Multi-objective Predictive Control Research Articles

User-Centric Multi-Objective Predictive Control for Mixed Vehicular Platoon

User-Centric Multi-Objective Predictive Control for Mixed Vehicular Platoon

Distributed Multiobjective Predictive Control for Connected Electric Vehicles

Distributed Multiobjective Predictive Control for Connected Electric Vehicles

Precise Voltage Regulation of Unbalanced Bipolar DC-Grid-Based Charging Stations: A New Multiobjective Predictive Control Approach

This paper proposes a new multi-objective predictive control approach and an optimized switching-scheme to precisely regulate the dc-voltages in bipolar dc-grid based charging stations working under critical conditions. An angular region is pre-selected to reduce the size of control sets to seven candidates within minimized angle error of 30-degree. Then, two subregions are located to further simplify finite sets without losing options for multi-objective optimization. Last, a hybrid switching scheme is proposed to improve current regulation working under a reduced sampling frequency. Delayed-signal-cancellation (DSC) technique and instantaneous power theory are also employed to ensure a resilient operation under grid-voltage distortions. Consequently, the proposed control strategy can precisely balance dc-voltages, track voltage references, and regulate grid-injected currents against unbalanced dc-loads and ac-grid distortions. Matlab/Simulink and an experimental prototype are utilized to investigate the comparative performances between the proposed approach and the recent predictive control methods. The simulation and experiments show significant improvements on dc-link voltages including reference tracking, dc-voltage balance, fast dynamic response, and harmonic reduction of ac-currents under a reduced sampling frequency.

Multi-objective predictive control for three-phase three-level neutral-point clamped inverter

Multi-objective predictive control for three-phase three-level neutral-point clamped inverter

Multi-objective predictive control based on the cutting tobacco outlet moisture priority

In this paper, we propose a new priority multi-objective optimization strategy of system output variables in cutting tobacco process. The proposed strategy focuses on the cutting tobacco moisture-controlled output variables optimization in feasible regions with two levels according to the priority. This study aims to provide a novel technical support for the chemical industry contained drying process. In order to alleviate the lack of degree of freedom of the system, strict set-point control is given, meanwhile, other output variables adopt zone control. Firstly, the system control output variables are optimized in ascending order of priority. Secondly, the specific lower-level target constraints are first relaxed. Finally, the relaxation of other high-priority target constraints is stopped when the optimization is feasible. Thus, the system control output variables move along the optimal target trajectory. From the perspective of practical application of engineering, under the condition of disturbance existing in the cutting tobacco drying process, the simulation shows that the proposed approach has good robustness when there is disturbance, and the previous method cannot meet the control requirement. The proposed strategy meanwhile has better tracking effect through single and multiple output variables simulation, which compared with traditional predictive control in real cutting tobacco drying process.

Data-driven predictive control for floating offshore wind turbines based on deep learning and multi-objective optimization

In order to deal with the problems of complicated modeling and the conflicts of control objectives in the control of floating offshore wind turbines (FOWTs), a multi-objective predictive control strategy for FOWTs based on a deep learning model and multi-objective optimization is proposed. In this strategy, the dynamic prediction model of the FOWT based on a gated recurrent neural network is established. Based on the dynamic prediction model, a multi-objective predictive control algorithm for individual pitch control of FOWTs is developed. The multi-objective distribution estimation algorithm is used to optimize the pitch angle considering the constraints of the pitch angle setting. The proposed method is simulated in different wind conditions by FAST. The results show that, compared with the collective pitch control and the gain scheduling PI individual pitch control, the output power of the proposed method is closer to the rated power, and the proposed method significantly reduces the mechanical load of the blade root. Moreover, compared with the gain scheduling PI individual pitch control, the proposed method effectively restrains platform pitching motion.

