Multiple Model Predictive Control Research Articles

Operational safety of an ammonia process network via model predictive control

Process safety is a crucial issue in the area of process systems engineering as accident prevention is a top priority in process operations. Operational safety needs to be directly incorporated into control system design to handle disturbances in the process. Motivated by the above considerations, this paper presents a practical application simulating multiple model predictive controllers within a multi-unit ammonia process, all of which integrate safety constraints of the process within their design. Specifically, catalytic deactivation in the shift reactor is a common and problematic disturbance that may trigger reaction thermal runaway in the methanator. Two controllers designed with the objective of improving operational safety are implemented on the ammonia plant under catalyst deactivation. Aspen Plus is a commercial process simulation software which is integrated with Matlab to run a closed-loop simulation for the ammonia process. The results demonstrate that under suitable design of the controllers, desirable closed-loop performance is achieved with high temperature extremities avoided during operation.

Asteroid precision landing via Probabilistic Multiple-Horizon Multiple-Model Predictive Control

This paper seeks to provide a probabilistic control framework, named Probabilistic Multiple-Horizon Multiple-Model Predictive Control, for the soft and precise landing on an asteroid. The modified version of the Predictive Path Planning method is also introduced to generate a safe and smooth reference trajectory for landing. The Probabilistic Multiple-Horizon Multiple-Model Predictive Control is carried out in two phases; the offline phase to calculate the difference between models, and the online phase to use a probabilistic approach to track the reference trajectory. The difference of the dynamical models is utilized in the online control method to compensate the accuracy of the approximate models in the Multiple-Horizon Multiple-Model Predictive Control method. Furthermore, the presented control framework has the model-updating characteristic that helps to modify the prediction according to past information. The numerical simulation results demonstrate the effectiveness of the proposed guidance and control approach in the reduction of the fuel consumption as well as the landing error in the presence of different sources of uncertainties and disturbances.

An offset-free MPC formulation for nonlinear systems using adaptive integral controller

This paper investigates a novel offset-free control scheme based on a multiple model predictive controller (MMPC) and an adaptive integral action controller for nonlinear processes. Firstly, the multiple model description captures the essence of the nonlinear process, while keeping the MPC optimization linear. Multiple models also enable the controller to deal with the uncertainty associated with changing setpoint. Then, a min–max approach is utilized to counter the effect of parametric uncertainty between the linear models and the nonlinear process. Finally, to deal with other uncertainties, such as input and output disturbances, an adaptive integral action controller is run in parallel to the MMPC. Thus creating a novel offset-free approach for nonlinear systems that is more easily tuned than observer-based MPC. Simulation results for a pH-controller, which acts as an example of a nonlinear process, are presented to demonstrate the usefulness of the technique compared to using an observer-based MPC.

Economic Multiple Model Predictive Control for HVAC Systems—A Case Study for a Food Manufacturer in Germany

The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible Renewable Energies levy (EEG levy) was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering the electricity price and weather forecasts as well as an internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system.

Multiple-horizon multiple-model predictive control of electromagnetic tethered satellite system

This study aims to investigate the control of the electromagnetic tethered satellite system using a Model Predictive Control (MPC) scheme. The electromagnetic tethered satellite system is actuated by electromagnetic coils to generate controlling forces. The dynamical model of the system is described in high and low levels of accuracy, which are used to design the control framework. Multiple-Horizon Multiple-Model Predictive Control approach is employed to drive the formation to the desired state. Not only does the presented control law satisfy input and output constraints but also has appropriate characteristics in the sense of optimality. The main benefit of using Multiple-Horizon Multiple-Model Predictive Control is having lower computational burden than the classical MPC. The numerical simulation results are presented and compared with sliding mode control to demonstrate the effectiveness of the proposed control method and its advantage over both classical MPC and sliding mode control methods. The obtained results show dramatic reductions in computational time and consumed energy compared to the classical MPC and sliding model control methods, respectively.

Modeling, Control, and Numerical Simulations of a Novel Binary-Controlled Variable Stiffness Actuator (BcVSA).

