Cascaded Model Predictive Control Research Articles

Design and Real-Time Implementation of a Cascaded Model Predictive Control Architecture for Unmanned Aerial Vehicles

Modeling and control are challenging in unmanned aerial vehicles, especially in quadrotors where there exists high coupling between the position and the orientation dynamics. In simulations, conventional control strategies such as the use of a proportional–integral–derivative (PID) controller under different configurations are typically employed due to their simplicity and ease of design. However, linear assumptions have to be made, which turns into poor performance for practical applications on unmanned aerial vehicles (UAVs). This paper designs and implements a hierarchical cascaded model predictive control (MPC) for three-dimensional trajectory tracking using a quadrotor platform. The overall system consists of two stages: the mission server and the commander stabilizer. Different from existing works, the heavy computational burden is managed by decomposing the overall MPC strategy into two different schemes. The first scheme controls the translational displacements while the second scheme regulates the rotational movements of the quadrotor. For validation, the performance of the proposed controller is compared against that of a proportional–integral–velocity (PIV) controller taken from the literature. Here, real-world experiments for tracking helicoidal and lemniscate trajectories are implemented, while for regulation, an extreme wind disturbance is applied. The experimental results show that the proposed controller outperforms the PIV controller, presenting less signal effort fluctuations, especially in terms of rejecting external wind disturbances.

Discrete space vector modulation and optimized switching sequence model predictive control for three-level voltage source inverters

This paper proposes a discrete space vector modulation and optimized switching sequence model predictive controller for three-level neutral-point-clamped inverters in grid-connected applications. The proposed strategy is based on cascaded model predictive control (MPC) for controlling the grid current while maintaining the capacitor voltage balanced without weighting factor. To enhance the closed-loop performance, the external MPC evaluates 19 basic and 138 virtual vectors (VV) of the proposed space vector method. The optimal control voltage is then selected using an extended deadbeat method to reduce the execution time of the proposed control algorithm. By using the discrete space vector modulation principle, the VV are synthesized based on switching sequence (SS) and are divided into negative and positive SSs considering their impact on the neutral point (NP) potential. The inner MPC evaluates both types of SSs and selects the one that keeps the capacitor voltage balanced. Various controllers are evaluated and compared against the proposed control strategy. The results show that the proposed strategy improves performance without weighting factor, while maintaining a total harmonic distortion of current to be less than 2%. Compared to the modulated MPC which provides the same fixed switching frequency, the proposed controller reduces the computational burden by over 50% while also providing better NP voltage balance accuracy.

A cascaded model predictive control based trajectory tracking controller for an autonomous and remotely operated vehicle with LOS guidance

ABSTRACT Autonomous and Remotely Operated Vehicle (ARV) are widely used for underwater detection and typically require ARVs to track predefined trajectories. Manual operation is not very accurate and stable, and intelligent control methods are popularly devised. This paper firstly introduces the line of sight (LOS) method for desired heading guidance. Later, a cascaded model predictive control (MPC) is proposed. In the cascaded MPC, a quantum-behaved particle swarm optimisation algorithm based on a non-linear decreasing contraction-expansion coefficient (QPSO-CE-nonlinear) is applied to optimise the controller’s output. Several validation and comparative simulations are carried out. The results show that the controller has advantages in accuracy, convergence, and stability. The LOS is applicable to holonomic ARV and accelerates convergence. The QPSO-CE-nonlinear algorithm can improve the rate of convergence when solving optimisation problems. In addition, compared with MPC-SMC (sliding mode control), the cascaded MPC is more robust.

Cascaded Model Predictive Control of Six-Phase Permanent Magnet Synchronous Motor with Fault Tolerant Ability

Cascaded Model Predictive Control of Six-Phase Permanent Magnet Synchronous Motor with Fault Tolerant Ability

A Novel Low-Complexity Cascaded Model Predictive Control Method for PMSM

A novel low-complexity cascaded model predictive control method for permanent magnet synchronous motors is proposed to achieve a fast dynamic response to ensure the system’s steady-state performance. Firstly, a predictive speed controller based on an extended state observer is designed in the outer speed loop to improve the anti-interference ability of the system; then, a low-complexity three-vector predictive control algorithm is adopted in the current inner loop, taking into account the steady-state performance of the system and lower computational burden. Finally, a comparative analysis is conducted between the proposed method and traditional methods through simulation and experiments, proving that the proposed method performs well in dynamic and static performance. On this basis, the computational complexity of the current inner loop three-vector prediction algorithm is effectively reduced, indicating the correctness and effectiveness of the proposed method.

