An exact-model based unified predictive force control of flexible manipulators

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.

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.

The utilization of fast-switching solenoid valves permits the construction of lowcost hydraulic systems with high resistance to oil contamination. In spite of these advantages, the use of solenoid valves results in severe nonlinearities and discontinuities in the flow rate, making it difficult to control and design the system. In this study, the performance of a hydraulic system operated by an on/off unified predictive controller was investigated. It was verified that despite strong nonlinearities in the plant, the performance of the system was considerably improved by employing the predictive control scheme.

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

Energy-efficient fuzzy model-based multivariable predictive control of a HVAC system

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.

Model Predictive Control in Milling based on Support Vector Machines

Model-based predictive control that highly depends on system parameters has been widely investigated. In contrast, model -free predictive current control (MFPCC) can be performed based on input or output measured data, rather than on any system model information, hence eliminating the influence of parameter uncertainties. In such a strategy, the current gradients due to each of the possible voltage vectors are stored and used to predict future currents. In this paper, a model free predictive control method based on discrete space vector modulation is used, which increases the effective vector control set by constructing a virtual vector which is composed of linear combination of real vectors. The THD of grid connected current is reduced. Simulation and experimental results confirmed the effectiveness of the proposed method.

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.

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 growth of distributed solar photovoltaic (PV) generation in low-voltage grids in recent years has raised concerns about power quality disruptions introduced by solar variability. In response, some electricity utilities have introduced restrictions to new PV installations, such as imposing limits to power ramp rates and exports to the grid. These restrictions effectively require PV installations to locally compensate rapid variations in power output, such as those arising from passing clouds, by using batteries, for example. The direct control of responsive loads to follow variations in solar capacity is a cost-effective alternative to batteries to mitigate impacts to the grid. This paper explores the application of model-based predictive control (MPC) of a population of air conditioners (ACs) to achieve optimal tracking of PV generation to compensate solar variability at a substation or distribution transformer aggregation level. The proposed approach exploits an existing model for aggregate AC demand to design an MPC scheme to modulate the demand of clusters of ACs to track fluctuations in PV capacity. The effectivity of the proposed MPC scheme to achieve AC load following and regulation at different levels of PV penetration is analysed in a numerical simulation study, which shows that solar variability can be reduced by 80 to 95% at PV capacities between 25 and 10% of the peak AC demand with minimal impact to end-user comfort levels.

Model-based predictive signal control is a popular method to pro-actively control traffic and to reduce the effects of congestion in urban networks. In combination with structure-free controllers, which adapt signal settings in arbitrary order and combination (no imposed cycles), these predictive control methods have a high potential to increase system performance by adapting to individual vehicle patterns, which are increasingly available due to new technology. However, most of these control methods assume perfect predictions, while in practice there are prediction errors due to various reasons. In this paper, the sensitivity of the system performance to these prediction errors is analyzed, for an urban corridor with spillback.In a microscopic simulator, first the ideal world is created for the structure-free model-based predictive signal controller, in which perfect predictions are made and the controller can reach its optimal performance. Then prediction errors are introduced in this perfect world, distinguished in aggregation errors that arise using a macroscopic prediction model and biases that represent structural errors in the prediction model or in its demand and state input. The effects of these prediction errors on the system performance are analyzed, as a function of the prediction horizon and update frequency of the control system.The results show that, even under errors, longer prediction horizons lead to better performance, up to a certain optimal prediction horizon length. A high update frequency dampens the influence of prediction errors, enabling the structure-free controller to correct mistakes faster. However, there remains a significant performance loss due to aggregation errors and biases in the prediction model, indicating a promising performance gain of more reliable predictions and the incorporation of information on individual vehicles in future control applications. Moreover, for all model quantities one direction of the bias has more impact on the system performance than the other direction, indicating guidelines towards a more robust control system that suffers less from erroneous predictions.

This chapter presents a strategy for controlling the concentration of dissolved oxygen (DO) in the bioreactor of a pilot wastewater plant (WWTP). The control strategy being developed is model-based predictive control (MPC). To apply the control algorithm, the estimation of the oxygen transfer function (KLa) is first performed, then the model and linearization technique are determined and finally the MBPC controller is designed. The results are simulated in MATLAB® and executed in the plant control and supervision system Supervisory Control and Data Acquisition (SCADA) in LabVIEWTM. This chapter is organized as follows: Section 1 presents a brief introduction, and Section 2 determines and describes the model to be used and its respective linearization, as well as the results obtained for the KLa parameter. Finally, Section 3 describes the design methodology of the generalized predictive control (GPC) controller proposed by Clarke, using the Model Predictive Control Toolbox and the EPSAC strategy developed by De Keyser. It should be noted that the simulations in each of the sections were performed in MATLAB® and executed in the control and supervision system with the MATLAB® script interface in LabVIEWTM.

This paper presents the results of a model-based predictive control (MPC) design for a quadrotor aerial vehicle with a suspended load. Unlike previous works, the controller takes into account the hanging payload dynamics, the dynamics in three-dimensional space, and the vehicle rotation, achieving a good balance between fast stabilization times and small swing angles. The mathematical model is based on the Euler–Lagrange formulation and considers the dynamics of the vehicle, the cable, and the load. Then, the mathematical model is represented as an input-affine system to obtain the linear model for the control design. A constrained MPC strategy was designed and compared with an unconstrained MPC and an algorithm from the literature for the case of study. The constraints to be considered include the limits on the swing angles and the quadrotor position. The constrained control algorithm was constructed to stabilize the aerial vehicle. It aims to track a trajectory reference while attenuating the load swing, considering a maximum swing range of ±10∘. Numerical simulations were carried out to validate the control strategy.

The model-based predictive control (MBPC) technologies are based on the prediction of future behaviour of the process to be controlled which is obtained by the model of the plant. Using the explicit process model and an optimization approach MBPC can be applied to complex, multivariable, nonminimum-phase, open loop unstable systems or processes with a long delay time. The paper introduces the predictive control scheme with nonlinear optimization and a fuzzy Takagi-Sugeno model which is used to describe the nonlinear properties of the process and to predict the process behaviour. A neural network for model parameter estimation in the predictive control scheme is applied, and simulation results of the process control is presented to illustrate the benefits possible with the given concept.