Observer-based Model Predictive Control Research Articles

Neural Network and Extended State Observer-Based Model Predictive Control for Smooth Braking at Preset Points in Autonomous Vehicles

Neural Network and Extended State Observer-Based Model Predictive Control for Smooth Braking at Preset Points in Autonomous Vehicles

Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method.

Constraint tightening-based control for small-satellite formations using differential aerodynamic forces

Aerodynamic forces hold significant promise for controlling small-satellite formations in low Earth orbit. However, significant challenges still exist regarding complex input constraints and their application to multi-satellite formations. This paper proposes a constraint tightening-based control scheme for small-satellite formations, utilizing differential drag and lift as actuating forces. Two attitude adjustment strategies, namely the reorientation strategy and the single-radial rotation strategy, are compared in terms of their control effectiveness and applicability. Relative aerodynamic coefficients are employed as control inputs in view of their solid reachable region, followed by the development of a decoupling algorithm for determining pointing angles. Through a method that tightens the reachable region using liner models, the input constraints are linearized and can be explicitly expressed. Subsequently, an observer-based Model Predictive Control (MPC) algorithm is formulated to meet these linearized input constraints and provide feedforward compensation for system disturbances. This discrete algorithm enables practical onboard implementations owing to its low computational complexity. Additionally, a concept of overall computation for all satellites’ attitudes is proposed to adapt the control scheme for multi-satellite formations. Hardware-in-the-loop simulations are conducted to evaluate the control scheme's performance in along-track and circular formations, accounting for uncertainties in aerodynamic forces and attitude dynamics.

Luenberger Observer-Based Model Predictive Control for Six-Phase PMSM Motor With Localization Error Compensation

Luenberger Observer-Based Model Predictive Control for Six-Phase PMSM Motor With Localization Error Compensation

Nonlinear disturbance observer-based model predictive control for magnetically levitated slice motor

Nonlinear disturbance observer-based model predictive control for magnetically levitated slice motor

Enhanced Tracking of DC-DC Buck Converter Systems Using Reduced-Order Extended State Observer-Based Model Predictive Control

Enhanced Tracking of DC-DC Buck Converter Systems Using Reduced-Order Extended State Observer-Based Model Predictive Control

Model Predictive Control for a Voltage Sensorless Grid-Connected Inverter With LCL Filter Using Lumped Disturbance Observer

This paper introduces a disturbance observer-based model predictive control for a voltage sensorless grid-connected inverter (GCI), which minimizes the number of sensor measurements and eliminates the steady-state error by estimating the lumped disturbance in the presence of grid impedance variations. A full-state estimation and lumped disturbance observer are obtained based on the Luenberger observer and gradient steepest descent method, respectively. A cost function, which consists of state error, is used for controller gain design. An optimal full-state observer and controller gains are obtained by solving an optimization problem based on linear matrix inequality. The discrete-time frequency responses analysis of open-loop and closed-loop systems is presented to demonstrate the filter resonance suppression. The robustness of the proposed control against grid impedance variation is analyzed through the pole-zero map approach. Simulations and experiments are conducted for a GCI under the grid impedance variation to demonstrate the theoretical analysis and the efficacy of the proposed control schemes.

Observer-based model predictive control for uncertain NCS subject to hybrid attacks via interval type-2 T–S fuzzy model

In this study, an observer-based model predictive control (MPC) algorithm is addressed for an uncertain discrete-time nonlinear networked control system (NCS) subject to hybrid malicious attacks by using interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy theory. Hybrid malicious attacks, including two typical attacks, i.e., denial-of-service (DoS) attacks and false data injection (FDI) attacks, are considered in the communication networks. Under DoS attacks, the control signals will be interfered, which cause the degradation of signal-to-interference-plus-noise ratio, then lead to packets loss. Under FDI attacks, the false signals are injected and output signals are modified so that the system performance is deteriorated. For the NCS subject to hybrid attacks, a secure observer that can resist FDI attacks is devised and a fuzzy MPC algorithm that can solve the controller gains is proposed. Besides, by updating the bound of augmented estimation error, the recursive feasibility can be guaranteed. Finally, illustrative examples are given to show the effectiveness of proposed scheme.

