Recurrent neuro-fuzzy networks for nonlinear process modeling

A recurrent neuro-fuzzy network-based nonlinear long range model predictive control strategy is proposed in this paper. The process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to model the process. The global model output is obtained through the centre of gravity defuzzification. Based upon a neuro-fuzzy network model, a nonlinear model-based predictive controller can be developed by combining several local linear model-based predictive controllers which usually have analytical solutions. This strategy avoids the time consuming numerical optimisation procedure, and the uncertainty in convergence to the global optimum which are typically seen in conventional nonlinear modelbased predictive control strategies, Furthermore, control actions obtained based on local incremental models contain integration actions which can naturally eliminate static control offsets. The technique is demonstrated by an application to the modelling and control of liquid level in a water tank.

This paper presents an optimal control scheme based on model-based predictive control (MPC) for a wheeled mobile robot (WMR) with nonholonomic constraints. It is shown that, by using MPC, some advantages can be obtained, such as the ability to handle constraints due to state or input limitations and performance improvement. To solve some problems with other MPCs for WMRs, this paper proposes to formulate a cost function in polar coordinates. Considerations regarding the computational effort of the MPC are developed with the purpose of analysing the viability of the proposed technique in real-time.

The local linear modelling approach is capable of modelling complex nonlinear processes. The transparency of the model architecture permits the incorporation of prior knowledge in different ways and the integration of process knowledge and experimental data has been demonstrated in detail for the modelling of the heat exchanger. Utilising an arbitrary local modelling strategy does not automatically guarantee a transparent model. The employed LOLIMOT algorithm has proven to yield transparent and well interpretable models. The appropriate choice of an excitation signal is vital for a good model. Here, the transparency of the local structure again aids the engineer in the design and the assessment of the excitation signal. The interpretability of the local structure offers simple ways for the validation of the identified model based on local characteristic values as process gains or time constants. The obtained local linear models can further be utilised in model-based control approaches as well as process monitoring or fault detection.

This chapter deals with local linear neuro-fuzzy models, also referred to as Takagi-Sugeno fuzzy models, and appropriate algorithms for their identification from data. The local linear modeling approach is based on a divide-andconquer strategy. A complex modeling problem is divided into a number of smaller and thus simpler subproblems, which are solved (almost) independently by identifying simple, e.g., linear, models. The most important factor for the success of such an approach is the division strategy for the original complex problem. Therefore, the properties of local linear neuro-fuzzy models crucially depend on the applied construction algorithm that implements a certain division strategy. This chapter focuses on the local linear model tree (LOLIMOT) algorithm proposed by Nelles [267, 271, 286].

This paper is concerned with a computationally efficient (suboptimal) nonlinear model-based predictive control (MPC) algorithm and its application to a high-purity high-pressure ethylene-ethane distillation column. A neural model of the process is used on-line to determine the local linearisation and the nonlinear free response. In comparison with general nonlinear MPC technique, which hinges on non-convex optimisation, the presented structure is far more reliable and less computationally demanding because it results in a quadratic programming problem, whereas its closed-loop control performance is similar.

A new local linear prediction model for chaotic time series

The article studies the trajectory tracking control problem of underactuated marine vehicles via the nonlinear model predictive control (NMPC) strategy, where practical control and state constraints present. It is a well-known challenging issue that the conventional NMPC is not applicable for underactuated marine vehicles, due to the fact that there does not exist a local static continuous state-feedback controller to stabilize the underactuated dynamics. To resolve this issue, this article proposes to construct an auxiliary time-varying tracking controller to aid terminal constraint design in the NMPC framework, where the time-varying tracking controller borrows the ideas from Lyapunov's direct method and backstepping approach. Based on this, a novel NMPC algorithm is designed to ensure trajectory tracking control of underactuated marine vehicles. Furthermore, a systematic parameter design approach is developed. Under the designed parameters, we show that the tracking error system is input-to-state stable (ISS). Finally, the effectiveness of the designed algorithm is verified by thorough simulation and hardware experiments.

Nonlinear model predictive control for the space heating system of a university building in Norway

Extracting harmonic signal from a chaotic background with local linear model

Notice of Violation of IEEE Publication Principles<br><br>"Internal model control based on locally linear model tree (LOLIMOT) model with application to a PH neutral process"<br>by M. Jalili-Kharaajoo, A. Rahmati and F. Rashidi,<br>in proceedings of the 2003 IEEE Internationa Conference on Systems, Man, and Cybernetics, vol 4, pp. 3051-3-55.<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>"Nonlinear Internal Model Control for MISO Systems Based on Local Linear Neuro-Fuzzy Models"<br> by A. Fink, O. Nelles and R. Isermann,<br>in 15th IFAC World Congress, Barcelona, Spain, 2002.<br><br> <br/> The internal model control (IMC) scheme has been widely applied in the field of process control. So far, IMC has been mainly applied to linear processes. This paper discusses the extension of the IMC scheme to nonlinear processes based on local linear models where the properties of linear design procedures can be exploited. The IMC scheme results in controllers that are comparable to conventional multi layer perceptron (MLP) networks. In practice, the tuning of conventional MLP based controllers can be very time-consuming whereas the IMC design procedure is very simple and reliable. The design effort of the IMC based on locally linear model tree (LOLIMOT) algorithm are discussed and the control results are compared by application to nonlinear control of an industrial-scale PH neutralization process. Simulation studies of a PH neutralization process confirm the excellent nonlinear modeling properties of the proposed locally linear network and illustrate the potential for set point tracking and disturbance rejection within an IMC framework.

Local automatic controllers are an integral part of a modern distribution system. They control transformer load tap changer (LTC) positions and statuses of switched capacitors to ensure voltage and loading constraints are satisfied under changing operating conditions. Distribution network applications, which traditionally have been used for planning, account for the presence of local controllers in a simplified manner. Distribution management systems (DMS) bring network applications to the operational practice which requires much more detailed modeling of local controllers, both for the purpose of real-time power flow monitoring, and for centralized control. In the latter case, the local controllers create both opportunities and restrictions for DMS optimization functions such as volt/VAr control and feeder reconfiguration. This paper describes how the LTC and capacitor local controllers are modeled as a part of the power flow solution, and how they interact and affect DMS optimization functions. The impact of the local controller modeling on power flow, volt/VAr control and feeder reconfiguration is illustrated by numerical examples.

04/01864 Intelligent decoupling control of power plant main steam pressure and power output: Liu, H. et al. International Journal of Electrical Power & Energy Systems, 2003, 25, (10), 809–819

A Local Model Networks Based Multivariable Long-Range Predictive Control Strategy for Thermal Power Plants

Hierarchical energy management system for multi-microgrid coordination with demand-side management

Decentralized switched model-based predictive control for distributed large-scale systems with topology switching