Switching Model Predictive Control Research Articles

Asynchronous time dependent switched model predictive control for nonlinear systems considering feasibility constraints

AbstractThis paper presents a model predictive control for nonlinear asynchronous switched systems, aiming to achieve H∞ performance and addressing feasibility issues. The method proposed guarantees the recursive feasibility of each sub‐system after switching moments in the presence of exogenous disturbances and unknown matched and mismatched time intervals due to asynchronous switching. The boundaries of the feasibility region for each sub‐system are determined in order to impose constraints on decision parameters and switching signals. These constraints aim to maximize the mentioned region while ensuring stability. Additionally, by identifying the common region of feasibility among different sub‐systems, conditions are established for initializing the sub‐systems at the switching moments to ensure that the system states remain within this feasibility region even under the worst switching scenario. Therefore, by ensuring the initial feasibility at the switching moments and demonstrating recursive feasibility, the overall feasibility of the developed MPC is guaranteed at all times. The benefits of the proposed approach are thoroughly investigated through the simulation of a chemical system. The results demonstrate the effectiveness of the proposed method.

A Novel Fuzzy Logic Switched MPC for Efficient Path Tracking of Articulated Steering Vehicles

This paper introduces a novel fuzzy logic switched model predictive control (MPC) algorithm for articulated steering vehicles, addressing significant path tracking challenges due to varying road conditions and vehicle speeds. Traditional single-model and parameter-based controllers struggle with tracking errors and computational inefficiencies under diverse operational conditions. Therefore, a kinematics-based MPC algorithm is first developed, showing strong real-time performance but encountering accuracy issues on low-adhesion surfaces and at high speeds. Then, a 4-DOF dynamics-based MPC algorithm is designed to enhance tracking accuracy and control stability. The proposed solution is a switched MPC strategy, integrating a fuzzy control system that dynamically switches between kinematics-based and dynamics-based MPC algorithms based on error, solution time, and heading angle indicators. Subsequently, simulation tests are conducted using SIMULINK and ADAMS to verify the performance of the proposed algorithm. The results confirm that this fuzzy-based MPC algorithm can effectively mitigate the drawbacks of single-model approaches, ensuring precise, stable, and efficient path tracking across diverse adhesion road conditions.

Operational optimisation of a microgrid using non-stationary hybrid switched model predictive control with virtual storage-based demand management

Demand-shaping mechanisms are a key component of modern energy management systems, although not unproblematic. The degree of social acceptance of interference with demand or generation and the ease of integration of various types of non-critical loads depends on the method of their implementation. In addition, the critical load pool typically includes devices with different response times. The energy management systems currently in use often cannot meet user expectations. Especially when considering other vital aspects, such as energy market spread, storage wear, or connection to the utility grid and neighbouring microgrids. The authors adopted an approach of unifying demand side management and response in the form of virtual energy storage. Said store allows for the accommodation of loads operating under differing scheduling horizons. Such a new concept allows management not only in terms of quantity but also in terms of time. The storage is the focal point of a comprehensive energy management system based on switched model predictive control. The receding horizon algorithm relies on a non-stationary hybrid microgrid model. The study compares the operating costs of microgrids with virtual storage, allowing only demand postponement, preponement or bidirectional operation. The energy management system is also examined for sensitivity to changes in the weight matrices of the cost function, horizon length and forecast inaccuracy. Introducing virtual energy storage reduces microgrid operating costs by up to 16%. The decrease in control performance is proportional to the prediction accuracy, and the sensitivity allows for customisation.

Switched Model Predictive Control for Nonholonomic Mobile Robots Under Adaptive Dwell Time.

In this article, switched model predictive control (MPC) is proposed for nonholonomic mobile robots with adaptive dwell time and a dual-terminal set. The dual-terminal set is used to reduce on-line complexity of the switched MPC for the nonholonomic mobile robots with multiple constraints. By a switched signal with the adaptive dwell time, cost functions are switched to improve control performance under multiple constraints. The switched MPC with feasibility and stability can adjust a tradeoff between control performance and computational complexity for the closed-loop system. Simulation results are given to illustrate superiority of the switched MPC for nonholonomic mobile robots.

