Multi-model Control Research Articles

Finite-Time Convergence Guidance Law for Hypersonic Morphing Vehicle

Aiming at the interception constraint posed by defensive aircrafts against hypersonic morphing vehicles (HMVs) during the terminal guidance phase, this paper designed a guidance law with the finite-time convergence theory and control allocation methods based on the event-triggered theory, achieving evasion of the defensive aircraft and targeting objectives for a morphing vehicle in the terminal guidance phase. Firstly, this paper established the aircraft motion model; the relative motion relationships between HMV, defensive aircraft, and target; and the control equations for the guidance system. Secondly, a guidance law with finite-time convergence was designed, establishing a controller with the angle between the aircraft–target–defense aircraft triplet as the state variable and lift as the control variable. By ensuring the angle was non-zero, the aircraft maintained a certain relative distance from the defense aircraft, achieving evasion of interception. The delay characteristic of the aircraft’s flight controller was considered, analyzing its delay stability and applying control compensation. Thirdly, a multi-model switching control allocation method based on an event-triggered mechanism was designed. Optimal attack and bank angles were determined based on acceleration control variables, considering different sweep angles. Finally, simulations were conducted to validate the effectiveness and robustness of the designed guidance laws.

Adaptive Dynamic Model-Based Path Following Controller Design for an Unmanned Surface Vessel

Abstract The effective design of a path following controller for Unmanned Surface Vessels (USVs) under uncertain influences induced by various factors such as environmental disturbances is a challenging task. In this study, we propose to ful?lling this task through taking benefits from an online parameter identi?cation technique, the discrete-time sliding mode control (DSMC) method, and the improved line of sight (LOS) algorithm. The Particle Swarm Optimization algorithm (PSO) was adopted to provide initial settings for the straightforward online identification method, i.e., the Forgetting Factor Recursive Least Square method (FFRLS). In order to handle the time-varying sideslip angle of a ship that exists in reality due to environmental disturbances, a multi-model course control scheme is proposed to improve the control performance. For this control scheme, a flexible selection mechanism in-between a heading angle or a course angle tracking controller based on the DSMC method is designed. A solution to ?xing the tracking deviation problem of the LOS guidance law is investigated for which the gradient descent method is introduced. A series of experiments are carried out at sea with a USV called Orca to verify and validate the proposed hybrid path following approach. The results showed that tracking errors mainly induced by environmental disturbances existed but the maximum magnitude among them was small enough and remaining within the acceptable range especially from the marine engineering point of view. These results, to a high degree, validated the robustness and precision of the proposed controller.

Systematic solution for multi-model control approaches in nonlinear dynamic systems based on the s-gap metric

This paper proposes a systematic approach for optimizing the distribution of local models in multi-model control systems (MMCS) to enhance overall robustness. While existing literature discusses this method for linear parameter varying (LPV) and uncertain linear time-invariant (LTI) systems, significant limitations persist in addressing nonlinear dynamic systems. Robust control tools like the gap metric and generalized stability margin (GSM) have limited effectiveness in analyzing the robustness of nonlinear feedback systems. To address these challenges, novel concepts of the gap metric and GSM are introduced to determine central operating points (COPs) within local operating areas (LOAs) across the total operating area (TOA). These COPs guide the extraction of affine disturbance local models (ADLMs). Additionally, an optimization problem based on the s-gap metric and GSM is presented to optimize COPs placement and LOAs boundaries. Challenges such as non-monotonic behavior of the cost function and complexity arising from the s-gap metric formulation necessitate novel solution methods. To address these, constraints are applied to the cost function, and a novel discrete optimization approach is introduced. Finally, theoretical findings are applied to the Duffing system, pH neutralization process, and continuous stirred tank reactor (CSTR) plant to evaluate the proposed method's effectiveness. This comprehensive validation across different systems underscores the versatility and practical utility of the proposed approach.

Supervisory multi-model control for supply chains with large uncertainty on the perishability rate

Defining an efficient inventory management of Perishable Supply Chains (PSCs) becomes a very difficult problem when the ”a priori” information on the perishability rate is affected by a large degree of uncertainty. In this paper we face this problem using a multi-model based supervisor and a reconfigurable min-max Robust Model Predictive Control (RMPC). The twofold task of the supervisor is: 1) identify the most likely uncertainty scenario, 2) drive towards it the current min-max MPC law.