Model Predictive Controller Approach for Automated Vehicle's Path Tracking.

In this paper, a model predictive control (MPC) approach for controlling automated vehicle steering during path tracking is presented. A (linear parameter-varying) LPV vehicle plant model including steering dynamics is proposed to determine the system evolution matrices. The steering dynamics are modeled in two different ways by using first-order lag and a second-order lag; the application of the first-order system resulted in a slightly more accurate path-following. Additionally, a cascade MPC structure is applied in which two MPCs are used; the second-order steering dynamics are separated from the path-following controller in a second MPC. Both steering system models and the cascade MPC are evaluated in simulation and on a test vehicle. The reference trajectory is calculated based on a fixed predefined path by transforming the necessary path segment to the vehicle ego coordinate system, thereby describing the reference for the path-following task in a novel way. The MPC method computes the optimal steering angle vector at each time step for following the path. The longitudinal dynamics is controlled separately by a PI controller. After simulation evaluation, experimental tests were conducted on a test vehicle on an asphalt surface. Both simulation and experimental results prove the effectiveness of the proposed reference definition method. The effect of the applied steering system models is evaluated. The inclusion of the steering dynamics in the prediction model resulted in a significant increase in controller performance. Finally, the computational requirements of the proposed control and modeling methods are also discussed.

An adaptive cascade predictive control strategy for connected and automated vehicles

SummaryConnectivity is a key element enabling intelligent vehicles to communicate with each other and the Smart Road. In general, the connectivity is allowed by an On‐Board Unit enabling the Vehicle to Everything communication. This paper proposes an innovative unit acting not only as a device allowing connectivity but also as an intelligent electronic control unit to perform advanced and adaptive control strategies able to give reference trajectories and actuator set‐points to the low‐level control systems of power‐train and vehicle dynamics. In particular, based on a cascade model predictive control, an adaptive control strategy is proposed, considering time‐varying parameters and information related to vehicle kinematics and dynamics. Such a strategy allows the vehicle to follow a certain origin‐destination path in the inertial frame, based on an upper controller that considers a vehicle kinematic model so as to give speed references and target values for the steering angle at the wheels to the inner predictive control loop acting on both longitudinal and lateral vehicle dynamics. To check the effectiveness of the proposed approach, a comparison is made with existing cascade control strategies. The results show better safety and lane‐keeping performance. Then, using Hardware‐In‐the‐Loop testing for the proposed Intelligent On‐Board Unit, the considered approach demonstrates robustness against parametric variations, signal delay and noise both in the Global Positioning System and in the yaw rate sensor. The achieved results show good robustness properties, safety performances and confirm the real‐time execution of the cascade control strategy, paving the way to future developments necessary before tests in a real road scenario.

Research on Path Tracking for an Orchard Mowing Robot Based on Cascaded Model Predictive Control and Anti-Slip Drive Control

In complex orchard environments, orchard mowing robots are prone to longitudinal slippage because of the characteristics of tires and the adhesion conditions of the road surface, which makes it difficult for the robots to maintain high-precision path tracking and autonomous navigation positioning. This not only affects the accuracy of path tracking but also leads to unstable motion for the mowing robots. To solve the above problems, we take an orchard mowing robot as the control object and establish a cascaded path-tracking controller and an adaptive time domain model based on a kinematics model. By designing a linear error model, an objective function, and constraint conditions for the mowing robot, the optimal linear velocity and angular velocity of the mower are obtained and converted into the speed of the driving wheel. Then, an anti-slip driving controller is designed based on fuzzy control of the slip rate. The slip-rate-based fuzzy controller is constructed according to the real-time speed of the mower and the reference speed of the driving wheel solved by the model predictive controller, and anti-slip driving control is implemented through a combination of a PID controller and a tire dynamics model. To verify the effectiveness of the proposed method, simulation and field experiments are conducted. The experimental results show that the slip rate of the driving wheel of the mower remains within the target slip rate range in the orchard working environment, avoiding excessive driving wheel sliding. Furthermore, the average lateral error of the path-tracking controller is controlled within 0.05 m, and the average value of the longitudinal error is kept within 0.04 m, which satisfies the control accuracy requirements of lawn mower operations. The proposed method provides a reference optimization scheme for improving the path-tracking and motion stability of a mowing robot.

