Generalized Predictive Control Research Articles

Study on noise and disturbance issues of generalized predictive speed control for permanent magnet synchronous machines

Generalized predictive control (GPC) is promising for permanent magnet synchronous machine drive systems, due to its outstanding dynamic performance and disturbance rejection capability. However, the speed measurement noise introduced by the encoder and the unmodelled disturbances introduced by the non-ideal machine characteristics deteriorate the performance of GPC. The noise and disturbance issues are studied in detail, and an enhanced GPC method is proposed to address them. For the noise issue, the mathematical model of the speed measurement noise is derived, and the noise spectra at different speeds are analysed. Then, a novel GPC with internal low-pass filter is proposed to reject the influence of the noise. For the disturbance issue, the influence of the unmodelled disturbances on the speed performance is assessed when applying different noise rejection methods, and a resonant controller-based speed ripple mitigation method is proposed and integrated to the GPC. Experimental results show that the proposed methods can effectively deal with the noise and disturbance issues, as well as providing better dynamic performance than the conventional PI based speed control.

Adaptive Predictive Control of a data center cooling unit

As server densities increase to support the rising demand for high density computing, traditional room cooling infrastructure is struggling to keep up. Rack-mounted cooling units are increasingly being deployed in data centers. These cooling units are located closer to the heat sources (the servers) which allow them to cool more efficiently than traditional cooling infrastructure. In this work, a Multi-Input Single-Output (MISO) Adaptive Predictive Controller (APC) for rack-mounted cooling units is investigated and implemented using a low-cost general-purpose microcontroller. The proposed APC is implemented using Weighted Recursive Least Squares (WRLS) and a sub-optimal but fast algorithm based on the General Predictive Controller (GPC) approach. These are combined with a variable forgetting factor and variable prediction horizon algorithms. In addition, methods are proposed to handle stability issues arising due to practical hardware limitations. The controller is implemented on a real single rack system and challenges for practical implementation are addressed and illustrated. The proposed APC is also compared (via simulation) to a standard APC with equivalent complexity and a split-range PI controller. The results show that the proposed controller outperforms both the standard APC and the split-range PI controllers with respect to Mean Squared Error (MSE).

A General Double Vector-Based Model Predictive Current Control for the Dual Three-Phase Motors

Traditional model predictive current control (MPCC) for motors can only choose one optimal voltage vector during one control period, which creates problems of over-regulation or under-regulation for the current tracking. With zero vectors being injected in the chosen optimal voltage vector, the traditional MPCC can obtain better performance, which is called duty cycle MPCC. However, whether the traditional or the duty cycle MPCC is being applied to multiphase motors, it is more difficult for the phase to increase. In this paper, a general double vector-based MPCC mechanism has been studied for a dual three-phase permanent synchronous motor (PMSM) with dual Y shift 30° windings used in aerospace propulsion. Firstly, the choosing range of the second voltage vector in duty cycle MPCC was extended to an arbitrary vector; then, the cost function was rationally designed, and the delay compensation was added to improve the control performance as well. Compared with the traditional or duty cycle MPCC, this general double vector-based MPCC has better torque performance and fewer total harmonic distortions in the full speed range and under different load conditions.

Survey of Optimal Control and Model Predictive Control with State Estimation and a Real Time Application

Abstract The optimal control and its limited version namely the model predictive control represent one of the most important nonlinear control alternatives nowadays. The success of them are also proven in many practical applications. These can provide for several industrial applications the optimal trajectory calculation as well as calculation of the real-time control signal. One successful version of this is Generalized Predictive Control (GPC). A big advantage of these control algorithms is that they solutions are able to take into account the limitations of the inputs, and the states. In some cases, it is important to know the mathematical model chosen and the complete state information. Otherwise, the model can be estimated during the operation. Our study shows through the control of the cathode heating of a high-power electron beam device the self-tuning adaptive control thus constructed. Using a suitable dynamic model and an extended Kalman estimator, we determine the estimated temperature of the two cathodes during operation and the saturation electron current, which ensures the maximum cathode life. The practical application was tested on a CTW 5/60 type electron gun.

Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models

Chemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a challenging task. The model predictive controller (MPC) poses a viable solution to handle the variability. This paper addresses the design of predictive controller for the cement grinding process using the state-space model and the implementation of this industrially prevalent predictive controller in a real-time cement plant simulator. The real-time simulator provides a realistic environment for testing the controllers. Both the designed state-space predictive controller (SSMPC) in this work and the generalised predictive controller (GPC) are tested in an industrially recognized real-time simulator ECS/CEMulator available at FLSmidthPvt. Ltd., Chennai, by introducing a grindability factor from 33 to 27 (the lower the grindability factor, the harder the clinker) to the clinkers. Both the predictive controllers can maintain product quality for the hardest clinkers, whereas the existing controller maintains the product quality only up to the grindability factor of 30.