Disturbance Observer-Based Model Predictive Voltage Control for Electric-Vehicle Charging Station in Distribution Networks

This paper proposes a disturbance observer (DOB)-based model predictive voltage control (MPVC) method to improve the power quality of electric vehicle charging stations (EVCSs) with battery energy storage systems (BESSs) in distribution networks. As the volume of EVCS increases, we face challenges related to transformer overloading and power quality issues. In particular, voltage fluctuations in local EVCS become the most critical problem due to the highly unpredictable EV charging loads and renewable energy production. In this study, the DOB estimates the EV charging loads and PV generation power to ensure that the MPVC can compensate for them effectively and minimize the voltage fluctuation of the EVCS. The proposed MPVC with DOB does not require communication system and is obtained by solving a linear matrix inequality (LMI)-based optimization problem. Furthermore, the parameter uncertainties, caused by the inherent tolerances and aging degradation of circuit components, are considered. The effectiveness of the proposed control scheme is demonstrated based on simulations and experiments using a 10 kVA EVCS simulator.

Interval type-2 T-S fuzzy MPC for CPS under hybrid attacks over a multi-channel framework

In this paper, the problem of observer-based model predictive control (MPC) for a multi-channel cyber-physical system (CPS) with uncertainties and hybrid attacks is investigated via interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model. Both denial-of-service (DoS) and false data injection (FDI) attacks are studied due to the vulnerability of wireless transmission channels. The objective of the addressed problem is to improve the security performance of the multi-channel CPS under malicious attacks, which has not been adequately investigated in the existing MPC algorithms. Moreover, uncertainties which appear not only in the membership functions but in both state and input matrices are considered. In this paper, different from the method that FDI attacks are handled by the bounded functions, an off-line observer is designed to actively defend against the FDI attacks. Meanwhile, an on-line MPC optimization algorithm, which minimizes the upper bound of objective function respecting input constraints, is presented to obtain the secure controller gains. Finally, an illustrative example is provided to verify the effectiveness and superiority of presented approach.

A T-S fuzzy state observer-based model predictive reset control for a class of fuzzy nonlinear systems with event-triggered mechanism

In this study, the problem of observer-based control for a class of nonlinear systems using Takagi-Sugeno (T-S) fuzzy models is investigated. The observer-based model predictive event-triggered fuzzy reset controller is constructed by a T-S fuzzy state observer, an event-triggered fuzzy reset controller, and a model predictive mechanism. First, the proposed controller utilizes the T-S fuzzy model and is constructed based on state observations and discrete sampling output, which can greatly reduce the occupation of communication resources. Then, the model predictive strategy for reset law design is designed in this paper. With a reasonable reset of the controller state at certain instants, the performance of the reset control systems is improved. Finally, the validity of the proposed method is illustrated by simulation. The merits of the proposed controller in improving transient performance and reducing the communication occupation are demonstrated by comparing its results with the output feedback fuzzy controller and the first-order fuzzy reset controller.

Extended state observer-based model predictive temperature control of mechanically pumped two-phase loop: An experimental study

Mechanically pumped two-phase loop (MPTL), as an emerging two-phase heat dissipation transformational technique, has made booming progress in some critical but tough cases such as space stations and high-performance chips. However, accurate temperature regulation of MPTL is intractable due to periodic heat load disturbance, measurement noise, and micro-channel boiling nonlinearity. To address these issues, an extended state observer-based model predictive control (ESOMPC) strategy is developed in this paper. To describe the process model, this paper builds an MPTL platform equipped with a micro-channel evaporator, based on which a series of sinusoidal heat load variation is carried out to show the effects on the wall temperature. Open-loop step experiments are then conducted in terms of flow rate of the working fluid so that the process model can be identified and the nonlinearity can be analyzed. Then, the ESOMPC is developed by incorporating the disturbance information in the optimization framework of the MPC. The ESO part is utilized to estimate the unknown disturbances and compensate the nonlinearity of MPTL, and thus enhance the disturbance mitigation property of the MPC algorithm. Finally, the closed-loop experiments verifies the efficacy of the proposed solution, showing smaller temperature fluctuation and shorter settling time than the conventional controller. The results indicate the great potential of the proposed ESOMPC in enhancing the temperature control accuracy of MPTL in terms of thermal management performance.

