Generalized Predictive Control Controller Research Articles

Advanced embedded generalized predictive controller based on fuzzy gain scheduling for agricultural sprayers with dead zone nonlinearities

Variable rate application of pesticides in agriculture can improve pest control and also increase food production. Nevertheless, incorrect spraying poses risks to the environment and human health, as well as may increase the total cost of production. Nowadays, it is quite known the importance of innovation in techniques and technologies to improve the spraying process in a variable rate application for pest control. This work presents a generalized predictive control (GPC) strategy to cope with nonlinearities. An extension of the stability analysis for constrained GPC controller for infinite horizons is also developed, which guarantees the stability of a fuzzy GPC for a limited variation of its tuning parameters λ and δ. Results show the usefulness in adding a fuzzy logic system, to cope with nonlinearities, leading to a conception of an advanced fuzzy GPC for a limited variation of its tuning parameters.

Multi-Area Load Frequency Control in Power System Integrated With Wind Farms Using Fuzzy Generalized Predictive Control Method

Frequency security is critical for the power systems’ stability and reliability. The integration of renewable energy resources brings new challenges to the frequency security of power systems. To provide a more robust control method for controlling the frequency and tie-line power flow of an interconnected power system integrated with wind farms (IPSWF), a load frequency control (LFC) controller of generalized predictive control (GPC) based on the Takagi-Sugeno (T–S) fuzzy model (fuzzy-GPC) is proposed in this paper. First, the T–S fuzzy model of the IPSWF is constructed. Then, the GPC is used to design the controller. Lastly, to validate the proposed scheme, an LFC strategy is established for a two-area interconnected power system integrated with a wind farm in power systems computer aided design (PSCAD). The control performance of proportional-integral, GPC, and fuzzy-GPC controller are compared under two faulty operating conditions. The simulation results show that the performance indicators and the dynamic behavior when the fuzzy-GPC controller is used are better than the performance when the PI controller and GPC controller are used. The proposed fuzzy-GPC used in the LFC of the IPSWF can regulate the frequency deviation and tie-line power deviation adaptively and achieve minimum frequency deviation and tie-line power deviation in a multi-area IPSWF.

Thermal management of an unloaded hybrid dryer by generalized predictive control

The temperature control inside a hybrid dryer has always remained the main concern in drying applications. At present, the majority of the drying community researchers use classical controllers to command the thermal environment inside the drying chamber. Such a control strategy leads to diminish the dryer efficiency and alters the quality of the dried products. In this regard, the objective of this work is to develop an advanced temperature control system for the thermal management of a hybrid solar-electrical dryer. In addition, the proposed system must be convenient, efficient, and affordable in developing countries. A Generalized Predictive Controller (GPC) is designed and validated for temperature control covering a wide range of 40 °C–75 °C. Mathematical and identified models are developed to represent the thermal behavior of the dryer. The identified linear model is used to design the GPC controller. This controller has proven great feasibility and effectiveness by maintaining the chamber temperature with a settling time that remained under 22 min, with a static error of 0.5 °C and no overshoot or dips. Moreover, the GPC algorithm is implemented with an Arduino board which represents an easy, and low-cost solution for temperature control in drying applications.

Nonlinear generalized predictive controller based on ensemble of NARX models for industrial gas turbine engine