Learning-based demand response in grid-interactive buildings via Gaussian Processes

This paper presents a predictive controller for a grid-interactive multi-zone building where the temperature dynamics are learned via Gaussian Process (GP) regression. We investigate the development of a learning-based predictive control with two main objectives: (i) continuously learn the temperature dynamics of the building based on data; and, (ii) use the learned dynamics to solve a multi-objective predictive control problem to guarantee occupants’ comfort and energy efficiency during normal conditions and demand response events. We leverage the probabilistic non-parametric properties of GPs to estimate the (unknown) non-linear temperature dynamics of the building and to incorporate the uncertainty of those predictions in a multi-objective optimization problem. The GP-based predictive control is solved via a zero-order primal-dual projected-gradient algorithm. We evaluate numerically the performance of the proposed controller using a five-zone commercial building.

Design and Application of Hierarchical Multi-objective Predictive Control for Continuous Flow Stirred Tank Reactor

Design and Application of Hierarchical Multi-objective Predictive Control for Continuous Flow Stirred Tank Reactor

Data-Driven Multiobjective Predictive Optimal Control of Refining Process With Non-Gaussian Stochastic Distribution Dynamics

The fiber length and the Canadian standard freeness (CSF) are two key indices in measuring pulp quality of the refining process with non-Gaussian stochastic distribution dynamics. Among them, it is defective to use the conventional 1-D average fiber length (AFL) as a pulp quality index because the AFL is insufficient to describe the 2-D probability density function (pdf) shaping of fiber length distribution (FLD) with non-Gaussian types. In this article, a data-driven multiobjective predictive optimal control method is proposed to control the 2-D pdf shaping of FLD and the 1-D CSF, simultaneously. First, a radial basis function neural network (RBF-NN) based stochastic distribution model is developed to approximate the 2-D pdf shaping of FLD, and the parameters of RBF basis functions are updated by an iterative learning rule. Then, taking the developed pulp quality models, including the 2-D pdf model of FLD and the model of 1-D CSF as two predictors, a multiobjective predictive controller is designed by solving the nonlinear programming problems with constraints. Then, the stability of the resulted closed-loop system is also analyzed. Ultimately, the industrial experiments demonstrate the effectiveness of the proposed method.

Data-driven multi-objective predictive control of offshore wind farm based on evolutionary optimization

Abstract A data-driven multi-objective predictive control approach is proposed to increase the power production and reduce fatigue loads on a wind farm level using evolutionary optimization. The FLORIS (FLOw Redirection and Induction in Steady-state) tool is employed to characterize the wake characteristics within a wind farm and generate necessary data for data-driven prediction. A data driven wind farm predictor (WFP) is then constructed by using the turbine yaw angles as inputs and the wind farm power and thrust load as outputs under different inflow wind speeds and wind directions. Based on the WFP, a constrained optimization problem is formulated and the multi-objective predictive controller (MOPC) is designed based on wake steering and evolutionary optimization while considering the yaw angle control constraints. Extensive design experiments are conducted under various wind speeds and wind directions, and the results indicate that the wind farm thrust can be reduced by up to 12.96% while the wind farm power production can be well maintained at almost the same level by using the proposed control in comparison with a conventional model predictive control. The yaw angles optimized from the proposed control are more responsive and active in tuning the wind farm power production and thrust load mitigation than that in the conventional control method.

Reliability aware multi-objective predictive control for wind farm based on machine learning and heuristic optimizations

In this paper, a reliability aware multi-objective predictive control strategy for wind farm based on machine learning and heuristic optimizations is proposed. A wind farm model with wake interactions and the actuator health informed wind farm reliability model are constructed. The wind farm model is then represented by training a relevance vector machine (RVM), with lower computational cost and higher efficiency. Then, based on the RVM model, a reliability aware multi-objective predictive control approach for the wind farm is readily designed and implemented by using five typical state of the art meta-heuristic evolutionary algorithms including the third evolution step of generalized differential evolution (GDE3), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective particle swarm optimization (MOPSO), the multi-objective grasshopper optimization algorithm (MOGOA), and the non-dominated sorting genetic algorithm III (NSGA-III). The computational experimental results using the FLOw Redirection and Induction in Steady-state (FLORIS) and under different inflow wind speeds and directions demonstrate that the relative accuracy of the RVM model is more than 97%, and that the proposed control algorithm can largely reduce thrust loads (by around 20% on average) and improve the wind farm reliability while maintaining similar level of power production in comparison with a conventional predictive control approach. In addition, the proposed control method allows a trade-off between these objectives and its computational load can be properly reduced.