Computationally-Efficient Model Predictive Control of Dual-Output Multilevel Converter in Hybrid Microgrid

This paper presents a hybrid microgrid configuration with a flying capacitor dual-output (FCDO) converter. The output ports of the FCDO converter can directly interface ac sources/loads operating at different amplitudes and frequencies without additional ac/dc/ac converter units. Compared to the conventional configuration, the hybrid microgrid with the FCDO converter operates at multilevel voltages, reduced power conversion stages, less power switch count, and fewer control loops. The paper also presents a cascaded model predictive control (CMPC) algorithm for such configuration to control the variables, namely three-phase dual-output currents, ac/dc bus and FC voltages, and active/reactive power. The proposed CMPC sequentially executes multiple single-objective MPC units with adaptive dynamic reference (ADR) models to control the multivariable. The controller first obtains the optimum voltage vector for each output port by minimizing the output current errors, where the ADR model generates the appropriate references. Finally, the controller identifies the optimum state from the determined voltage vector pair by minimizing the FC voltage errors. Unlike conventional MPC, the CMPC algorithm reduces the computational burden of the controller and attains multivariable control without additional control loops and weighting factors. Furthermore, the converter's performance with CMPC algorithm is validated experimentally on a low-power hybrid microgrid.

Cascaded Model Predictive Control of a Tandem-Rotor Helicopter

This letter considers cascaded model predictive control (MPC) as a computationally lightweight method for controlling a tandem-rotor helicopter. A traditional single MPC structure is split into separate outer and inner-loops. The outer-loop MPC uses an SE2(3) error to linearize the translational dynamics about a reference trajectory. The inner-loop MPC uses the optimal angular velocity sequence of the outer-loop MPC to linearize the rotational dynamics. The outer-loop MPC is run at a slower rate than the inner-loop allowing for longer prediction time and improved performance. Monte-Carlo simulations demonstrate robustness to model uncertainty and environmental disturbances. The proposed control structure is benchmarked against a single MPC algorithm where it shows significant improvements in position and velocity tracking while using significantly less computational resources.

Decentralized Circulating Current Attenuation With Model Predictive Control for Distributed/Shunted Single/Three-Phase Grid-Tied Inverters

This letter proposed a decentralized control method to attenuate the circulating current among the grid-connected inverters without extra cost on the communication. Based on the combination of cascaded model predictive control (MPC) and modified <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$LCL$</tex-math></inline-formula> filter topologies, the developed method can locally limit the circulating current for different types of inverters, e.g., single/three-phase grid-connection, and various applications, e.g., shunted or distributed inverters with/without common dc bus, respectively. The merits of the proposed method include the following. 1) Decentralized circulating current control without the need of a central controller or the corresponding communication to collect the sampling and calculate the common mode components for leakage current attenuation among different inverters. 2) Generalized control method that can be applied to single/three-phase inverters for both shunted and distributed inverters with/without common dc bus applications. 3) No synchronization is needed for pulsewidth modulation signals among different inverters to attenuate the circulating current. 4) Cascaded MPC without the need of individual inverter’s grid side uncertain filter parameters for the attenuation of circulating current and system parametric modeling. 5) Better transient performance with the inner loop MPC. The experimental results verified the proposed method.

Double-Loop Control Strategy With Cascaded Model Predictive Control to Improve Frequency Regulation for Islanded Microgrids

The microgrid (MG) which effectively utilizes distributed energy resources (DERs) is crucial to the modern power system. However, since the islanded MG with high penetration of DERs inherently lacks sufficient inertial support, the frequency regulation is challenging for the voltage source converter (VSC)-based MG. This paper presents a cascaded model predictive control (MPC) scheme for both the outer and inner loops of primary control, which improves frequency regulation capability with the advantage of satisfactory dynamic response and high tracking accuracy. In the outer loop, to enhance dynamic frequency characteristics, the MPC-based virtual synchronous generator (VSG) method with two control objectives is proposed. According to the movement direction of frequency under different load switching cases, the corresponding frequency response process speeded up or slowed down properly. In the inner loop, an improved finite-set double vector MPC (DV-MPV) is proposed to follow the output reference from the outer loop, which contributes to accurate frequency control by reducing tracking error, and faster dynamic response as well. The simulation and experimental results further demonstrated the effectiveness of the proposed method.

