Linear Model Predictive Control of Olefin Metathesis Process

In this paper, a self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented. The controller has been applied to a control study of Fluid Catalytic Cracking (FCC) unit riser temperature, and regenerator temperature respectively. Performance comparison of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID(12.6%)/Fuzzy-PID(6%)); settling time (PID(80 seconds)/Fuzzy-PID(20seconds));Integral absolute error(PID(14.29)/Fuzzy-PID(8.63)); integral square error(PID(6.713).Fuzzy-PID(4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)).Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

An off-line Model Reduction-based Technique for On-line Linear MPC Applications for Nonlinear Large-Scale Distributed Systems

Predictive controllers are used in many in many industries recently. In this paper, a comparison between conventional Proportional plus integral (PI) controller using Ziegler–Nichols tuning method (Z–N) and predictive controllers such as dynamic matrix controller (DMC) and receding horizon control (RHC) are designed. Above-mentioned controllers are applied to a bench mark process quadruple-tank process (QTP) which is operated in nonminimum phase. The simulation for the process and above-mentioned controllers are obtained from MATLAB. Proposed controllers’ performance is supported with better tracking, disturbance rejection, and performance indices in terms of integral time absolute error (ITAE), integral absolute error (IAE) integral square error (ISE).KeywordsZiegler–Nichols tuning methodQuadruple-tank processDynamic matrix controllerReceding horizon controlRelative gain arrayNonminimum phase

Model predictive control of a miniaturized linear single-piston cryocooler

Model predictive control (MPC) as a nonlinear control method, is being studied for a wide variety of power electronics applications targeting various power ranges. The fast dynamic response, robustness under different load conditions, multi-objective control, and simplicity of implementation are the main features of MPC. However, the implementation of finite control set MPC (FCS-MPC) or direct MPC in its basic form for multilevel converters (MLCs) demonstrates the inability to generate the staircase multilevel output voltage waveform in a voltage sourced inverter (VSI) application. Adding a reference evaluation block to the basic FCS-MPC method can significantly improve its multilevel voltage generation capability. In this paper, an enhanced model predictive nearest level control (EMP-NLC) is proposed and implemented on a 5-Level flying capacitor multilevel converter (FCMC). The proposed method is simulated under different load conditions, while a comparison against existing MPC techniques is also presented. Experimental results verified the efficacy of the proposed controller.

An important feature of linear model predictive control (MPC) is the ability to provide offset-free control through integral action. Linear MPC can utilize a steady-state target optimizer (SSTO) in conjunction with a dynamic optimization in order to manage systems that are non-square, have integrating modes, or encounter infeasible setpoints. Integral action does not ensure that the feasible steady-state target is closest to the true optimum when the desired setpoint is infeasible. This paper describes the modifications necessary to linear state-space MPC algorithms in order to address this problem (assuming systems with no integrating modes). The solution employs features of the integrated system optimization and parameter estimation (ISOPE) algorithm: the SSTO cost is modified by a term that results in matching of the true plant and model conditions necessary for optimality. This work combines well with prior work which has determined the situations where the modification is actually necessary.

Model Predictive Control (MPC) is a proven control concept with many applications in the process industry. Popularity of the framework is mainly due to its ability to optimize behavior of the process while respecting physical and economical constraints. The major challenge of implementing MPC in real time on low-cost hardware is the inherent computational complexity. To address this goal, it is proposed to solve a given MPC problem using parametric programming, which encodes the optimal control moves as a lookup table. A great advantage being that such tables can then be processed even with low computational resources and therefore allow MPC to be deployed to low cost control devices. In the paper we present a unique software tool which allows MPC problems to be designed with low human effort, and is capable to automatically generate real-time executable code for various target platforms.