Systematic model bank determination approach for nonlinear systems using gap metric and stability margin

This paper introduces a novel systematic approach for designing multimodel controller tailored to nonlinear systems. Our methodology focuses on the precise selection of local controllers, aligning their performance with specific requirements while minimizing redundancy. We achieve this through a two-fold process: first, the creation of an initial multimodel bank using the Self-Organizing Map (SOM) algorithm. Then, we employ gap metrics to assess the similarity between linear subsystems and hierarchical agglomerative clustering to group local models. This process leads to the identification of model sets for aggregation. Finally, we use stability margins as a guidance to get the reduced bank to elaborate the multimodel controller, ensuring robust stability. The main advantages of our method lie in the elimination of redundancy, simplification of the controller structure and the guarantee of robust stability. Three highly nonlinear processes are studied to demonstrate the efficiency of the proposed multimodel control approach.

Robust Solution of the Multi-Model Singular Linear-Quadratic Optimal Control Problem: Regularization Approach

We consider a finite horizon multi-model linear-quadratic optimal control problem. For this problem, we treat the case where the problem’s functional does not contain a control function. The latter means that the problem under consideration is a singular optimal control problem. To solve this problem, we associate it with a new optimal control problem for the same multi-model system. The functional in this new problem is the sum of the original functional and an integral of the square of the Euclidean norm of the vector-valued control with a small positive weighting coefficient. Thus, the new problem is regular. Moreover, it is a multi-model cheap control problem. Using the solvability conditions (Robust Maximum Principle), the solution of this cheap control problem is reduced to the solution of the following three problems: (i) a terminal-value problem for an extended matrix Riccati type differential equation; (ii) an initial-value problem for an extended vector linear differential equation; (iii) a nonlinear optimization (mathematical programming) problem. We analyze an asymptotic behavior of these problems. Using this asymptotic analysis, we design the minimizing sequence of state-feedback controls for the original multi-model singular optimal control problem, and obtain the infimum of the functional of this problem. We illustrate the theoretical results with an academic example.

Fused multi-model predictive control with adaptive compensation for proton exchange membrane fuel cell air supply system

Regulating the air supply is crucial for high efficiency and reliable operation of proton exchange membrane fuel cell systems (PEMFCs). In this study, a fused multi-model predictive control (FM-MPC) with an adaptive compensation is proposed for the oxygen excess ratio (OER) regulation of the air supply system. The FM-MPC is designed based on the linearized PEMFC model at low and high power phases, with two linear MPCs implemented and combined using adaptive featured weights. An adaptive compensation strategy is created to address the imbalance of the two MPCs and external load disturbances. The stability of the proposed control is analyzed using Lyapunov's second law. Simulation results demonstrate that the proposed method exhibits less overshoot and faster response than conventional MPCs, with the OER total sum-of-squares error (TSSE) reduced by 59.4% and 87.7% for New European Driving Cycle (NEDC) and Urban Dynamometer Driving Schedule (UDDS) conditions, respectively. Finally, a Hardware-In-the-Loop (HIL) experiment verifies the real-time application potential of the proposed controller, with a mean relative error (MRE) of 1.12% between experiment and simulation.

Data-Driven Multimodel Predictive Control for Multirate Sampled-Data Nonlinear Systems

This paper is concerned with the asynchronous control problem of multi-rate sampled-data nonlinear systems. To solve this problem, the data-driven multi-model predictive control (DMMPC) strategy is proposed. First, a multi-model predictive control structure is designed such that each state variable of the multi-rate sampled-data nonlinear system can be controlled synchronously at all sampling instants. In this structure, the fuzzy neural network (FNN) is introduced to build the multi-model. Then, the prediction outputs of each state variable at all sampling instants are obtained to provide control information for the controller. Specially, the objective function with adaptive weight matrix (AWM) is designed to reduce the influence of the prediction error caused by nonlinear fitting on control performance. Then, the optimal control laws are calculated to improve the control precision. Finally, the convergence and stability of DMMPC are proved in detail. The numerical example and industrial application reveal that the proposed DMMPC can obtain considerable control performance for the multi-rate sampled-data nonlinear systems. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The asynchronous control problem of the multi-rate sampled-data nonlinear system (MRSNS) may degrade the operation performance of closed-loop system. In this paper, a data-driven multi-model predictive control (DMMPC) strategy is designed for MRSNS without mechanism model. The strategy mainly consists of three parts: First, a multi-model prediction structure based on fuzzy neural network (FNN) is established to obtain the prediction output of all variables at each sampling point. The parameters of FNNs are corrected at each sampling point to ensure prediction accuracy. Second, an objective function with an adaptive weight matrix (AWM) is designed to compute the control law, in which AWM is used to reduce the influence of prediction error on control performance. Third, the effectiveness of DMMPC is verified by a numerical example and an industrial application of the wastewater treatment process. The experimental results show that DMMPC can achieve satisfied operation performance in control accuracy.