New design and operation of modern gas turbine engines (GTEs) are becoming more and more complex where several limitations and control modes should be fulfilled at the same time to accomplish a safe and ideal performance for the engine. For this purpose, a constrained multi-input multi-output (MIMO) non-linear model predictive controller (NMPC) based on neural network model is designed to fulfill the control requirements of a Siemens SGT-A65 three-spool aero-derivative gas turbine engine (ADGTE) used for power generation. However, the implementation of NMPC in real time has two challenges: Firstly, the design of an accurate non-linear model, which can run many times faster than real time. Secondly, the usage of a rapid and reliable optimization algorithm to solve the optimization problem in real time. To solve these issues, the constrained MIMO NMPC is created based on the generalized predictive control (GPC) algorithm as a result of its clarity, ease of use, and capacity to deal with problems in one algorithm. In addition, seven ensembles of eight multi-input single-output (MISO) non-linear autoregressive network with exogenous inputs (NARX) models are used as a base model for the GPC controller to predict the future process outputs. Estimation of free and forced responses of the GPC based on the neural network (NN) model of the plant each sampling time without performing instantaneous linearization is proposed in this study, which reduces the NMPC optimization problem to a linear optimization problem at each sampling step. In addition, the Hildreth's quadratic programming algorithm is used to solve the quadratic optimization problem within the NMPC controller, which offers ease of use and reliability in real time applications. To demonstrate the performance of the NNGPC controller developed in this study, we have compared the performance of the neural network generalized predictive control (NNGPC) controller to the existing controller of the SGT-A65 engine. The simulation results show that the NNGPC has demonstrated output responses with less oscillatory behavior and smoother control actions to the sudden variation in the electric load disturbance than those observed in the existing min-max controller. However, the min-max controller has faster response than that of the NNGPC controller.

GPC and PI Controllers Applied to an Aerothermic Process

The aerothermic process is a laboratory ventilation and heating system. It is assembled with a heating grate and fan systems, completely associated through the data acquisition system (Humusoft MF624) for real-time control. Its air temperature variable constitutes an element that must be operated for energy saving. In order to keep this variable around a desired value, this article presents an experimental comparison between generalized predictive control (GPC) and integral proportional controller (PI). Both techniques are designed using a model obtained from experimental online data. The effectiveness of two methods is demonstrated by an implementation on an aerothermic process. Experimental results show that the main control objectives, such as set-point tracking and the perturbation rejection, are well achieved. The obtained results in closed loop of the PI controller, are promising in comparison to those the GPC ones.

In Silico Testing of a Closed-Loop Artificial Pancreas Based on Generalized Predictive Control

The artificial pancreas (AP) system is a promising approach to maintain blood glucose concentration (BGC) of patients with type 1 diabetes (T1D) in a euglycemic range (70–180 mg/dl). A controller based on generalized predictive control (GPC) with an adaptive reference glucose trajectory and an adaptive softening factor (GPC+AA) was proposed for AP system in our previous research. It was built and tested with the University of Virginia (UVA)/Padova type 1 diabetes mellitus simulator (T1DMS), which embodies the biophysiological parameters of in-silico population accepted by the US Food and Drug Administration. It was showed that the GPC+AA controller realized effective control of the BGC of in-silico patients with T1D. Here, the effectiveness of the GPC+AA controller was further tested with strict conditions, including a pump with noise and error, a continuous glucose monitoring sensor and a high carbohydrate intake. Test results showed that the BGC of adolescent group was not effectively controlled with the GPC+AA controller. Then, the adaptive softening factor strategy was further optimized and a GPC controller with an adaptive reference glucose trajectory and an optimized adaptive softening factor (GPC+AO) was proposed here. It was demonstrated that the optimized adaptive softening factor strategy significantly improved the performance of the GPC controller, and the average BGC percentage within the euglycemic range of 9 adults and 10 adolescents increased to 80% and 70%, respectively. Thus, our GPC+AO controller is effective for patients with T1D and can be potentially applied in the AP systems.

The Model Order Reduction Method as an Effective Way to Implement GPC Controller for Multidimensional Objects

The paper addresses issues associated with implementing GPC controllers in systems with multiple input signals. Depending on the method of identification, the resulting models may be of a high order and when applied to a control/regulation law, may result in numerical errors due to the limitations of representing values in double-precision floating point numbers. This phenomenon is to be avoided, because even if the model is correct, the resulting numerical errors will lead to poor control performance. An effective way to identify, and at the same time eliminate, this unfavorable feature is to reduce the model order. A method of model order reduction is presented in this paper that effectively mitigates these issues. In this paper, the Generalized Predictive Control (GPC) algorithm is presented, followed by a discussion of the conditions that result in high order models. Examples are included where the discussed problem is demonstrated along with the subsequent results after the reduction. The obtained results and formulated conclusions are valuable for industry practitioners who implement a predictive control in industry.