Multi-Objective Predictive Control Optimization with Varying Term Objectives: A Wind Farm Case Study

This paper introduces the incentive of an optimization strategy taking into account short-term and long-term cost objectives. The rationale underlying the methodology presented in this work is that the choice of the cost objectives and their time based interval affect the overall efficiency/cost balance of wide area control systems in general. The problem of cost effective optimization of system output is taken into account in a multi-objective predictive control formulation and applied on a windmill park case study. A strategy is proposed to enable selection of optimality criteria as a function of context conditions of system operating conditions. Long-term economic objectives are included and realistic simulations of a windmill park are performed. The results indicate the global optimal criterium is no longer feasible when long-term economic objectives are introduced. Instead, local sub-optimal solutions are likely to enable long-term energy efficiency in terms of balanced production of energy and costs for distribution and maintenance of a windmill park.

Data-Driven Multiobjective Predictive Control for Wastewater Treatment Process

To comply with the effluent standards and growing demands for safety and reliability, the operation of wastewater treatment processes (WWTPs) has been considered as a multiobjective control problem. In this article, a data-driven multiobjective predictive control (MOPC) method is developed to deal with the conflicting control objectives to improve the operation performance of WWTPs. The main contributions of MOPC are three folds: first, a multiobjective control strategy is developed in the design of MOPC. And an adaptive fuzzy neural network identifier, using the relevant process data, is designed to catch the nonlinear behaviors of WWTPs. Second, a transfer multiobjective optimization algorithm (TMOOA) is developed to obtain the optimal solutions of the conflicting control objectives. The major advantage of TMOOA is its low computational cost, which is realized by avoiding the computation of Pareto fronts. Third, the stability of MOPC has been given in detail. Meanwhile, the benefits and feasibility of MOPC are confirmed on the benchmark simulation model no. 2. The results further demonstrate the effectiveness of the proposed control method.

Comparative study of multi‐objective finite set predictive control methods with new max–min strategy applied on a seven‐level packedU‐cell inverter

This article studies the design and implementation of multi-objective predictive control (MO-PC) of a grid-connected seven-level packed U-cell (PUC7) inverter for minimising the line current total harmonic distortion (THD) and capacitor voltage error simultaneously. The weighting factor method is usually used as a simple method for solving the control problem in the literature. However, there are some difficulties and shortcomings in the calculation of weighting factors. Here, max–min selection strategy with together priority is adopted to reduce these deficiencies and improves the system performance. The switch model of the PUC inverter is derived and then applied in designing the MO-PC for grid-connected applications, where a controlled active or reactive power is injected into the utility. A comparative study among three strategies of weighting factor, fuzzy decision-making and max–min selection is performed to distinguish the proposed method superiority. Experimental results are given to validate the practicality of the applied controller in regulating the line current and capacitor voltage of the grid-connected PUC7 inverter.

Weight-Free Multi-Objective Predictive Cruise Control of Autonomous Vehicles in Integrated Perturbation Analysis and Sequential Quadratic Programming Optimization Framework