105-LB: Safety and Performance of an Advanced Hybrid Closed-Loop (AHCL) Algorithm with Ultra-rapid Lispro (URLi) and Lispro

Tuning a multi-model predictive control AHCL algorithm to the PK/PD properties of URLi may result in improved performance vs Lispro with no increased safety concerns. This was a randomized, double-blind, inpatient, 2-period crossover study in 22 adults with T1D on insulin pumps. Each URLi and Lispro treatment period had a 4-day inpatient visit with insulin doses calculated using the tuned AHCL algorithm. To challenge the algorithm, tests included unannounced exercise, 1 hr delayed bolus, underbolus, and overbolus. Participants (59% male) had mean ±SD age 43.4±13.77 years, T1D duration 25.9±9.61 yrs, and HbA1c 7.6±0.47%. Mean time in range (TIR; 70-180 mg/dL) during the overall inpatient period: URLi 79.4%; Lispro 78.7 % (p=0.80) with a trend towards decreased time &amp;lt;70 mg/dl (1.5 vs 2.4, p=0.08) for URLi. URLi had a significant difference in Level 1 hypoglycemia (30 vs 55 events, p = 0.01) but not Level 2 (4 vs 9 events, p=0.3). TIRs were similar including during meal/challenge periods (Table) with a trend towards greater TIR during the postprandial period and reduced time &amp;lt;70 mg/dl during exercise for URLi. There was greater TIR during overbolus with Lispro. No severe hypoglycemia events occurred. Results suggest reduced hypoglycemia with URLi vs Lispro with comparable glycemic control. The AHCL algorithm showed promising glycemic performance in a supervised setting when used with URLi or Lispro. Disclosure R. Pollom: None. E. Dassau: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. M. Katz: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. L. Nosek: None. H. Coester: None. E. Zijlstra: Other Relationship; Eli Lilly and Company, Speaker's Bureau; Gan &amp; Lee Pharmaceuticals, Novo Nordisk A/S. A. Ghosh: None. A. Haidar: Consultant; Eli Lilly and Company, Other Relationship; Bigfoot Biomedical, Inc., Research Support; Adocia, Dexcom, Inc., Tandem Diabetes Care, Inc. R. Jones: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. J. Leohr: None. S. E. Gleissner: None.

Flexible operation of the pressurized water reactor nuclear power system using multi-model predictive control over a wide nonlinear operating range

The nonlinearity problem caused by the large load variation is a significant challenge for the pressurized water reactor nuclear power system (PWRNPS) to realize its flexible operation over a wide operating range when it participates in peak regulation in the power grid. Thus, this work proposes a novel multi-model predictive control (MMPC) strategy for PWRNPS to address its nonlinearity problem in multivariable control within a wide range of conditions, in which the coupling effect between the average coolant temperature and the steam turbine power is considered. Given the insufficient understanding of the nonlinearity of PWRNPS in previous work, an in-depth investigation in this regard is performed to support the control design, in which the nonlinearity of PWRNPS at the system level is analyzed both on quantitative and qualitative aspects for the first time. Considering the captured uneven nonlinearity distribution of PWRNPS, a new approach integrating hierarchical clustering with gap metric is proposed to guide the decomposition of the operating space of PWRNPS systematically, based on which MMPC is designed to realize the nonlinearity control of PWRNPS under the large load variation. Three types of simulations demonstrate the effectiveness of the proposed methods.