Research on coordinated control system of drum boiler units considering energy demand decoupling

This paper proposes a coordinated control system (CCS) of a drum boiler unit considering energy demand decoupling. Since there is large delay and inertia in the response from the coal feed flow to the main steam pressure, the generalized predictive control (GPC) algorithm is adopted to design the CCS. As the coupling between the boiler and the turbine is serious, an energy demand signal is constructed and integrated into the GPC controller to realize decoupling control of the unit. Based on evaluation rules, a load command signal is reconstructed to improve the adaptability of the unit to the grid. Simulation results show that the CCS can track the load command rapidly, overcome the nonlinearity and backlash of governor valve, and ensure the safe and stable operation of the unit. Application results show that the CCS performs well during load up, load down, and load swing. The performance indicator exceeds 4.0 for one month, and its maximum value is up to 5.09. Simultaneously, the unit maintains a high degree of stability, the overshoot of coal feed flow is almost zero, the fluctuations of steam pressure and temperature are small. Obviously, the proposed CCS is advanced and practical.

Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings

Ventilation, heating and air conditioning systems are the main energy consumers in building sector. Improving the energy consumption of these systems, while satisfying the occupants’ comfort, is the major concern of control and automation designers and researchers. Model predictive control (MPC) methods have been widely studied in order to reduce the energy usage while enhancing the occupants’ comfort. In this paper, a generalized predictive control (GPC) algorithm based on controlled auto-regressive integrated moving average is investigated for standalone ventilation systems’ control. A building’s ventilation system is first modeled together with the GPC and MPC controllers. Simulations have been conducted for validation purposes and are structured into two main parts. In the first part, we compare the MPC with two traditional controllers, while the second part is dedicated to the comparison of the MPC against the GPC controller. Simulation results show the effectiveness of the GPC in reducing the energy consumption by about 4.34% while providing significant indoor air quality improvement.

Predictive Speed-Control Algorithm Based on a Novel Extended-State Observer for PMSM Drives

To enhance the control performance of permanent-magnet synchronous motor (PMSM) drive systems, achieving high-precision motion control, a generalized predictive control (GPC) method based on a novel extended-state observer (ESO) is investigated for the speed control of PMSM. In this paper, the controller design consists of two steps. Firstly, according to the continuous time model of PMSM, using the Taylor series expansion, the predictive value of motor speed in finite time is derived, and the single-loop speed controller by combining the speed loop and q - axis current loop is obtained through the defined cost function. The structure of the controller is simple compared to other forms. Secondly, considering the uncertainty of the load torque and the model uncertainties, a novel extended-state observer is designed to compute the actual torque, and the observed value is introduced to the GPC controller. The simulation and experimental results show that the proposed GPC+ESO control method has superior dynamic performance and strong robustness.

A Generalized Predictive Control-Based Path Following Method for Parafoil Systems in Wind Environments

Parafoil systems represent flexible wing vehicles. In case a vehicle is flying at low altitude, it is well known that the vehicle is more susceptible to winds. Also, due to the nonlinear, large inertial existing within the system, traditional control methods, such as traditional proportional-integral-derivative (PID), cannot guarantee the quality of path following. Therefore, we here apply generalized predictive control (GPC)-based method for parafoil systems to follow the designed path for a better control effect. To achieve this, we first propose a novel modeling method based on computational fluid dynamics to build a dynamic model of the parafoil system in windy environments. Afterward, a guidance law is designed according to a hybrid approach that combines the cross track error and the line of sight. In addition, the path following controller is established by using GPC. Finally, we generate and interpret numerical results to demonstrate the feasibility of the horizontal path following method in windy environments by utilizing the semi-physical simulation platform. The achieved results show that the GPC controller achieves high precision path following. More precisely, it possesses a better anti-wind ability and tracking accuracy and, therefore, the method outperforms PID controller.

Improved Generalized Predictive Control Algorithm for Blood Glucose Control of Type 1 Diabetes.

