feasibility and optimality is obtained. This is demonstrated in a simulation example.  相似文献   

10.
Hierarchical model-based predictive control of a power plant portfolio   总被引:1,自引:0,他引:1  
One of the main difficulties in large-scale implementation of renewable energy in existing power systems is that the production from renewable sources is difficult to predict and control. For this reason, fast and efficient control of controllable power producing units – so-called “portfolio control” – becomes increasingly important as the ratio of renewable energy in a power system grows. As a consequence, tomorrow's “smart grids” require highly flexible and scalable control systems compared to conventional power systems. This paper proposes a hierarchical model-based predictive control design for power system portfolio control, which aims specifically at meeting these demands.The design involves a two-layer hierarchical structure with clearly defined interfaces that facilitate an object-oriented implementation approach. The same hierarchical structure is reflected in the underlying optimisation problem, which is solved using Dantzig–Wolfe decomposition. This decomposition yields improved computational efficiency and better scalability compared to centralised methods.The proposed control scheme is compared to an existing, state-of-the-art portfolio control system (operated by DONG Energy in Western Denmark) via simulations on a real-world scenario. Despite limited tuning, the new controller shows improvements in terms of ability to track reference production as well as economic performance.  相似文献   

11.
This paper describes an adaptive fuzzy control strategy for decentralized control for a class of interconnected nonlinear systems with MIMO subsystems. An adaptive robust tracking control schemes based on fuzzy basis function approach is developed such that all the states and signals are bounded. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The resultant adaptive fuzzy decentralized control with multi-controller architecture guarantees stability and convergence of the output errors to zero asymptotically by local output-feedback. An extensive application example of a three-machine power system is discussed in detail to verify the effectiveness of the proposed algorithm.  相似文献   

12.
Within this brief paper, a stable indirect adaptive controller is presented for a class of interconnected nonlinear systems. The feedback and adaptation mechanisms for each subsystem depend only upon local measurements to provide asymptotic tracking of a reference trajectory. In addition, each subsystem is able to adaptively compensate for disturbances and interconnections with unknown bounds. The adaptive scheme is illustrated through the longitudinal control of a string of vehicles within an automated highway system (AHS)  相似文献   

13.
In this paper, a model-predictive trajectory-tracking control applied to a mobile robot is presented. Linearized tracking-error dynamics is used to predict future system behavior and a control law is derived from a quadratic cost function penalizing the system tracking error and the control effort. Experimental results on a real mobile robot are presented and a comparison of the control obtained with that of a time-varying state-feedback controller is given. The proposed controller includes velocity and acceleration constraints to prevent the mobile robot from slipping and a Smith predictor is used to compensate for the vision-system dead-time. Some ideas for future work are also discussed.  相似文献   

14.
针对某炼油厂油品车间柴油调和过程这个多输入多输出复杂对象进行了神经网络内模控制的仿真研究,其中在线优化算法采用线性规划的方法.神经网络预测控制正是克服了传统控制思想的束缚,通过对象的输入输出特性建立对象的数学模型,而不必通过复杂的系统辨识来建立过程的模型.对仿真结果进行了比较,结果表明神经网络预测控制算法对复杂对象具有较好的控制作用.  相似文献   

15.
针对一类不确定大规模系统,研究其全局稳定的分散自适应神经网络反推跟踪控制问题.在假设不匹配的未知关联项满足部分已知的非线性Lipschitz条件下,采用神经网络作为前馈补偿器,逼近参考信号作为输入的未知关联函数;设计者可根据参考信号的界预先确定神经网络逼近域,同时保证了闭环系统的全局稳定性.仿真实例验证了控制算法的有效性.  相似文献   

16.
A model-reference adaptive control system is described where extrapolation techniques are used for identification and for error-prediction at discrete time intervals. The system employs rectangular adaptation pulses of finite duration to minimize a cost-functional of predicted square errors. Weighted squares of the error rate-of-change are included in the cost-functional to be minimized and a number of constraints are considered. Simulation resuits for systems consisting of linear, time-varying, and nonlinear second- to fifth-order processes with linear second-order reference-models are given, where satisfactory adaptation is accomplished.  相似文献   

17.
This paper presents the Generalized Predictive Control (GPC) strategy based on Artificial Neural Network (ANN) plant model. To obtain the step and the free process responses which are needed in the generalized predictive control strategy we iteratively use a multilayer feedforward ANN as a one-step-ahead predictor. A bioprocess was chosen as a realistic nonlinear SISO system to demonstrate the feasibility and the performance of this control scheme. A comparison was made between our approach and the adaptive GPC (AGPC).  相似文献   

18.
A multirate model-based predictive controller   总被引:1,自引:0,他引:1  
Model-based predictive control (MBPC) is an emerging design tool in process control, thanks to its capability to incorporate several attributes fundamental in practical applications. On the other hand, multirate control systems are able to describe various practical situations where technological constraints require that sensor measurements and control calculations are performed at different times and rates. For these reasons, it seems appropriate to extend MBPC to the multirate case. Then, a design procedure is developed in this paper, leading to a multirate controller which guarantees stability and zero error asymptotic regulation to the overall control system  相似文献   

19.
Syuan-Yi  Faa-Jeng  Kuo-Kai 《Neurocomputing》2009,72(13-15):3220
A direct modified Elman neural networks (MENNs)-based decentralized controller is proposed to control the magnets of a nonlinear and unstable multi-input multi-output (MIMO) levitation system for the tracking of reference trajectories. First, the operating principles of a magnetic levitation system with two moving magnets are introduced. Then, due to the exact dynamic model of the MIMO magnetic levitation system is not clear, two MENNs are combined to be a direct MENN-based decentralized controller to deal with the highly nonlinear and unstable MIMO magnetic levitation system. Moreover, the connective weights of the MENNs are trained online by back-propagation (BP) methodology and the convergence analysis of the tracking error using discrete-type Lyapunov function is provided. Based on the direct and decentralized concepts, the computational burden is reduced and the controller design is simplified. Furthermore, the experimental results show that the proposed control scheme can control the magnets to track various periodic reference trajectories simultaneously in different operating conditions effectively.  相似文献   

20.
A decentralized control of interconnected systems using neural networks.   总被引:4,自引:0,他引:4  
We develop a decentralized neural-network (NN) controller for a class of large-scale nonlinear systems with the high-order interconnections. The controller is a mixed NN comprised of a conventional NN and a special NN. The conventional NN is used to approximate the unknown nonlinearities in the subsystem, while a special NN is used to counter the high-order interconnections. We prove that this NN structure can achieve a stable controller for the large-scale systems.  相似文献   

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