Optimisation algorithms offer a valuable tool for enhancing the energy efficiency of buildings by fine-tuning specific design parameters. Model Predictive Control (MPC) emerges as a compelling technology to address the rising need for improved efficiency and adaptable operation within building energy systems. A simulation study using MATLAB and EnergyPlus was conducted to examine the influence of MPC and building design optimisation (BDO) on wall insulation thickness and internal mass area. The focus was on their impact on heating energy use and indoor thermal comfort in an office room located in Brussels, Belgium. The study revealed that the sole implementation of MPC led to a 7.6% decrease in heating energy use, while the application of BDO resulted in a more significant reduction of 12.8%. Remarkably, the fusion of MPC and BDO yielded the highest energy savings, cutting heating electricity usage by 23.4% compared to the baseline model. Moreover, MPC effectively maintained indoor temperature within the desired thermal comfort boundaries. The optimal wall insulation thickness and internal mass area were also ascertained through BDO, both of which exceeded the levels set by the baseline model. BDO, in conjunction with MPC, demanded the maximum permissible insulation thickness of 320 mm for the external north and south walls. Interestingly, when BDO was combined with MPC, the requirement for the internal mass area reduced by 11.7 m2 compared to utilising BDO alone. The study’s results underscore the potential of integrating MPC with BDO to elevate building energy efficiency. Furthermore, this strategy may be adaptable to optimising other building parameters at the early design stage, thereby augmenting overall building energy efficiency.

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.

Japan’s rapidly aging population and shrinking workforce are creating serious challenges, especially in jobs that require long hours outdoors. To solve this, Japan urgently needs innovative solutions, including automation technologies for outdoor work such as farming, construction, and maintenance. One promising approach is the application of model predictive control (MPC) to outdoor mobile robots. Although MPC has been widely studied in the context of mobile robotics, there remains a paucity of practical research specifically targeting cleaning robots operating in outdoor environments. Alternative approaches, such as geometric path-following methods like pure pursuit, are frequently employed in simpler applications, but often encounter limitations in achieving high-precision trajectory tracking. This study proposes a model predictive contouring control (MPCC) framework for trajectory tracking in outdoor cleaning robots. The proposed method offers the capability to balance the trade-off between execution time and trajectory accuracy. Both simulation and experimental results validate the effectiveness of the proposed MPCC approach.

Energy efficient model predictive building temperature control

The operating conditions of wastewater treatment process is time-varying and nonlinear. In the study of biochemical reaction process, there are coupling relations and size differences among variables of non-single controlled objects. In order to solve the multi-variable control problem, this paper use the Model Predictive Control (MPC) based on neural network. A number of control factors are considered in the design process and by constructing the cost function, the controller is developed in the expected direction. In addition, at the actual working condition change, the single working condition change is very difficult to maintain for a long time. In this paper, a multi-condition identification model predictive control is established by using T-S model. The scheme can make the controller more fit with the change of state. In order to verify the effectiveness of the designed controller and guarantee the data authenticity, the multi-state condition of this paper is constructed by using the real data provided by BSM1.The experimental results show that the designed controller is more stable at single condition and multi-condition, and the control accuracy has been improved obviously. The advantages of the controller are also illustrated by the evaluation indexes of IAE (Integral Absolute Error) and ISE (Integral Square Error).

A successful implementation of Model Predictive Control (MPC) requires appropriately tuned parameters. This paper presents a novel tuning approach based on Artificial Neural Network (ANN). To build the data learning base of the ANN, we adopted the Particle Swarm Optimisation (PSO) method, and we used the reliable algorithm, Online Sequential Extreme-Learning-Machine (OS-ELM) to learn the ANN. The objective of this work is to show that good tuning of MPC parameters makes it possible to reach closed-loop stability and ensure robustness against disturbances and sensor noises, without using robustification approaches in addition to MPC. The effectiveness of our approach is brought to light by comparing the obtained performances to other MPC tuning approaches without disturbances, and also to a robustified Generalized Predictive Control (GPC) using Youla parametrisation in the presence of disturbances.

The effect of including hydronic radiator dynamics in model predictive control of space heating

The challenges of contingent island operation of hydropower plants are addressed by proposing an enhanced frequency control with advanced control algorithms. The study is based on a real power plant and considers the relevant power system in its entirety along with the load and transmission lines. Integrated transfer functions between the guide vane opening of a hydro turbine and the frequency of the associated generator were determined by in-situ identification. A model predictive controller, a fractional order PID controller, and a PID controller utilizing integral absolute error and integral time absolute error criteria were designed and developed. The performance of the proposed algorithms with the identified plant model was tested on a NI-cRIO 9049 FPGA platform. Improved frequency control in terms of setpoint change was achieved with the MPC and FOPID controllers. The MPC controller also features betters disturbance rejection compared to conventional PID controllers, which are tuned according to integral error criteria. The considered algorithms have been simulated in different operating scenarios, such as variations of active power load and transmission length. The proposed approach enhances stability and consequently avoids operational failures of hydropower plants in contingent islanding mode.