This research work aims at realizing a new compliant robotic actuator for safe human-robotic interaction. In this paper, we present the modeling, control, and numerical simulations of a novel Binary-Controlled Variable Stiffness Actuator (BcVSA) aiming to be used for the development of a novel compliant robotic manipulator. BcVSA is the proof of concept of the active revolute joint with the variable recruitment of series-parallel elastic elements. We briefly recall the basic design principle which is based on a stiffness varying mechanism consisting of a motor, three inline clutches, and three torsional springs with stiffness values (K0, 2K0, 4K0) connected to the load shaft and the motor shaft through two planetary sun gear trains with ratios (4:1, 4:1 respectively). We present the design concept, stiffness and dynamic modeling, and control of our BcVSA. We implemented three kinds of Multiple Model Predictive Control (MPC) to control our actuator. The main motivation of choosing this controller lies in the fact that working principle of multiple MPC and multiple states space representation (stiffness level) of our actuator share similar interests. In particular, we implemented Multiple MPC, Multiple Explicit MPC, and Approximated Multiple Explicit MPC. Numerical simulations are performed in order to evaluate their effectiveness for the future experiments on the prototype of our actuator. The simulation results showed that the Multiple MPC, and the Multiple Explicit MPC have similar results from the robustness point of view. On the other hand, the robustness performance of Approximated Multiple Explicit MPC is not good as compared to other controllers but it works in the offline framework while having the capability to compute the sub-optimal results. We also performed the comparison of MPC based controllers with the Computed Torque Control (CTC), and Linear Quadratic Regulator (LQR). In future, we are planning to test the presented approach on the hardware prototype of our actuator.

Wind Turbine Torque Oscillation Reduction Using Soft Switching Multiple Model Predictive Control Based on the Gap Metric and Kalman Filter Estimator

A new multiple model predictive control (MMPC) is reported to regulate the output power of the National Renewable Energy Laboratory (NREL) 1.5 MW baseline wind turbine (WT). The maximum power point tracking in the wide range of the partial load regime without losing any control performance is the goal of the proposed algorithm. To do this, the whole area of the partial load regime is divided into adequate linear models using a mathematical tool called gap metric. Next, we set different MPCs (controller bank) to each model. The controller selection is conducted based on wind speed value estimated using a neural network estimator. The sudden change between controllers applies a hazardous chattering to control signal. To tackle this problem and reduce the chattering, a new soft switching MMPC (SSMMPC) is introduced. Furthermore, the stability of the closed-loop system is guaranteed using Lyapunov theory creating novelty in this work. Moreover, to validate the proposed algorithm, SSMMPC and classical MMPC are implemented on the NREL 1.5 MW baseline WT using fatigue aerodynamic structure and turbulence simulator. Finally, the simulation results and comparisons demonstrate the effectiveness of the proposed controller in the tracking error, oscillation in control signal, and mechanical power.

Gap metric–based model bank construction for wind turbine predictive control

SummaryThis paper presents a novel model bank construction method for the multiple model predictive control of wind turbine system. The gap metric is used to measure the dynamic difference between the linearized models of the wind turbine system at different wind speed. Two algorithms are then proposed to divide the wind speed range in different operating regions. Meanwhile, a complete and nonredundant linear model bank is established to approximate the wind turbine system in the whole operating region. We take the robust model predictive control algorithm to design the local controller and utilize the wind speed as the switching criterion to combine the submodels. The simulation study on a 5‐MW wind turbine verifies the efficiency of the proposed method.

Multiple Model Predictive Hybrid Feedforward Control of Fuel Cell Power Generation System

Solid oxide fuel cell (SOFC) is widely considered as an alternative solution among the family of the sustainable distributed generation. Its load flexibility enables it adjusting the power output to meet the requirements from power grid balance. Although promising, its control is challenging when faced with load changes, during which the output voltage is required to be maintained as constant and fuel utilization rate kept within a safe range. Moreover, it makes the control even more intractable because of the multivariable coupling and strong nonlinearity within the wide-range operating conditions. To this end, this paper developed a multiple model predictive control strategy for reliable SOFC operation. The resistance load is regarded as a measurable disturbance, which is an input to the model predictive control as feedforward compensation. The coupling is accommodated by the receding horizon optimization. The nonlinearity is mitigated by the multiple linear models, the weighted sum of which serves as the final control execution. The merits of the proposed control structure are demonstrated by the simulation results.

Multiple Model Predictive Functional Control for Marine Diesel Engine

A novel control scheme based on multiple model predictive functional control (MMPFC) is proposed to solve the cumbersome and time-consuming parameters tuning of the speed controller for a marine diesel engine. It combines the MMPFC with traditional PID algorithm. In each local linearization, a first-order plus time delay (FOPTD) model is adopted to be the approximate submodel. To overcome the model mismatches under the load disturbance conditions, we introduce a method to estimate the open-loop gain of the speed control model, by which the predictive multimodels are modified online. Thus, the adaptation and robustness of the proposed controller can be improved. A cycle-detailed hybrid nonlinear engine model rather than a common used mean value engine model (MVEM) is developed to evaluate the control performance. In such model, the marine engine is treated as a whole system, and the discreteness in torque generation, the working imbalance among different cylinders, and the cycle delays are considered. As a result, more reliable and practical validation can be achieved. Finally, numerical simulation of both steady and dynamic performances of the proposed controller is carried out based on the aforementioned engine model. A conventional well-tuned PID with integral windup scheme is adopted to make a comparison. The results emphasize that the proposed controller is with stable and adaptive ability but without needing complex and tough parameters regulation. Moreover, it has excellent disturbance rejection ability by modifying the predictive multimodels online.