Process control via random forest classification of profile signals: An application to a tapping process

Abstract Due to technological advancements, many manufacturing processes are now real-time monitored through sensors that provide continuous signals of the process parameters rather than providing simpler point observations of the process response. Signals (profiles) obtained through these sensors can reveal important information about the quality of the process being monitored. In this work, we propose a general predictive control framework for on-line process quality monitoring where data is available in the form of a profile. The proposed framework is an integration of ideas from classical on-line process control and advanced machine learning techniques, namely, Random Forests. The proposed framework has the advantages of being more interpretable compared to other methods found in the literature, and has the flexibility to include several commonly used transformations of the signal as features. In addition, abnormal out of control signal characteristics of the process known from experience by operators can be easily incorporated in the random forest technique. An illustration of the proposed framework applied to the case of a tapping manufacturing process is provided. Model comparison results show a superior performance of the proposed framework over previously proposed monitoring methods for the considered tapping process. From a receiver operating characteristic curve analysis, it was found that an area under the curve (AUC) of 0.923 was achieved by the proposed model compared to an AUC of 0.867 for the Generalized Variance model proposed in the literature.

Stepped generalized predictive control of test tank temperature based on backpropagation neural network

It is significant to ensure the temperature stability of the test tank, which has a direct impact on reducing production energy consumption, improving tank quality and ensuring experimental results. However, the high nonlinearity and long delay of the temperature control process in the test tank make it difficult to satisfactorily control the temperature by traditional control methods. To solve the problem, this paper designs a temperature prediction model for the test tank based on backpropagation neural network (BPNN), which has a good fitting ability for nonlinear systems. The proposed model was coupled with improved generalized predictive control (GPC) into a new test tank temperature control method, namely, BPNN-based stepped GPC. Simulation results show that the new control method could reduce the prediction error of the BPNN and effectively control the temperature of the test tank.

Ramp-Tracking Generalized Predictive Control System-Based on Second-Order Difference

This brief proposes an adaptation of the Generalized Predictive Control (GPC) for ramp-reference tracking. The second-order difference operation and the plant model are used to get an augmented model with two embedded integrators and whose output is the tracking error. Differently from other GPC-based tracking algorithms, the proposed approach does not require information about the reference parameters, and the GPC prediction horizon is composed of the predicted errors instead of the expected plant outputs. Thus, the optimization function and the receding horizon strategy used in conventional GPC can be applied to get the control law. Simulation and experimental results prove that the proposed approach can successfully track constant and ramp references. The proposed method is applicable for single-input single-outputs plants. However, the mathematical background presented in this brief can be used in the development of new GPC strategies.

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.

Fuzzy K-Means Cluster Based Generalized Predictive Control of Ultra Supercritical Power Plant

This article proposes a fuzzy k-means cluster based generalized predictive control (GPC) method for a 1000 MW ultra supercritical power plant to improve of the boiler combustion efficiency. First, to fully use the statistic characteristic of the historical data, a fuzzy k-mean cluster network (FKN) is well constructed to derive the local linear models, and the nonlinear dynamic process of studied system is elaborately approximated by the fuzzy combination of the local linear models. Then, a global GPC method is proposed to improve the control performance by using the membership of the current FKN. Different from the traditional GPC, the advantage of proposed GPC is that local GPC is fuzzily combined together to achieve the purpose of global GPC by a scheduling algorithm. Finally, an example illustrates that the proposed control strategy can achieve the satisfactory performance.

Closed-loop dynamic real-time optimization (CL-DRTO) of a bioethanol distillation process using an advanced multilayer control architecture

The ethanol production has become an important part of the worldwide economy driven by its use as renewable energy and environmentally clean fuel. To obtain a high-level quality product, a large quantity of energy is used in the distillation stage. This work proposes to optimize the bioethanol production process applying a closed-loop dynamic real-time optimization (CL-DRTO) framework associated with advanced control strategies in the ethanol distillation facilities to improve production and minimize energy losses. A high-fidelity computational platform is developed using a software-in-the-loop (SIL) technique to demonstrate the feasible application in real cases. OLE (Object Linking and Embedding) Automation integrates Matlab and Aspen Hysys software to simulate practical scenarios currently applied in the industry. The optimization and control algorithms are developed in Matlab and the ethanol distillation process is modeled in the Aspen Hysys. Different advanced control strategies, such as IHMPC (Infinite Horizon Model Predictive Control), MIMO FSP (Filtered Smith-Predictor) and DTCGPC (Dead-Time Compensator Generalized Predictive Controller), are used to overcome the process dynamic issues, for instance, strong nonlinearity, dead-times, long time constants and coupled loops, on ethanol distillation process. A CL-DRTO layer is developed in the proposed control structure to consider economic and production aspects, guiding the process to optimal operating points. Performance indices and computational effort are used to evaluate the control behavior for disturbance rejection and reference tracking scenarios. Furthermore, economic performance is analyzed to ensure the advantages of the proposal. Results show that the proposed computational platform is able to reproduce industrial scenarios with high-fidelity. In addition, preliminary results demonstrate that advanced control structure can improve the production and profitability of the bioethanol distillery and present a powerful alternative to replace classical controllers currently used in this type of industry.