Robust Locomotion Exploiting Multiple Balance Strategies: An Observer-Based Cascaded Model Predictive Control Approach

Robust locomotion is a challenging task for humanoid robots, especially when considering dynamic disturbances. This article proposes a disturbance observer-based cascaded model predictive control (MPC) approach for bipedal locomotion, with the capability of exploiting ankle, stepping, hip and height variation strategies. Specifically, based on the variable-height inverted pendulum model, a nonlinear MPC that is run at a low frequency is built for 3-D locomotion (i.e., with height variation) while accounting for the footstep modulation as well. Differing from previous works, the nonlinear MPC is formulated as a convex optimization problem by semidefinite relaxation. Subsequently, assuming a flywheel at the pelvis center, a linear MPC that is run at a high frequency is proposed to regulate angular momentum (e.g., through rotating the upper body), which is solved by convex quadratic programming. To run the cascaded MPC in a closed-loop manner, a high order sliding mode observer is designed to estimate system states and dynamic disturbances simultaneously. Simulation and hardware experiments demonstrate the walking robustness in real-world scenarios, including 3-D walking with varying speeds, walking across non-coplanar terrains and push recovery.

Cascaded model predictive controller performance for the selection of robust working fluids in absorption refrigeration cycles

• Evaluation of the dynamic performance of 13 novel working fluids. • Cascade model predictive control is of superior performance than cascade PI control. • Systematic ranking of ABR fluids based on setpoint tracking and resource utilization. • A multi-criteria assessment approach for working fluid selection is presented. • Acetaldehyde/Dimethylformamide exhibits superior steady state and dynamic performance. Current working fluid selection approaches for the absorption refrigeration cycle are based exclusively on steady-state cycle performance. In real operating conditions, the cycle is subject to exogenous disturbances, with detrimental effects on their performance, resulting in increased resource utilization or even failure to meet load demands. In the present work, the problem of working fluid evaluation for the absorption refrigeration cycle is investigated, by considering both the steady state and the dynamic performance of the system. The evaluation of 13 novel, organic working fluids is performed utilizing a cascaded proportional-integral and a cascaded model predictive controller, implemented on a single-effect absorption refrigeration cycle. A set of previously validated non-linear, dynamic process models, developed in ASPEN Plus are used to create the linear models required for the model predictive controller. The closed loop dynamic performance is evaluated based on speed of response, resource utilization and deviations from the desired operating point (setpoint), while operating under a disturbance scenario involving load demand changes. Multi-criteria assessment results indicate that the cascaded model predictive control is considerably more consistent than the cascade PI controller. The novel mixture of acetaldehyde/dimethylformamide exhibits superior performance than the conventional NH 3 /H 2 O mixture, by 57% in speed of response and 76% in resource utilization. The mixture ιs also 25% and 12% better than NH 3 /H 2 O in steady-state cost per ton of cooling and coefficient of performance, indicating high economic potential and robustness for single-effect ABR systems.

Cascade Model Predictive Current Control for Five-Phase Permanent Magnet Synchronous Motor

As to the model predictive control for five-phase permanent magnet synchronous motor (PMSM), it is difficult to ensure the optimal control performance by using calculation method to obtain the weighting factor. To solve this problem, a cascade model predictive current control based on the idea of sequential model predictive control is proposed for five-phase PMSM for the first time. Firstly, the principle of selecting the optimal voltage vector by the proposed method is analyzed in detail. Then, combined with the characteristics of five-phase PMSM, the control priority of controlled variables is set for designing two cost function schemes without weight factor. The maximum torque scheme can generate a trapezoidal stator voltage, improving the DC bus voltage utilization rate and the system loading capacity. The minimum harmonic current scheme can reduce the harmonic of stator current, obtaining small system noise and vibration. The experimental results indicate that the proposed method can ensure the optimality of the voltage vector applied. Therefore, the five-phase PMSM obtains good performance under different working operation, such as small torque ripple, fast dynamic response, and small harmonic current.