Delay-dependent recursive feasibility based switched model predictive control for nonlinear systems

This paper presents a coordinated design of a model predictive controller (MPC) and switching law for delayed nonlinear switched systems with Lipschitz property. The article derives delay-dependent recursive feasibility constraints to handle disturbances in a polytopic form and incorporates them into the optimization problem. The control gain at each step is determined to ensure the feasibility of the optimization problem during the execution time of each sub-system. Additionally, constraints related to the cost function and H∞ performance conditions are introduced in the developed linear matrix inequality problem to minimize the cost function with an infinite predictive horizon and guarantee controller robustness against unknown constant delays. The coordinated design of the MPC and switching law is achieved using the multiple Lyapunov–Krasovskii functional, which reduces the strictness of the controller constraints compared to the switched Lyapunov–Krasovskii functional. However, it imposes a dwell-time limitation on the switching law. By adopting the PDT structure, the dwell-time limitation is reduced compared to common structures. To evaluate the proposed design, it is applied to a water pollution system, and its performance is assessed. The results demonstrate superior performance compared to previous works.

A hybrid-MPC based energy management system with time series constraints for a bioclimatic building

Microgrids play a crucial role in transitioning from the current energy grid model to one that seamlessly integrates renewable energy sources. However, the variability and unpredictability inherent in renewable energy pose new challenges in controlling energy flows within the microgrid. To ensure smooth operation in isolated mode or to minimize reliance on polluting energy sources, microgrids need to incorporate energy buffers and accurately predict renewable energy production, considering its intermittent nature. These factors significantly influence the development of effective microgrid controllers. This work presents a hybrid model-based predictive control to manage the energy flows in a microgrid. The proposed approach addresses both the discrete and continuous dynamics of the system through an optimized solution, incorporating, as main contribution, temporary constraints, which allows for integration of electric vehicle operating modes. The controller is tested in simulation with real data from a microgrid located at the CIESOL centre in Almería (Spain). Moreover, a comparison with a rule-based controller and a switching model predictive control is provided. Results demonstrate that the proposed approach effectively decreases the economic costs of microgrid management, outperforming the rule-based controller and the switching model predictive controller by 8.23% and 1.94%, respectively, over a thirty-day scenario.

Switched Auto-Regressive Neural Control (S-ANC) for Energy Management of Hybrid Microgrids

Switched model predictive control (S-MPC) and recurrent neural networks with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for the energy management of microgrids (MGs). These methods ease constraint satisfaction, computational demands, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method dynamically selects optimal models and control strategies based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) control with these powerful control methods improves the prediction accuracy and the adaptability of the system conditions. This paper compares the two control approaches and proposes a novel algorithm called switched auto-regressive neural control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC, while enabling continuous learning. The results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. In addition, this study evaluated our predictive model’s accuracy: (i) the R-squared error was 0.951, indicating a strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions, with minimal deviations from the actual values.

Driveline oscillation attenuation through Clutch Micro-Slip and Model Predictive Control

This paper considers a Model Predictive Control (MPC) approach to micro-slip clutch control for driveline shuffle attenuation and longitudinal acceleration oscillation reduction in vehicles with an Automated Manual Transmission system. We introduce an original MPC formulation to consider the performance tradeoff between the clutch slip regulation, the torsional oscillation attenuation and the jerking reduction. A particular problem to be considered is handling saturation constraints on the transmitted torque. We employ a piecewise-affine linear model of the actuator-driveline system to take care of special manoeuvres that cause inversions in the transmission motion. On the methodological side, we present a switching MPC approach where the underlying optimization problem is a quadratic programme for a fast online controller implementation on real-world transmission control unit platforms. To show the effectiveness of the proposed approach, we present extensive simulation results and experimental tests performed on a prototype vehicle.

A switching MPC-based power management strategy for DC microgrids with SOC consistency and energy loss

The DC microgrids equipped with photovoltaic and energy storage systems have increasingly been applied to unmanned vehicles, and energy management is attracted much more attention. In this work, a power management strategy based on switching model predictive control is proposed to tackle energy loss and inconsistency of energy storage systems. The dynamic characteristic of the battery are fully addressed, and the non-affine equation constraints are dealt with by the iterative approximation method. The proposed strategy balances energy loss and state-of-charge consistency of the energy storage system and can operate when future environmental information is unavailable. Finally, comparative examples with existing schemes verify the effectiveness of the proposed strategy.