Multivariable generalised predictive control with measurement noise rejection and speed ripple mitigation for PMSM drives

Generalised predictive control (GPC) is known for its good dynamic performance and long prediction horizon, but the performance can be severely deteriorated when measurement noises exist due to the wide control bandwidth of GPC. Meanwhile, the unmodelled periodic disturbances can cause ripples to the output variables. In this study, a practical permanent magnet synchronous motor (PMSM) drive system with speed measurement noise and periodic disturbances is considered, for which a novel multivariable GPC method is proposed to achieve both good dynamic performance and speed ripple mitigation. The proposed method has a simple structure, since the traditional cascaded speed and current control loops are replaced by a non-cascaded one. To reject the measurement noise without jeopardising the system stability, an internal low-pass filter is embedded in the GPC. Meanwhile, external resonant loops are added to the GPC to mitigate the low-order speed ripples caused by the periodic disturbances. Furthermore, a deadbeat-based current constraint method is proposed to avoid overcurrent during transient processes. Theoretical stability analysis of the proposed method is presented. Experimental results show that the proposed method has good steady-state and dynamic performances, including measurement noise rejection and speed ripple mitigation.

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.

Characterization of Predictive Control Based on Model (MPC) in Multivariable Process of Milling in a Mineral Concentrator Plant

In this article, the simulation level characterization of the predictive control system -based in multivariable model (MPC) is developed, without restrictions in a milling process of a mineral concentrator plant. The multivariability of the process is considered and is evaluated the interaction between the variables. The control strategy that integrates all the control actions is developed, assessing the robustness in the application against disturbances. Generalized predictive control (GPC) is used, presenting the methodology in accordance with the philosophy of predictive control MPC, based on an initial modeling of the process, developed in reference [1]. The case study includes the use of a ball mill in a process of 4 input variables by 4 output (MIMO 4x4), where one of the output variables of greater control is the size of the mineral particles in an iron mine. The results of the control are evaluated observing and discussing the temporal responses in all the variables, the robustness of the control system is evaluated considering the response of the system before the application of multiple disturbances. Results are presented in various simulation scenarios using MATLAB.

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.

Suboptimal midcourse guidance design using generalized model predictive spread control

A new generalized model predictive spread control technique is presented for the midcourse guidance of interceptors that are designed to intercept high-speed ballistic missile targets. Because of using the basis functions, this new technique is further computationally efficient over the model predictive static programming technique. Also, the smoothness of the control variable is guaranteed for the smooth basis functions. For demonstrating the performance of the proposed technique, an interceptor midcourse guidance problem with an angle constraint is formulated and solved to intercept an incoming ballistic missile target successfully. Additionally, the results are compared with those of the midcourse guidance design using the model predictive static programming technique. A comparative study of the new technique has also been conducted with the quasi-spectral model predictive static programming technique proposed earlier in the literature. It has been observed that the orthogonality of the basis functions is a necessary assumption and without that, the quasi-spectral model predictive static programming technique is not a near-optimal technique. By using the new technique based on Legendre basis functions, the solution converges to the model predictive static programming method solution by increasing the number of basis functions with less computational load.

A Design Method of Generalized Predictive Control Systems in Consideration of Noise

Generalized Predictive Control (GPC) is one of the model-based control methods. The control law is derived by minimizing performance index, which includes the squares of control input and the squares of the error between reference signal and output prediction, in order to make the derived control system be stable. Although coprime factorization approach has been used to extend the conventional control law in the previous researches, there has been a possibility that the order of the derived control law becomes high. Therefore, this paper extends GPC through newly-defined output prediction and proposes the method to redesign the control law or the disturbance response with keeping the closed-loop transfer function. Numerical example is shown to check the characteristic of the proposed method.

Balancing-Aware Charging Strategy for Series-Connected Lithium-Ion Cells: A Nonlinear Model Predictive Control Approach

Charge unbalancing in series-connected cells can lead to lower storage capacity and shorter battery life. Model-based optimization strategies have proven to be very effective in addressing this problem. In this article, we propose a general nonlinear model predictive control (NMPC) scheme for obtaining a balancing-aware optimal charging. The presented method relies on an electrochemical model, tailored for control purposes. In view of the possibility of practical implementation, the concepts are subsequently specialized for an easily implementable power supply scheme. Finally, the NMPC approach is validated on commercial cells using a detailed battery simulator, with sound evidence of its effectiveness both under the assumption of full state availability and in the presence of an observer scheme.

A generalized predictive control for remote cardiovascular surgical systems

Robot-assisted cardiovascular surgery is used to avoid surgeon suffering from X-ray radiation and relieve fatigue caused by long-time standing wearing protective clothing. Its remote surgery can alleviate the lack of experienced doctors in remote areas. Due to the existence of time-delay phenomena, flexible deformation and nonlinearity of interventional instruments, it is difficult to ensure system transparency. This paper analyzes the evaluation index of system transparency. A generalized predictive control (GPC) is developed to suppress the effect of time-varying delay and parameter identification error. Moreover, a terminal sliding mode controller (SMC) is designed to improve the robustness of the system under consideration. Simulation results are provided to show that the proposed control strategy can improve transparency of the remote vascular interventional surgery system.