Joint structure for the real-time estimation and control of automotive dry clutch engagement

In this paper, real-time identification of the dry-clutch torque characteristics model in automotive systems is investigated. The proposed algorithm focused to provide the direct capability to update the clutch torque characteristics model by using engine speed and torque as measurable signals in real-time, within a multiple model predictive control loop. First, the clutch torque is estimated with some uncertainty. Then, it is proposed a new trust-region based identification approach which is robust enough to cover this uncertainty. The aim of the MPC controller is to ensure a comfortable lockup by avoiding engine stall and reasonable engagement time. The effectiveness and performance of the approach are demonstrated through the real-time model simulations in various conditions.

Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS) framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC) controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.

Soft landing on an irregular shape asteroid using Multiple-Horizon Multiple-Model Predictive Control

This study has introduced a predictive framework including a heuristic guidance law named Predictive Path Planning and Multiple-Horizon Multiple-Model Predictive Control as the control scheme for soft landing on an irregular-shaped asteroid. The dynamical model of spacecraft trajectory around an asteroid is introduced. The reference-landing trajectory is generated using Predictive Path Planning. Not only does the presented guidance law satisfy the collision avoidance constraint, but also guarantees the landing accuracy and vertical landing condition. Multiple-Horizon Multiple-Model Predictive Control is employed to make the spacecraft track the designed reference trajectory. The proposed control approach, which is a Model Predictive Control scheme, utilizes several prediction models instead of one. In this manner, it heritages the advantages of optimality and tackling external disturbances and model uncertainties from classical Model Predictive Control and at the same time has the advantage of lower computational burden than Model Predictive Control. Finally, numerical simulations are carried out to demonstrate the feasibility and effectiveness of the proposed control approach in achieving the desired conditions in presence of uncertainties and disturbances.

Switched Offline Multiple Model Predictive Control with Polyhedral Invariant Sets

This paper presents a switched offline multiple model predictive control procedure for nonlinear processes to ease the online computational burden and reduce the number of submodels. We employ the gap metric to characterize the dynamic difference between linear models and establish a linear model bank to approximate the nonlinear system. Based on the robust MPC algorithm, we develop an offline model predictive controller for each submodel. The polyhedral invariant set is utilized to expand the work scope of each local controller. In the offline part, a series of discrete states are selected, the corresponding feedback gains are precomputed, and associated polyhedral invariant sets are constructed. In the online implementation, the control input is simply calculated by calling the feedback gain according to the current state. A switching rule is then designed to integrate the submodels and guarantee the stability of the whole system. Finally, the corresponding simulation example is presented to validate th...

Individualized drug dosing using RBF-Galerkin method: Case of anemia management in chronic kidney disease

Background and ObjectiveAnemia is a common comorbidity in patients with chronic kidney disease (CKD) and is frequently associated with decreased physical component of quality of life, as well as adverse cardiovascular events. Current treatment methods for renal anemia are mostly population-based approaches treating individual patients with a one-size-fits-all model. However, FDA recommendations stipulate individualized anemia treatment with precise control of the hemoglobin concentration and minimal drug utilization. In accordance with these recommendations, this work presents an individualized drug dosing approach to anemia management by leveraging the theory of optimal control. MethodsA Multiple Receding Horizon Control (MRHC) approach based on the RBF-Galerkin optimization method is proposed for individualized anemia management in CKD patients. Recently developed by the authors, the RBF-Galerkin method uses the radial basis function approximation along with the Galerkin error projection to solve constrained optimal control problems numerically. The proposed approach is applied to generate optimal dosing recommendations for individual patients. ResultsPerformance of the proposed approach (MRHC) is compared in silico to that of a population-based anemia management protocol and an individualized multiple model predictive control method for two case scenarios: hemoglobin measurement with and without observational errors. In silico comparison indicates that hemoglobin concentration with MRHC method has less variation among the methods, especially in presence of measurement errors. In addition, the average achieved hemoglobin level from the MRHC is significantly closer to the target hemoglobin than that of the other two methods, according to the analysis of variance (ANOVA) statistical test. Furthermore, drug dosages recommended by the MRHC are more stable and accurate and reach the steady-state value notably faster than those generated by the other two methods. ConclusionsThe proposed method is highly efficient for the control of hemoglobin level, yet provides accurate dosage adjustments in the treatment of CKD anemia.