Model predictive control for switched systems with a novel mixed time/event-triggering mechanism

This paper investigates observer-based model predictive control (MPC) for switched systems with a mixed time/event-triggering mechanism. The problem of predictive control that can achieve receding horizon optimization is considered and solved by minimizing an upper bound of the quadratic cost function. Since the system state may not be fully measured in practice, state observers are employed to estimate. A mixed mechanism including adaptive event-triggering and time-triggering is proposed, which can be switched determined by a threshold describing system performance to better balance system resource utilization and performance requirements. Then, a closed-loop switched system subject to networked-time-delay is modeled. Piecewise Lyapunov function technique and average dwell time approach are utilized to ensure asymptotical stability. Afterwards, MPC controller construction problem is turned into a LMIs feasibility problem. A new solving method of sufficient conditions for co-design of the state observers, feedback controllers and mixed triggering mechanism is derived. Lastly, simulation examples illustrate the correctness and advantages of research content.

Robust model predictive control for three-level voltage source inverters

To solve the problem of parameter mismatch in model predictive control (MPC), this paper presents a robust model predictive control method based on a fixed window optimization (FWO) algorithm for a three-level voltage source inverter that only needs to sample the current value. When compared with the traditional observer-based model predictive control (such as the Luenberger observer, sliding mode observer (SMO) and Kalman filter (KMF)), the proposed method does not require an observer with a complicated design, and its algorithm is simple and easy to understand. Meanwhile, high current sampling accuracy is not needed in the proposed method. However, it is necessary in some types of model-free predictive control. In addition, low switching frequency operation and delay compensation are also considered in this paper. In general, the proposed method is simple to implement and does not have high requirements in terms of the accuracy of its current sensor. Experimental results show that the proposed method can accurately estimate parameter values and improve the parameter robustness of MPC.

Simultaneous trajectory tracking and aerial manipulation using a multi-stage model predictive control

With the exception of a few works, the current approaches to aerial manipulation control do not typically consider the system constraints in the control design process. Also, the issue of closed-loop stability in the presence of system constraints is not thoroughly analyzed. In this paper, a novel multi-stage model predictive control (MPC)-based approach for aerial manipulation is proposed to ensure the closed-loop stability in the presence of model uncertainties and external disturbances, while satisfying the operational constraints. The detailed nonlinear model of a general aerial manipulator, consisting of a quadrotor equipped with a 3 degrees of freedom manipulator, is first developed using the Euler-Lagrange method. Subsequently, a multi-stage disturbance observer-based model predictive control is introduced in the framework of aerial manipulation. Both the aerial grasping and the trajectory tracking tasks can be simultaneously fulfilled using a novel multi-step optimization process within the MPC framework. The closed-loop stability, considering the system constraints, unmodeled dynamics, and external disturbances, is rigorously analyzed based on the Lyapunov theory. To the best of the authors' knowledge, this is the first study to perform simultaneous trajectory tracking and aerial grasping tasks while ensuring closed-loop stability in the presence of operational constraints. The simulation results demonstrate the satisfactory performance of the proposed control scheme for robust trajectory tracking and aerial manipulation missions at an acceptable computational cost.

Disturbance Estimation-Based Robust Model Predictive Position Tracking Control for Magnetic Levitation System

This article presents an improved equivalent input disturbance observer-based model predictive control (IEIDO-based MPC) scheme for position tracking control of a magnetic levitation system. It overcomes the problem of the position tracking performance degradation for the magnetic levitation system in the presence of time-varying disturbances. To enhance the accuracy of estimation, an IEIDO is first proposed to estimate both the system states and the time-varying equivalent input disturbance. The estimations of IEIDO are then integrated into the MPC design for output position prediction. Afterward, an explicit analytical form of the optimal predictive controller is obtained to achieve the optimized tracking performance, as well as the fine robustness against lumped time-varying equivalent input disturbance. Rigorous stability analysis shows that the closed-loop system is stable. Simulation and experimental results demonstrate the effectiveness of the proposed method as compared with the conventional EID and Luenberger observer-based integral MPC approaches.

