Adaptive neural model-based fault tolerant control for multi-variable processes

An adaptive neural network model-based fault tolerant control approach for unknown non-linear multi-variable dynamic systems is proposed. A multi-layer Perceptron network is used as the process model and is adapted on-line using the extended Kalman filter to learn changes in process dynamics. In this way, the adaptive model will learn the post-fault dynamics caused by actuator or component faults. Then, the inversion of the neural model is used as a controller to maintain the system stability and control performance after fault occurrence. The convergence of the model inversion control is proved using Lyapunov method. The proposed method is applied to the simulation of a two-input two-output continuous-stirred tank reactor to demonstrate the effectiveness of the approach. Several actuator and component faults are simulated on the continuously stirred tank reactor process when the system is under the proposed fault tolerant control. The results have shown a fast recovery of tracking performance and the maintained stability.     

非线性关联系统自适应神经网络输出反馈分散控制

针对一类带有完全未知关联项的非线性大系统,提出一种自适应神经网络输出反馈分散控制方法.采用神经网络逼近未知的关联项,因此对关联项常做的假设如匹配条件,被上界函数所界定等不再要求.在神经元输入中采用参考信号取代关联信号,从而成功地避免了对关联信号的微分.保证了闭环系统所有信号半全局一致最终有界,证明了跟踪误差收敛于一个包含原点的小残集.     

Unconstrained networked decentralized model predictive control

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.     

Adaptive neural decentralized control for strict feedback nonlinear interconnected systems via backstepping

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.     

双馈风机自适应神经分散协调预测控制

目前,双馈感应发电机转子侧励磁控制系统均依据"孤立"模型设计."孤立"模型忽略了各子系统之间、各控制器之间的相互作用,因此这种控制器仅对改善本系统的控制特性有一定作用.针对以上情况,提出了一种自适应神经分散协调控制策略,并将其应用于双馈感应发电机转子侧励磁控制系统仿真研究中.首先,利用电力关联测量法建立了基于本地变量的双馈风机关联测量模型.其次,以关联测量模型作为预测模型,采用多模型预测控制器对双馈风机转子侧励磁系统进行控制.最后,利用可在线调整的人工神经网络作为多模型加权控制器以补偿双馈风机强非线性、工作区间变化范围大的特点.主导特征值分析和动态仿真表明:该控制策略不仅实现了高精度的最大功率跟踪控制,而且在电力系统故障时可提供持续的、充足的阻尼.     

基于扩张状态观测器的分散模型预测控制

针对复杂关联系统中分散控制方法无法有效解决子系统间的耦合和干扰问题, 提出一种基于扩张状态观测器的分散模型预测控制算法. 首先将复杂关联系统分解为多个状态维数较低、控制变量较少的子系统, 并为每个子系统设计本地预测控制器; 然后, 采用扩张状态观测器对子系统的耦合项以及干扰项进行估计, 进而利用估计值对子系统进行前馈补偿, 从而降低复杂关联系统的计算复杂度, 提高系统的稳定性和抗干扰能力; 最后, 利用液位控制系统验证了所提出算法的有效性.

     

Stabilizing decentralized model predictive control of nonlinear systems

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.     

Adaptive decentralized control under non-uniqueness of the optimal control

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.     

Neurofuzzy model-based predictive control of weld fusion zonegeometry

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     

Data-driven latent-variable model-based predictive control for continuous processes

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.     

基于Volterra模型的预测控制及应用

由于工业实践的需要,非线性预测控制近年来受到广泛地关注.Volterra模型是一类特殊的非线性模型,非常适合描述工业过程中的无记忆非线性对象.传统的基于Volterra模型的控制器合成法及迭代计算预测控制器法计算量大,且不便于处理控制约束.非线性模型预测控制求解是典型的非线性规划问题,序列二次规划(sequential quadratic program,SQP)算法是求解非线性规划问题常用方法之一.针对Volterra非线性模型预测控制求解问题,本文将滤子法与一种信赖域SQP算法相结合,提出一种改进SQP算法用于基于非线性Volterra模型的带控制约束的多步预测控制求解,并分析了所提方法的收敛性.工业实例仿真结果证实了所提方法的可行性与有效性.     

Adaptive neural network predictive control for nonlinear pure feedback systems with input delay

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.     

Non-minimal state-space model-based continuous-time model predictive control with constraints

This article proposes a model predictive control scheme based on a non-minimal state-space (NMSS) structure. Such a combination yields a continuous-time state-space model predictive control system that permits hard constraints to be imposed on both plant input and output variables, whilst using NMSS output-feedback without the need for an observer. A comparison between the NMSS and observer-based approaches using Monte Carlo uncertainty analysis shows that the former design is considerably less sensitive to plant-model mismatch than the latter. Through simulation studies, the article also investigates the role of the implementation filter in noise attenuation, disturbance rejection and robustness of the closed-loop predictive control system. The results show that the filter poles become a subset of the closed-loop poles and this provides a straightforward method of tuning the closed-loop performance to achieve a reasonable balance between speed of response, disturbance rejection, measurement noise attenuation and robustness.     

Hierarchical model-based predictive control of a power plant portfolio

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.     

Convergence of constrained model-based predictive control for batch processes

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.     

Multiple fuzzy model-based temperature predictive control for HVAC systems

In this paper, a multiple model predictive control (MMPC) strategy based on Takagi–Sugeno (T–S) fuzzy models for temperature control of air-handling unit (AHU) in heating, ventilating, and air-conditioning (HVAC) systems is presented. The overall control system is constructed by a hierarchical two-level structure. The higher level is a fuzzy partition based on AHU operating range to schedule the fuzzy weights of local models in lower level, while the lower level is composed of a set of T–S models based on the relation of manipulated inputs and system outputs correspond to the higher level. Following this divide-and-conquer strategy, the complex nonlinear AHU system is divided into a set of T–S models through a fuzzy satisfactory clustering (FSC) methodology and the global system is a fuzzy integrated linear varying parameter (LPV) model. A hierarchical MMPC strategy is developed using parallel distribution compensation (PDC) method, in which different predictive controllers are designed for different T–S fuzzy rules and the global controller output is integrated by the local controller outputs through their fuzzy weights. Simulation and real process testing results show that the proposed MMPC approach is effective in HVAC system control applications.     

Optimizing the end-point state-weighting matrix in model-based predictive control

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.     

Adaptive control of a class of decentralized nonlinear systems

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)     

Adaptive fuzzy decentralized control for interconnected MIMO nonlinear subsystems

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.     

Tracking-error model-based predictive control for mobile robots in real time

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.