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

A type of recurrent neuro-fuzzy network is proposed in this paper to build long-term prediction models for nonlinear processes. 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 which is essentially the interpolation of local model outputs. This modeling strategy utilizes both process knowledge and process input output data. Process knowledge is used to initially divide the process operation into several fuzzy operating regions and to set up the initial fuzzification layer weights. Process input output data are used to train the network. Network weights are such trained so that the long-term prediction errors are minimized. Through training, membership functions of fuzzy operating regions are refined and local models are learn. Based on the recurrent neuro-fuzzy network model, a novel type of nonlinear model-based long range predictive controller can be developed and it consists of several local linear model-based predictive controllers. Local controllers are constructed based on the corresponding local linear models and their outputs are combined to form a global control action by using their membership functions. This control strategy has the advantage that control actions can be calculated analytically avoiding the time consuming nonlinear programming procedures required in conventional nonlinear model-based predictive control. The techniques have been successfully applied to the modeling and control of a neutralization process.

This paper discusses the model-based predictive controller design of networked nonlinear systems with communication delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation results demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

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

The class of model-based predictive controllers (MBPCs) has been quite successful at overcoming many of the well-known difficulties inherent in nonminimum phase (NMP) systems, e.g., flexible structures, and among the various MBPCs, Unified Predictive Control (UPC) was selected for the control tasks in this paper. Two methods are used to model the tip force of a flexible manipulator. Modal expansion (summation) is a pole but not zero preserving approach, while Infinite Product Expansion preserves both poles and zeros of a system. It is shown that in a flexible structure framework the difference between the exact and modal expansion zeros is significant. The effect of transmission zero mismatch will be observed in a closed loop system, especially when a model based controller is used. Simulation results show that when a UPC is designed based on the modal expansion model, it destabilizes the closed loop system when an exact model is used as the plant. This suggests a severe problem in implementing the designed UPC experimentally. The use of an exact model in a model-based controller design procedure, such as UPC, is therefore suggested. (Author)

Increasing demand for data stream processing algorithms and evolving identification methods warrants research focused into new (fuzzy) evolving identification algorithms. The resulting models can increasingly be used in control system scenarios, resulting in an effectivly self-tuned closed loop system. These trends have motivated us to combine a recently developed evolving, fuzzy, FRLS-based identification technique with the model-based predictive control algorithm Predictive Functional Control in state space. The included nonlinear identification algorithm and the resulting model structure are investigated and shown to be suitable for direct use in model based control scenarios. The entire approach has a low number of tuning parameters. Since the model is evolved and then directly usable in a control scenario, the approach results in a self-tuned closed loop system. The approach is validated on a Wienner-Hammerstein dynamic process - a non-linear second order dynamic process. The evaluation is performed in an assortment of scenarios including reference tracking, closed loop dynamics analysis and both input- and output-disturbance rejection.

Fuzzy predictive control integrates conventional model-based predictive control with techniques from fuzzy multicriteria decision making, and translates the goals and the constraints to predictive control in a transparent way. The information regarding the (fuzzy) goals and the (fuzzy) constraints of the control problem is combined by using a decision function from the fuzzy set theory. This paper investigates the use of fuzzy decision making in predictive control and compares the results to those obtained from conventional model-based predictive control. Experiments on a nonminimum phase, unstable linear system and air conditioning system with nonlinear dynamics are studied. It is shown that the performance of the model-based predictive controller can be improved by the use of fuzzy goals and criteria with fuzzy decision-making techniques.

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

Irrigation canals are large-scale systems, consisting of many interacting components, and spanning vast geographical areas. For safe and efficient operation of these canals, maintaining the levels of the water flows close to pre-specified reference values is crucial, both under normal operating conditions as well as in extreme situations.    Irrigation canals are equipped with local controllers, to control the flow of water by adjusting the settings of control structures such as gates and pumps. Traditionally, the local controllers operate in a decentralized way in the sense that they use local information only, that they are not explicitly aware of the presence of other controllers or subsystems, and that no communication among them takes place. Hence, an obvious drawback of such a decentralized control scheme is that adequate performance at a system-wide level may be jeopardized, due to the unexpected and unanticipated interactions among the subsystems and the actions of the local controllers.    In this paper we survey the state-of-the-art literature on distributed control of water systems in general, and irrigation canals in particular. We focus on the model predictive control (MPC) strategy, which is a model-based control strategy in which prediction models are used in an optimization to determine optimal control inputs over a given horizon. We discuss how communication among local MPC controllers can be included to improve the performance of the overall system. We present a distributed control scheme in which each controller employs MPC to determine those actions that maintain water levels after disturbances close to pre-specified reference values. Using the presented scheme the local controllers cooperatively strive for obtaining the best system-wide performance. A simulation study on an irrigation canal with seven reaches illustrates the potential of the approach.

This paper presents a regulation technique based on the model-based predictive control (MPC) for a subway traffic system. The role of passengers in the traffic system has been modeled analytically by considering passenger flow arriving at platforms, passenger demand and the number of passengers at the platforms and in the trains. Three major reasons for passenger discomfort are introduced (waiting and traveling, congestion and jitter discomfort) to develop an appropriate objective function. The MPC approach is used to develop control actions to minimize the objective function by conforming to safety and operational constraints. A simulation was carried out using the specifications of Tehran Metro Line 2, the results of which demonstrate the effectiveness of the proposed method.

Virtual storage capability of residential buildings for sustainable smart city via model-based predictive control

Model Predictive Control in Milling based on Support Vector Machines

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.

We study the solution theory of singular linear switched systems with inputs (also known as switched descriptor systems). These systems are highly relevant in many applications; in particular, in economics the well-known dynamic Leontief model with changing coefficient matrices falls into this class. Theorem 5.1 in the paper by Anh et al. (2019) stated that if a singular linear switched system is jointly index-1, then there exists an explicit surrogate switched system having identical solution behavior for all switching signals. However, it was not clear yet whether the jointly index-1 condition is a necessary and sufficient condition for the existence and uniqueness of a solution. Furthermore, it was also not clear what conditions are actually required to guarantee existence and uniqueness of solutions for particular switching signals only. In this article, we provide necessary and sufficient conditions for existence and uniqueness of solutions for singular linear switched systems with respect to fixed switching signals (both mode sequences and switching times are fixed), fixed mode sequences (switching times are arbitrary), and arbitrary switching signals (both mode sequences and switching times are arbitrary). In all three cases we provide an explicit surrogate system with the same solution set; our approach improves the results presented in Anh et al. (2019) as the coefficient matrices describing the transition from x(k) to $$x(k+1)$$ x ( k + 1 ) only depend on original system matrices at time k and $$k+1$$ k + 1 and not on $$k-1$$ k - 1 as in Anh et al. (2019). We illustrate the theoretical findings with the dynamic Leontief model and investigate the solvability properties of discretizations of continuous-time singular systems.

The following paper presents an improved self-tuned Model-based Predictive Controller (MPC) with constraints through an Extended Kalman Filter (EKF). The algorithm is tested in a Continuous Stirred Tank Reactor (CSTR) process for temperature and molar concentration regulations. The proposed method was developed from a previous work [1], where the MPC was tuned from error and control weights of equal value. A posterior analysis demonstrated that the MPC tuning was not robust. Sometimes it generated negative weight values due to the EKF convergence and the algorithm cannot always assure a global minimum value at performance index. Therefore, this work considers this problem and always generates positive weights which assures a minimum global value in the cost function of the MPC.

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.