A novel engine and battery coupled thermal management strategy for connected HEVs based on switched model predictive control under low temperature

Under a low-temperature environment, electric vehicles face serious environmental adaptability problems, and efficient vehicle thermal management strategies are urgently needed. This paper presents a novel engine–battery coupled thermal management strategy for connected hybrid electric vehicles (HEVs). An improved system structure for an engine–battery coupled thermal management system (engine–battery CTMS) is designed to avoid unnecessary heat loss. The control requirements of the engine–battery CTMS include minimum engine fuel consumption, minimum power battery aging damage and minimum system energy consumption, which constitutes a multi-objective optimal control problem in a finite time domain. Based on model predictive control (MPC) theory, a switched nonlinear MPC (NMPC) control strategy is proposed to solve the optimal control problem of the complex coupled multi-input multi-output system. To verify the effectiveness of the proposed strategy, three comparative experiments of the centralized NMPC-based and rule-based methods combined with the improved system structure and the unimproved system structure are designed. The results of the cosimulation experiment between MATLAB/Simulink and AMEsim under various driving cycles and different ambient temperatures show that the improved structure and switched control strategy confer great advantages in reducing the controller computation burden, engine fuel consumption, and power battery aging damage.

Coordinated control of concentrated solar power systems with indirect molten salt storage considering operation mode switching: Using switching model predictive control

Coordinated control of concentrated solar power systems with indirect molten salt storage considering operation mode switching: Using switching model predictive control

Nonlinear switched model predictive control with multiple Lyapunov functions for trajectory tracking and obstacle avoidance of nonholonomic systems

AbstractIn this paper, we investigate trajectory tracking and obstacle avoidance for a nonholonomic system subject to external disturbances by a nonlinear switched model predictive control (MPC) strategy. In the nonlinear switched MPC strategy, a potential field is introduced in a cost function to guarantee a smooth path for the nonholonomic system. A switched mechanism is designed to ensure switching stability by setting multiple Lyapunov functions in different areas. Different from traditional switched mechanisms, an average dwell time closely related to stability conditions is proposed to balance safety and stability in the whole process. Recursive feasibility is presented for the nonlinear switched MPC strategy in trajectory tracking and obstacle avoidance. Simulation results are provided to show effectiveness and superiority of the nonlinear switched MPC strategy by a two‐wheeled mobile vehicle.

Energy Management of Grid-Connected Microgrids Using an Optimal Systems Approach

Microgrids (MGs) are a growing energy industry segment and represent a paradigm shift from remote central power plants to more localized distributed generation. Controlling MGs represents a challenge mainly due to their complexity and the different properties each asset in the MG has. Various methods have been proposed to address this challenging problem of MG control. Some of these methods are considered the optimal operation of MG assets. Other works are based on a systems approach and address the scalability and simplicity of synthesizing a MG’s energy management system (EMS). <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variables based logical control strategies, which are practical methods to model control strategies in MGs, can make the control structure more scalable. However, this method is not optimal. On the other hand, Switched Model Predictive Control (S-MPC) is an advanced method utilized to control power systems while satisfying several constraints to achieve an optimal solution based on various criteria. Nevertheless, its implementation is not straightforward. Therefore, to overcome these existing problems, this paper proposes a novel systems approach method called an extended optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variable method developed by combining the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variable based control method with the S-MPC method. This unique method has demonstrated a significant improvement in optimizing an MG’s energy management and enhanced the adaptation and scalability of a control structure of the MG. Our results show that the proposed extended optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variable method: (i) reduces the operational cost of MG by nearly 35%; (ii) reduces the usage of the battery energy storage system by 42%, and (iii) enhances the practicality of photovoltaic (PV) usage by 28%. Our novel extended optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variable technique also increases the adaptation and scalability of the control structure of the MG significantly by translating the results of S-MPC to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\varepsilon $ </tex-math></inline-formula> -variable method.

Design of a Distributed Switching Model Predictive Control for Quadrotor UAVs Aggregation

Design of a Distributed Switching Model Predictive Control for Quadrotor UAVs Aggregation

Switching Model Predictive Control of canals characterized by large operating conditions

This paper presents a switched linear system representation of water canal dynamics to incorporate different operating modes, which arise due to the occurrence of extreme weather phenomena such as flooding and drought episodes. To guarantee the stability during mode switching, a proper analysis on permanence regions–given by a collection of equilibrium states–for the switched linear system is presented. The permanence region is computed within a compact set, which depends on an adequate level region for the canals. A suitable algorithm is used to formulate an asymptotic stable Model Predictive Control (MPC) that steers and maintains the states of the system inside the target region indefinitely in a feasible manner. This strategy is successfully tested in a simulation using a realistic model of a canal.

Switching Model Predictive Control for Thin McKibben Muscle Servo Actuator

Dynamic characteristics and control of thin McKibben muscle (TMM) have not yet been fully investigated, especially on the translational antagonistic pair system. Therefore, the objective of this study is to propose a Switching Model Predictive Control (SMPC) based on a Piecewise Affine (PWA) system model to control a translational antagonistic-pair TMM servo actuator. A novel configuration enables the servo actuator to achieve a position control of 40 mm within a small footprint. The result shows that the feedback system gives minimal steady-state errors when tracking staircase and setpoint references ranging from 0 to 3.5 cm. The controller also produces better transient and steady-state responses than our previously developed Gain-scheduled Proportional–Integral–Derivative (GSPID) controller. The evidence from this study suggests that a predictive control for a TMM servo actuator is feasible.