Adaptive cruise control of autonomous vehicles can be posed as a multi-objective optimization problem where several conflicting criteria, e.g., fuel economy, tracking capability, ride comfort, and safety, need to be satisfied simultaneously. In order to reconcile these conflicting criteria, this paper presents a novel multi-objective predictive cruise control (MOPCC) approach in the feasible perturbation-based real-time iterative optimization framework. The longitudinal dynamics of vehicles are described as nonlinear car-tracking models. The new cost function for MOPCC is defined as the distance of the criteria vector to the vector of separately minimized criteria (i.e., a utopia point of the criteria). The weight-free MOPCC is then obtained by solving a constrained nonlinear optimal control problem in receding horizon fashion. Due to the difficulty in solving the optimization problem, the integrated perturbation analysis and sequential quadratic programming (InPA-SQP) is employed to compute the cruise controller. The merit of the proposed MOPCC is that it can systematically handle different cruise scenarios regardless of the weights of the predictive cruise control (PCC) criteria. Several driving cases are used to demonstrate the effectiveness and benefits of the proposed approach via comparing to weighted PCC approaches.

Multiobjective predictive cruise control for connected vehicle systems on urban conditions with InPA‐SQP

SummaryThe connected vehicle (CV) system is one of the most effective core technologies in intelligent transportation systems. In order to solve the optimal velocity prediction problem for a CV system on urban roads, a multiobjective predictive cruise controller (MOPCC) for vehicles in the CV system is proposed to coordinate multiple performances including safety, tracking ability, ride comfort, and fuel economy. Firstly, with the ad hoc wireless communication technology, the signal phase and timing information is obtained to calculate the feasible velocity range for improving mobility. Then, the optimal target velocity of vehicles is computed by minimizing the fuel economic polynomial models of the vehicle system. Secondly, in order to systematically cope with those multiple performances, the Utopia point method is applied to change the multiobjective optimization problem into Utopia tracking problem. Furthermore, the MOPCC problem is formulated and solved by a fast numerical algorithm, ie, integrated perturbation analysis and sequential quadratic programming. Finally, simulations are presented to demonstrate the effectiveness of the proposed method in terms of improved the multiple performances.

A multi-objective predictive control strategy for enhancing primary frequency support with wind farms

Nowadays, wind power plants (WPPs) should be able to dynamically change their power output to meet the power demanded by the transmission system operators. When the wind power generation exceeds the power demand, the WPP works in de-loading operation keeping some power reserve to be delivered into the grid to balance the frequency drop. This paper proposes to cast a model predictive control strategy as a multi-objective optimization problem which regulates the power set-points among the turbines in order to track the power demand profile, to maximize the power reserve, as well as to minimize the power losses in the inter-arrays connecting the wind turbines within the wind farm collection grid. The performance of the proposed control approach was evaluated for a wind farm of 12 turbines using a wind farm simulator to model the dynamic behavior of the wake propagation through the wind farm.

Improving power system stability in the presence of wind farms using STATCOM and predictive control strategy

In this study, a multi-objective predictive control strategy is presented for the stability improvement of a power system in the presence of wind farms and STATCOM. The main contribution of this study is in the multi-objective consideration for controlling the active and reactive powers of the rotor-side converter in each of the induction generators, controlling the voltage of the synchronous generators’ excitation system, and designing the damping controller of STATCOM using the predictive strategy. To reduce the computational burden, and to accurately choose the input paths into the predictive control, the Laguerre functions are used. Also, for reducing the sampling time in the selection of large prediction horizons, the exponential data weighting has been employed. The simulation results were evaluated using MATLAB software in the field of time and frequency under different scenarios. Moreover, the obtained results of each domain are compared using the two techniques of the predictive strategy, i.e. the classic model, Laguerre functions, and also the conventional proportional integral controller. The comparison of these three methods reveals that the functional predictive control outfits the two other controllers in damping of the oscillations.

Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC) balancing. To solve these problems, multi-objective predictive balancing control (MOPBC) based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.

Precise train stopping method based on predictive control

Precise train stopping is a key technology of automatic train operation. On the basis of analyzing the train stopping phase,the delay characteristics of brake model and constraint conditions of train characteristics were considered,using generalized predictive control theory,a multi-objective predictive controller with constraints was designed taking account of train speed and distance as control targets and combining the control constraint conditions. The simulation results show that the proposed controller can accurately track the train stopping curve to achieve high precision stopping requirements and higher comfort.