Observer‐based MPC for NCS with actuator saturation and DoS attacks via interval type‐2 T–S fuzzy model

This study addresses an observer-based model predictive control (MPC) algorithm for a networked control system (NCS) under denial of service (DoS) attacks and actuator saturation via an interval type-2 Takagi–Sugeno (IT2 T–S) fuzzy model. With few studies undertaking the cyber security problems in the research of MPC algorithm, this study considers the data transmission problem when DoS attacks occur. Under DoS attacks, signals in the communication networks will be interfered. The probability of error-free packet reception depends on signal-to-interference-plus-noise ratio of the wireless transmission networks. In order to reduce the on-line computation, an off-line fuzzy observer is devised to estimate the system states. Meanwhile, an on-line MPC algorithm is proposed to minimise the performance objective function and obtain the secure model predictive controller gain. Besides, the recursive feasibility is ensured by refreshing the bound of estimation error. Finally, illustrative examples certificate the effectiveness of the presented method.

Friction-Compensation and Extended State Observer Based Model Predictive Control for PMSM Servo Systems

In the conventional design of model predictive control (MPC) for PMSM servo systems, friction is usually treated as a part of the lumped disturbances and is eliminated by disturbance observer or integral control methods. In order to obtain better control performance, a composite MPC method is proposed by introducing friction and disturbance information. Firstly, an approximate friction model is obtained by fitting the Stribeck model linearly and piecewise. Secondly, an extended state observer (ESO) is designed to estimate the lumped disturbances, which are composed of the friction compensation bias and the external load disturbance. Thereafter, a more specific prediction model is established by embedding the results of friction identification and disturbance estimation in the mechanism model. In this way, the influence of friction and disturbance are taken into account in the receding optimization process. Various experimental results demonstrate the feasibility and effectiveness of the proposed method for higher-precision motion trajectory tracking.

An efficient ship autopilot design using observer-based model predictive control

An efficient model predictive control design for ship autopilot, which is a representative marine application, is proposed based on projection neural network in this article. Ship motion control at sea exhibits the characteristics of large inertia, strong nonlinearity, and large delay; furthermore, it is frequently influenced by the external disturbances, leading to a complex uncertain problem. In addition, the amplitude of control input—the rudder is constrained. Given the mechanism of on-line computing and the advantages of handling constraints, the model predictive control is one of the most favorable solutions for this problem. Nevertheless, the major challenge of the implementation of traditional model predictive control in application is the computation intensity. In this article, the capability of parallel computation of projection neural network is employed to optimize the objective function formulated by traditional model predictive control method, aiming to improve the computational efficiency. The overall information of ship motion is normally difficult to be obtained; therefore, a state observer should be also included. Extensive studies are conducted to illustrate the effectiveness of the proposed control design.

Disturbance Observer Assisted Error Sensitive Predictive Control for Induction Motors in Sensor less Environment: A Vector Field Control Model

The exponentially rise within the demands of Induction Motors in several applications has revitalized academia-industries to develop more robust and efficient IM drives. Amongst the main classically available IM drives efforts are made either to regulate speed or torque. However, the problem inculcated due to parametric mismatch and resulting errors have much addressed. Though, predictive control based approaches are found potential to help current and torque control; however, ensuring optimal controllability under non-linear condition remained a tedious task. Filter based approaches to impose delay that eventually impacts overall control performance. Realizing it as motivation, during this research a highly robust Disturbance Observer Assisted Error Sensitive Predictive Control Strategy for IM control is developed. Subsequently, a completely unique Disturbance Observer-based Model Predictive Control strategy is developed that performs predictive current control and torque-control during a non-linear environment. Our proposed model exploits the concept of Prediction-Error to realize transient controllability. Exploiting the error information our proposed model identified the optimal voltage vector value to be injected to the 3-∅ inverter connected to the PI-based Space Vector Pulse Width Modulation system to perform transient controllability. Structurally, our proposed system encompasses SQIM motor, 3-∅ inverter, PI controller SVPWM, Flux-observer, Torque and Speed controllers, VSI units, etc. The MATLAB 2017a based simulation has revealed that the proposed model is able to do better current control, flux-torque control and torque-ripple suppression, which broadened its employability for varied applications demands fast-torque control during a noisy environment.