MAQ: A Multiple Model Predictive Congestion Control Scheme for Cognitive Radio Networks

In this paper, we investigate the problem of robust congestion control in infrastructure-based cognitive radio networks (CRN). We develop an active queue management algorithm, termed MAQ, which is based on multiple model predictive control. The goal is to stabilize the TCP queue at the base station under disturbances from the time-varying service capacity for secondary users. The proposed MAQ scheme is validated with extensive simulation studies under various types of background traffic and system/network configurations. It outperforms two benchmark schemes with considerable gains in all the scenarios considered.

Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours

BackgroundMixed immunotherapy and chemotherapy of tumours is one of the most efficient ways to improve cancer treatment strategies. However, it is important to ‘design’ an effective treatment programme which can optimize the ways of combining immunotherapy and chemotherapy to diminish their imminent side effects. Control engineering techniques could be used for this. MethodsThe method of multiple model predictive controller (MMPC) is applied to the modified Stepanova model to induce the best combination of drugs scheduling under a better health criteria profile. The proposed MMPC is a feedback scheme that can perform global optimization for both tumour volume and immune competent cell density by performing multiple constraints. ResultsAlthough current studies usually assume that immunotherapy has no side effect, this paper presents a new method of mixed drug administration by employing MMPC, which implements several constraints for chemotherapy and immunotherapy by considering both drug toxicity and autoimmune. With designed controller we need maximum 57% and 28% of full dosage of drugs for chemotherapy and immunotherapy in some instances, respectively. Therefore, through the proposed controller less dosage of drugs are needed, which contribute to suitable results with a perceptible reduction in medicine side effects. ConclusionIt is observed that in the presence of MMPC, the amount of required drugs is minimized, while the tumour volume is reduced. The efficiency of the presented method has been illustrated through simulations, as the system from an initial condition in the malignant region of the state space (macroscopic tumour volume) transfers into the benign region (microscopic tumour volume) in which the immune system can control tumour growth.

Drivability Improvement Control for Vehicle Start-Up Applied to an Automated Manual Transmission

Drivability is the key factor for the automated manual transmission. It includes fast response to the driver’s demand and the driving comfort. This paper deals with a control methodology applied to an automated manual transmission vehicle for drivability enhancement during vehicle start-up phase. Based on a piecewise model of powertrain, a multiple-model predictive controller (mMPC) is designed with the engine speed, clutch disc speed, and wheel speed as the measurable input variables and the engine torque reference and clutch friction torque reference as the controller’s output variables. The model not only includes the clutch dynamic, the flexible shaft dynamic, but also includes the actuators’ delay character. Considering the driver’s intention, a slipping speed trajectory is generated based on the acceleration pedal dynamically. The designed control strategy is verified on a complete powertrain and longitudinal vehicle dynamic model with different driver’s torque demands.

Model predictive control under timing constraints induced by controller area networks

When multiple model predictive controllers are implemented on a shared controller area network (CAN), their performance may degrade due to the variable timing and delays among messages. The priority based real-time scheduling of messages on the CAN introduces complex timing of events, especially when the types and number of messages change at runtime. This paper introduces a novel hybrid timing model to make runtime predictions on the timing of the messages for a finite time window. Controllers can be designed using the optimization algorithms for model predictive control by considering the timing as optimization constraints. This timing model allows multiple controllers to share a CAN without significant degradation in the controller performance. The timing model also provides a convenient way to check the schedulability of messages on the CAN at runtime. Simulation results demonstrate that the timing model is accurate and computationally efficient to meet the needs of real-time implementation. Simulation results also demonstrate that model predictive controllers designed when considering the timing constraints have superior performance than the controllers designed without considering the timing constraints.

Adaptive Multiple MPC for a Wind Farm with DFIG: a Decentralized-Coordinated Approach

For low investment and flexible control, doubly fed induction generator (DFIG) is becoming the dominant type that is been used in the wind farms (WFs) today. The report researches about the rotor-side controller of DFIG where all design is based on the single machine infinite bus (SMIB) model. The interactions between the different generators have not been considered in the SMIB model, and the desired performance cannot be obtained by using the controller based on this model. In this situation, an adaptive decentralized-coordinated multiple model predictive control (ADM-MPC) is proposed. First, the interaction measurement method is developed to obtain the interaction measurement model of DFIG, where the interactions between the different generators have been considered. Next, an adaptive multiple MPC based on the obtained interaction measurement method of DFIG is employed to control the rotor-side converter of DFIG. In order to cope with the stochastic disturbance of wind turbine, the augment state structure is employed to improve the tracking control performance. An artificial neural network (ANN) trained online is employed as a weighting controller to cope with the nonlinearities and large operating range of DFIG. A simple, generic renewable power system (RPS) is used to demonstrate contributions. The results of both dominant eigenvalue analysis and time response simulations are represented to illustrate contributions to system damping and transient stability that the DFIG based WF can make with the proposed ADM-MPC controller.