Artificial pancreas (AP) is an important treatment for patients with Type 1 diabetes (T1D). The control algorithm adopted in an AP system determines its reliability and accuracy. The generalized predictive control (GPC) is a representative adaptive control algorithm and has been widely applied to AP systems. However, we found that the traditional GPC controller does not work well for adolescents with T1D because of their high-fluctuating blood glucose and high insulin resistance. Here, we propose an improved GPC algorithm with an adaptive reference glucose trajectory and an adaptive softening factor. The slopes of the reference trajectory and the value of softening factor are calculated real-time on the basis of the blood glucose concentration (BGC) variations. In silico testing was done using the US Food and Drug Administration (FDA) approved virtual patient software T1D mellitus. The BGC trace and density of 20 patient-subjects (10 adults and 10 adolescents) were recorded. Results showed that the average BGC percentage within the target regions (70-180mg/dL) of the tests with adaptive reference glucose trajectory and softening factor for adolescents (0.93±0.07) was significantly higher than that of the traditional GPC algorithm tests (0.88±0.11), suggesting that the control quality of the blood glucose of adolescents is significantly improved with our GPC algorithm. Therefore, our improved GPC controller is effective and should have a good applicability in AP systems.

An adaptive fuzzy predictive control of nonlinear processes based on Multi-Kernel least squares support vector regression

In this paper, an adaptive fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi–Sugeno system based Multi-Kernel Least Squares Support Vector Regression (TS-LSSVR). The proposed adaptive TS-LSSVR strategy is constructed using a multi-kernel least squares support vector regression where the learning procedure of the proposed TS-LSSVR is achieved in three steps: In the first step, which is an offline step, the antecedent parameters of the TS-LSSVR are initialized using a fuzzy c-means clustering algorithm. The second step, which is an online step, deals with the adaptation of the antecedent parameters which can be implemented using a back-propagation algorithm. Finally, the last online step is to use the Fixed-Budget Kernel Recursive Least Squares algorithm to obtain the consequent parameters. Furthermore, an adaptive generalized predictive control for nonlinear systems is introduced by integrating the proposed adaptive TS-LSSVR into the generalized predictive controller (GPC). The reliability of the proposed adaptive TS-LSSVR GPC controller is investigated by controlling two nonlinear systems: A surge tank and continuous stirred tank reactor (CSTR) systems. The proposed TS-LSSVR GPC controller has demonstrated good results and efficiently controlled the nonlinear plants. Furthermore, the adaptive TS-LSSVR GPC has the ability to deal with disturbances and variations in the nonlinear systems.

Design of a Generalized Predictive Controller for Temperature Control in a Cement Rotary Kiln

In this paper a generalized predictive controller (GPC) for effective control of clinkerization temperature in a cement rotary kiln is designed. By using methods for identification of dynamic systems a mathematical model of the plant under study is obtained, whose validation results showed a high adequacy degree. The design of the GPC controller is performed based on the mathematical model obtained. It is shown that the designed controller allows controlling the plant under study with a high accuracy considering different real industrial operation scenarios. The comparative simulation results of the control system with PID and GPC controllers showed a better performance when the GPC controller is applied.

Characteristic Model-based Generalized Predictive Control for Electro-hydraulic System with Dead-zones and Parameter Uncertainty

In the paper, the electro-hydraulic system (EHS) is regulated by the proportional directional valve (PDV) which has nonlinearities such as the dead-zone. Furthermore, the effective bulk modulus varies with the temperature and the entrained air in EHS; there exist the nonlinear friction forces and the unmodeled dynamics as well as the square root relationship between the flow rate and the pressure in EHS. All these factors make it difficult to design the controller for EHS. The characteristic model (CM) is used to describe the relationship between input and output of EHS whose exact mathematical model is difficult to obtain. The gradient method through projection modification is utilized to estimate parameters of the CM. The CM-based controller with generalized predictive control (GPC) is designed to make the load track the desired position precisely. Simulations demonstrate that the CM-based GPC controller has a better performance in dead-zone compensation and adaptation with parameter variation than the proportional integral derivative (PID) controller.