Intelligent Driver Assistance and Energy Management Systems of Hybrid Electric Autonomous Vehicles

Automotive companies continue to develop integrated safety, sustainability, and reliability features that can help mitigate some of the most common driving risks associated with autonomous vehicles (AVs). Hybrid electric vehicles (HEVs) offer practical solutions to use control strategies to cut down fuel usage and emissions. AVs and HEVs are combined to take the advantages of each kind to solve the problem of wasting energy. This paper presents an intelligent driver assistance system, including adaptive cruise control (ACC) and an energy management system (EMS), for HEVs. Our proposed ACC determines the desired acceleration and safe distance with the lead car through a switched model predictive control (MPC) and a neuro-fuzzy (NF) system. The performance criteria of the switched MPC toggles between speed and distance control appropriately and its stability is mathematically proven. The EMS intelligently control the energy consumption based on ACC commands. The results show that the driving risk is extremely reduced by using ACC-MPC and ACC-NF, and the vehicle energy consumption by driver assistance system based on ACC-NF is improved by 2.6%.

Effective adoption of vehicle models for autonomous vehicle path tracking: a switched MPC approach

Efficient path tracking plays a key role in the overall ride experience of Autonomous Vehicles (AVs). Model Predictive Control (MPC) is one of the most competent techniques which is capable of handling multiple variables and constraints. The performance of this controller heavily relies on the proper choice of the vehicle model used to predict future states. This work proposes a novel MPC framework for the effective adoption of vehicle models to achieve a compromise between MPC’s performance and computational cost. To this aim, a Switched MPC (SMPC) using vehicle models with different levels of complexity and fidelity is developed. The SMPC uses a novel supervision scheme to adopt the appropriate vehicle model based on the models’ prediction error and MPC’s solution time. Two different configurations of SMPC are implemented: (1) A Fuzzy Logic System (FLS), and (2) An adaptive switching supervisor. The outcomes show that both approaches can perform path tracking accurately on roads with varying curvatures for different AV speeds. Moreover, comparisons with the conventional MPCs show that the proposed controllers can perform comparably with selective use of a complex vehicle model. Notably, they benefit from a higher computation efficiency from sidestepping the most complex models during path tracking.

A novel switched model predictive control of wind turbines using artificial neural network-Markov chains prediction with load mitigation

The existing model predictive control algorithm based on continuous control using quadratic programming is currently one of the most used modern control strategies applied to wind turbines. However, heavy computational time involved and complexity in implementation are still obstructions in existing model predictive control algorithm. Owing to this, a new switched model predictive control technique is developed for the control of wind turbines with the ability to reduce complexity while maintaining better efficiency. The proposed technique combines model predictive control operating on finite control set and artificial intelligence with reinforcement techniques (Markov Chains, MC) to design a new effective control law which allows to achieve the control objectives in different wind speed zones with minimization of computational complexity. The proposed method is compared with the existing model predictive control algorithm, and it has been found that the proposed algorithm is better in terms of computational time, load mitigation, and dynamic response. The proposed research is a forward step towards refining modern control techniques to achieve optimization in nonlinear process control using novel hybrid structures based on conventional control laws and artificial intelligence.

Microgrid Operation Optimization Using Hybrid System Modeling and Switched Model Predictive Control

Optimization of economic aspects of microgrid operation in both grid-connected and islanded mode leads to contradictive definitions of optimality for both modes. There is no general agreement on how to cope with this duality. To address this issue, as well as modern energy market requirements and a better renewable energy utilization necessity in the case of large facilities, a comprehensive control solution utilizing the appropriate model is needed. In response, the authors propose a hybrid microgrid model covering fundamental features and designed to work in conjunction with two switched receding horizon control laws. A relevant controller is chosen according to the current microgrid operation mode and its cost function tailored to specific demands of the islanded or grid-connected operation. Performed research led to a new switched hybrid model predictive control approach focused on microgrid economic optimization. This approach utilizes an appropriate hybrid microgrid model also contributed by the authors. The introduced solution turned out to be effective in overall energy cost reduction in the case of large commercial facilities, regardless of grid-connection and renewable generation scenarios. Furthermore, it also provides satisfactory renewable energy and storage capabilities utilization in changing grid connection conditions.