Discrete-time multi-model preview control: Application to a real semi-active automotive suspension system

Road roughness is one of reasons of vehicle vibration. Passengers feel uncomfortable or goods are damaged with the serious vibration caused by the irregular road surface. This paper focus on the road adaptation for vehicle semi-active suspension system using preview robust control approach. This approach aims to improve the performance of control systems by using future measurements or predictions of road disturbances. The objectives are to minimize a quadratic criterion on the system output and at the same time to achieve a good magnitude of the control signal. The proposed solution combines a robust feedback controller together with a preview feedforward filter. This new method is developed to synthesize the preview feedforward filter in order to limit the control force demand of an electro-rheological (ER) damper in an automotive suspension system, and is implemented on the INOVE testbench from GIPSA-lab (1/5-scaled real vehicle) for real-time performance assessment. Both simulation and experimental results demonstrate the effectiveness of the proposed controller to determinate the damper force in real-time face to measurement noises and road disturbances.

Formula omitted]-gap metric based multi-model predictive control of an ocean current turbine system with blade pitch failures

In this article, a method of combining multiple linear models is developed and used by an advanced model predictive control algorithm for ocean current turbine flight control. The developed model bank consists of faulty and healthy linear models created for the ocean current turbine system. Each linear model is used to compute individual control actions using the model predictive control framework, which are combined to generate the aggregate control action that is applied to the system. A weighted average defined by the ν-gap distance between the current linear approximation of system dynamics and each individual linear system is used to compute the aggregate control action. The multi-model predictive control strategy is applied to control the ocean current turbine to mitigate performance degradation that may occur due to failures in the system. The proposed control framework’s effectiveness is shown with the renewable power generation improvement during a faulty case.

Multi-model coupling-based dynamic control system of ladle slag in argon blowing refining process

Multi-model coupling-based dynamic control system of ladle slag in argon blowing refining process

Damping of Low-Frequency Oscillations in a Power System, Based on Multiple-Model Optimal Control Strategy

This paper proposes a new control strategy based on multimodel linear optimization control (MMLOC) to damp power system low-frequency oscillations (LFO) faster. The multimodel control strategy is used to anticipate synchronous generator dynamic behavior and linear optimization control for generating a control signal uncomplicatedly. The third-order model of synchronous machine is considered for each of the models. The probability of each model’s validity is evaluated from the difference between model outputs and the same actual power system outputs via the Bayesian approach. To improve the postfault dynamics of the case study power system, which contains a static VAR compensator, a linear optimal controller (LOC) is designed. The 8th order model of the machines is used in simulations to represent the plant as accurately as possible.

Model Predictive Control With Disturbance Observer for Marine Diesel Engine Speed Control

The application of model predictive control (MPC) algorithm in the fixed phase control of marine diesel engine speed is studied under the premise of considering model mismatch and external disturbance. Firstly, the steady-state error problem of conventional MPC controller is solved by changing nonlinear model to incremental form. Furthermore, discrete disturbance observer (DO) is introduced in the feedback correction, which can filter out the high-frequency disturbance and reduce the requirement of algorithm on the accuracy of model. Then, considering that nonlinear MPC based on DO (DONMPC) requires a large amount of online computation, the algorithm is simplified by preliminarily converting the nonlinear model to linear model. Through analysis, the controller performance of the two models is similar. Furthermore, considering that the speed of marine diesel engine is usually set to a few fixed reference values, a linear multi-model predictive controller based on DO (DOLMMPC) with less online calculation is proposed. Finally, the designed controllers are verified by experiments. The software simulations of the designed controllers and the PID controller are carried out on the cylinder-by-cylinder mean value engine model (MVEM). It is proved that the algorithm simplification method retains the control performance of the DONMPC algorithm, and the control performance of the designed two controllers is better than the PID controller. Moreover, the DOLMMPC controller and PID controller are tested on the semi-physical simulation platform. The results demonstrate that the DOLMMPC controller can meet the computational power limit of the microprocessor in practical engineering.