A new T-S fuzzy model predictive control for nonlinear processes

A new Takagi-Sugeno system based Kernel ridge regression (TS-KRR) was proposed.The TS-KRR strategy is implemented for both adaptive and offline identification.The TS-KRR was integrated with the GPC to control discrete-time nonlinear systems.The proposed controller showed good results in TS fuzzy GPC with offline modeling.The adaptive TS fuzzy GPC showed good results in dealing with disturbances. In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems.

Assessment of predictive control performance using fractal measures

This paper presents novel approach to the task of control performance assessment. Proposed approach does not require any a priori knowledge on process model and uses control error time series data using nonlinear dynamical fractal persistence measures. Notion of the rescaled range R/S plots with estimation of Hurst exponent is applied. Crossover phenomenon is observed in data being investigated and discussed. Paper starts with industrial engineering rationale. Review of the control error histogram is followed by statistical analysis of probabilistic distribution functions (PDFs). Lévy alpha -stable PDF parameters seem to be best fitted. They directly lead to the fractal analysis using Hurst exponents and R/S plot crossover points. The evaluation aims at performance of the generalized predictive control (GPC) and discusses freshly introduced loop performance quality sensitivity against design parameters of the GPC controller.

Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model

Battery temperature is a primary factor affecting the battery performance, and suitable battery temperature control in particular internal temperature control can not only guarantee battery safety but also improve its efficiency. This is however challenging as current controller designs for battery charging have no mechanisms to incorporate such information. This paper proposes a novel battery charging control strategy which applies the constrained generalized predictive control (GPC) to charge a LiFePO4 battery based on a newly developed coupled thermoelectric model. The control target primarily aims to maintain the battery cell internal temperature within a desirable range while delivering fast charging. To achieve this, the coupled thermoelectric model is firstly introduced to capture the battery behaviours in particular SOC and internal temperature which are not directly measurable in practice. Then a controlled auto-regressive integrated moving average (CARIMA) model whose parameters are identified by the recursive least squares (RLS) algorithm is developed as an online self-tuning predictive model for a GPC controller. Then the constrained generalized predictive controller is developed to control the charging current. Experiment results confirm the effectiveness of the proposed control strategy. Further, the best region of heat dissipation rate and proper internal temperature set-points are also investigated and analysed.

GPC Temperature Control of A Simulation Model Infant-Incubator and Practice with Arduino Board

The thermal environment surrounding preterm neonates in closed incubators is regulated via air temperature control mode. At present, these control modes do not take account of all the thermal parameters involved in a pattern of incubator such as the thermal parameters of preterm neonates (birth weight < 1000grams). The objective of this work is to design and validate a generalized predictive control (GPC) that takes into account the closed incubator model as well as the newborn premature model. Then, we implemented this control law on a DRAGER neonatal incubator with and without newborn using microcontroller card. Methods: The design of the predictive control law is based on a prediction model. The developed model allows us to take into account all the thermal exchanges (radioactive, conductive, convective and evaporative) and the various interactions between the environment of the incubator and the premature newborn. Results: The predictive control law and the simulation model developed in Matlab/Simulink environment make it possible to evaluate the quality of the mode of control of the air temperature to which newborn must be raised. The results of the simulation and implementation of the air temperature inside the incubator (with newborn and without newborn) prove the feasibility and effectiveness of the proposed GPC controller compared with a proportional–integral–derivative controller (PID controller).

Generalized Predictive Control Applied to Motor Drive Axis

The topic of this article is the adaptive generalized predictive control (GPC) applied to the control of the speed of a digital axis. The system is used in CNC machine tools. Usually, the control of digital axes must obey quickly and effectively to changes in the input variable and continuously adapting with the machining conditions, and oppose the influence of external disturbances. However, The Online Recursive Least Squares with Forgetting factor was adopted to estimate inreal-time the system parameters and adjust instantaneously GPC controller parameters based on a modeling CARIMA (Controlled Auto-Regressive Integrated Moving Average). The results of this control architecture have achieved the desired local performance with good rejection of load disturbances and a good robustness for the parametric variations due to operating point changes.