Single-phase PFC Converter with Cascade Model Predictive Control for Integrated Grid Current and DC Voltage Controllers

Single-phase PFC Converter with Cascade Model Predictive Control for Integrated Grid Current and DC Voltage Controllers

Dual-Loop Continuous Control Set Model Predictive Control for a Three-phase Unity Power Factor Rectifier

In this article, a novel cascade model-predictive control for a three-phase unity power factor rectifier is presented. Unlike the traditional control techniques for rectifiers, the proposed method uses a cascade dual-model-predictive control for the inner and outer control loops. The three-phase currents of the rectifier are controlled with the inner control loop, while the desired output voltage is achieved with the outer control loop. In this proposal, the output power is used as a feedforward to enhance the transient dynamics of the output dc voltage. Several advantages can be highlighted such as a fixed switching frequency, and output voltage variations are avoided when a sudden change in the load or a voltage sag appears. The overall control proposal has been fully integrated into a digital signal processor. Selected experimental results are introduced to validate the theoretical contributions of this article.

Four-Quadrant Operations of Bidirectional Chargers for Electric Vehicles in Smart Car Parks: G2V, V2G, and V4G

This paper presents the four-quadrant operation modes of bidirectional chargers for electric vehicles (EVs) framed in smart car parks. A cascaded model predictive control (MPC) scheme for the bidirectional two-stage off-board chargers is proposed. The controller is constructed in two stages. The model predictive direct power control for the grid side is applied to track the active/reactive power references. The model predictive direct current control is proposed to achieve constant current charging/discharging for the EV load side. With this MPC strategy, EV chargers are able to transmit the active and reactive powers between the EV batteries and the power grid. Apart from exchanging the active power, the vehicle-for-grid (V4G) mode is proposed, where the chargers are used to deliver the reactive power to support the grid, simultaneously combined with grid-to-vehicle or vehicle-to-grid operation modes. In the V4G mode, the EV battery functions as the static var compensator. According to the simulation results, the system can operate effectively in the full control regions of the active and reactive power (PQ) plane under the aforementioned operation modes. Fast dynamic response and great steady-state system performances can be verified through various simulation and experimental results.

Steady-state target calculation integrating economic optimization for constrained model predictive control

Real-time optimization (RTO) in cascade model predictive control (MPC) is a classic two-layer architecture for economic optimization and dynamic control. The two-layer architecture is always integrated with steady-state target calculation (SSTC) to guarantee the feasibility of the control target. In this paper, we present a modified SSTC algorithm to recalculate the steady-state point for MPC when the optimal point calculated by RTO is no longer attainable. The proposed algorithm takes economic performance into account and retains the feasibility of solutions. Based on a classic SSTC formulation, ours has a new objective function with two terms, one for optimizing economic performance and the other for ensuring the feasibility. Moreover, the objective function of the proposed SSTC is rearranged, and the SSTC problem is formulated by quadratic programming, which can be solved in an instant for real applications. Furthermore, we provide a detailed 4-in-4-out model of the Fischer esterification reaction, which is similar to the benchmark continuous stirred tank reactor (CSTR) model, to demonstrate the effectiveness of our SSTC algorithm. The proposed algorithm is able to improve economic performances, whether a convex or non-convex economic cost function is concerned.

Design of model predictive controllers for PMSM drive system based on the extended Kalman filter observer

Considering some technical and economic reasons, it is not easy to directly measure the time-varying states which have a great impact on speed regulation performance in permanent magnet synchronous motor (PMSM) drive system. This paper proposes a method to estimate rotor speed, rotor position and load torque disturbance by utilising real-time estimations, based on the extended Kalman filter (EKF). In order to guarantee the accuracy and stability of PMSM drive system while running under load torque disturbance, this paper proposes a cascade closed-loop control strategy as a solution. The entire system is designed to track reference speed trajectory based on the estimated rotor speed, rotor position and load torque, using the model predictive control (MPC) algorithm based on linear PMSM model. Simulation results demonstrate the effectiveness of the states observer, and the cascade MPC structure shows the good performance in speed tracking of PMSM drive system.