Study on an adaptive multi-model predictive controller for the thermal management of a SOFC-GT hybrid system

A SOFC temperature control system based on adaptive multimodel predictive control (MMPC) method is designed for a solid oxide fuel cell-gas turbine (SOFC-GT) hybrid system with anode and cathode ejectors. Two multi-input and multi-output MPCs (under 100% and 90% load) are designed to control the anode and cathode inlet temperatures. The accuracy of the identified linear models are both more than 95%. The control performance of the designed MMPC is compared with a single MPC and traditional PI. The comparison results demonstrate that the proposed MMPC is most effective and competitive in SOFC thermal management. During the load following, the controller overshoot is less than 1.19K. The settling time is about 2000s, and the integral of time-weighted absolute error is less than 472.

Multi-model adaptive PID control of variable speed pump-controlled hydraulic cylinder system

Multi-model adaptive PID control of variable speed pump-controlled hydraulic cylinder system

A multi-model multi-objective robust damping control of GCSC for hybrid power system with offshore/onshore wind farm

In the recent decade, the power network has experienced a remarkable energy transition due to the large-scale integration of wind energy resources, especially converter-interfaced modern wind turbines. The increasing/decreasing wind penetration, on the other hand, unexpectedly affects the low frequency oscillations of the modern power grid. In this context, the proposed work adopts a multi-model multi-objective robust control framework to design a supplementary damping controller for the gate-controlled series capacitor (GCSC) to stabilize the hybrid power system with a permanent magnet synchronous generator (PMSG)-based off-shore wind farm (OSWF) and a doubly-fed induction generator (DFIG)-based onshore wind farm (ONWF). A bilinear matrix inequality (BMI) optimization problem, formulated as multi-objective synthesis considering H2/H∞ performance along with pre-defined pole placement, is presented for the GCSC control design, which is solved by a two-step linear matrix inequality (LMI) approach. In addition, all LMI constraints are constructed based on the multi-model control framework to incorporate multiple operating conditions. Afterward, the significant improvement in the damping characteristics of the closed-loop system, covering a wide operating range, is confirmed using eigenvalue analysis. The effectiveness of the scheme is validated using two case studies based on the hybrid power system, subject to various disturbances and uncertainties. The simulation results show the robustness and higher damping performance of the proposed multi-model strategy, compared to a conventional and a robust damping controller, for mitigating power system oscillations alongside voltage fluctuations of the wind farms.

Multi model robust control design for a floating offshore variable speed wind turbine with tension leg platform

This paper presents a multi-model robust control (MMRC) design for an offshore variable speed wind turbine with tension leg platform. The proposed control scheme covers the model uncertainty in the above rated wind speed, and it provides a reliable control for power regulation while minimizing the mechanical loads on the wind turbine structure. For this purpose, the above rated wind speed region is divided into several wind speed groups, and a set of linearized models are obtained from the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) simulator for various mean wind speeds of each group. Using Weibull wind speed distribution, a nominal model with additive uncertainty is generated for each wind speed group, to be used in the multi-model robust control design. The MATLAB/Simulink software environment is used to verify the performance of the proposed control strategy using the National Research Energy Laboratory (NREL) three-bladed horizontal axis 5-MW baseline wind turbine. The performance of the proposed multi-model robust controller is compared with the state-of-the-art gain-scheduling PI (GSPI) controller. The results show improvement in power regulation and platform movement minimization while reducing the mechanical loads of the wind turbine. By using the proposed control scheme, the severe oscillation in the blade pitch actuation and power output, which is due to the changes in the operating point of the wind turbine, decreases drastically.

Handling model uncertainty in control of a pressure swing adsorption unit for syngas purification: A multi-model zone control scheme-based robust model predictive control strategy

The application of a robustly stabilizing model predictive control (MPC) strategy to a pressure swing adsorption (PSA) unit, hitherto unexplored in the literature, is addressed in this work. Here, a set of linear models to handle model uncertainties describes the PSA nonlinear dynamic behaviour. Each model represents a different operating condition of the PSA, generating a multi-model uncertainty description. In addition to incorporating the multi-model uncertainty, the proposed MPC control law deals with a controlled output zone tracking scheme to systematically accommodate typical cyclic steady states of a PSA process. From the industrial standpoint, economic targets on process variables (controlled outputs, manipulated inputs, or both) are also incorporated into the robust control method. A Real-Time Optimizer usually defines these targets, making up a two-layer control scheme. The effectiveness of the robust MPC is evaluated in a syngas purification process via a single bed, six-step PSA unit composed of a porous amino-functionalized titanium terephthalate MIL-125-type MOF adsorbent.