Predictive Mode Control Research Articles

Tube-based integral sliding mode predictive control scheme for constrained uncertain nonlinear time-delay system

Tube-based integral sliding mode predictive control scheme for constrained uncertain nonlinear time-delay system

Double-Layer Fast Terminal Sliding Mode Predictive Control for PMSM Speed Regulation

Double-Layer Fast Terminal Sliding Mode Predictive Control for PMSM Speed Regulation

Fault-tolerant model predictive sliding mode control for trajectory replanning of multi-UAV formation flight

To tackle the trajectory-following problem of multiple unmanned aerial vehicles (UAVs) characterized by high non-linearity and strong coupling, this paper methodologically separates the dynamics of fixed-wing UAVs into two subsystems and designs appropriate controllers for each loop. Unlike previous works, the proposed multi-purpose method simultaneously accounts for constraints, computational time, external disturbances, and actuator faults. The inclusive structure of the proposed strategy is as follows: Firstly, in the outer loop, by employing the high precision and constraint-handling attributes of nonlinear model predictive control (NMPC), the trajectories of the agents are guided to their reference positions while considering spatial limitations, including no-fly zone evasion and inter-vehicle collision evasion. Then, the optimal states of the inner loop are designed. Secondly, in the inner loop, a fault-tolerant sliding mode predictive control (SMPC) is reconfigured to accommodate identified actuator faults and follow the optimal states produced by NMPC. The effectiveness of the suggested algorithm is verified through a series of simulation results. Comparison simulation results substantiate the ascendancy of the suggested dual-loop method over the NMPC trajectory replanning algorithm.

Collision-free trajectory tracking strategy of a UUV via finite-time extended state observer-based sliding mode predictive control

This paper focuses on a trajectory tracking problem for unmanned underwater vehicles (UUVs) subject to current disturbances and static obstacles. A double-loop framework is established. The kinematic governor employs model predictive control(MPC), which takes into account the UUV’s kinematic characteristics as well as the conditions for obstacle avoidance when calculating the control command and provides an optimal velocity command input for the dynamic controller. Then, the dynamic controller is developed based on a fast finite-time extended state observer (FESO) and a fast adaptive integral terminal sliding mode controller (FAITSMC). The construction of the fast FESO integrates UUV’s dynamics model, a proportional integral velocity variable, and a fractional order term of the output observation error, which can identify model uncertainties and external disturbances in finite time. By means of adaptive uncertainty compensation, the FAITSMC enables the error between actual speed and speed reference to converge to a minimum quickly. By applying Lyapunov stability theory and the finite-time analysis technique, sufficiency criteria are established to guide and keep the UUV on a reference trajectory via an inner-outer loop control structure. Finally, simulation examples in different scenarios are presented to verify the feasibility and effectiveness of the derived theoretical results.

Data-driven integral sliding mode predictive control with optimal disturbance observer

In this paper, a novel data-driven integral sliding mode predictive control algorithm based on an optimal disturbance observer (DDISMPC-ODO) is proposed for a class of nonlinear discrete-time systems (NDTS) subject to external disturbances. The designed optimal disturbance observer realizes the precise observation of the lumped disturbance, thus ameliorating the accuracy of the controller and weakening problems with chattering. In this work, a robust pseudo-partial derivative (PPD) estimation algorithm is introduced, which not only improves the system performance, but also facilitates theoretical proof of parameter estimation and tracking accuracy. The convergence of the PPD estimation error and disturbance observation error is proved. It is also proved that the accuracy of the disturbance observation error can converge to O(T3) and then the magnitude of the sliding variable and the tracking error are also reduced to O(T3) respectively. Finally, the effectiveness of the proposed method is demonstrated by a simulation example and an experiment.

Fault Tolerant Control of Fuzzy Stochastic Distribution Systems With Packet Dropout and Time Delay

The fault diagnosis (FD) and fault tolerant control (FTC) problem of the fuzzy stochastic distribution system (SDS) over packet dropout and time delay is studied. Firstly, the static model of linear fuzzy logic system that approximates the output PDF and the dynamic model with the multiplicative fault in a fuzzy system are established. The random packet dropout and time delay caused by the network are described in a unified framework, in which the lost data is compensated with the latest data received by the buffer. On this basis, an adaptive observer is devised to estimate the unknown multiplicative fault. The design of sliding mode predictive fault tolerant controller is discussed to ensure that the system still has good tracking performance after the fault occurs. Numerical simulations on a molecular weight distribution control system are supplied to prove the effectiveness of the presented scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to improve the reliability of the fuzzy stochastic distribution system with packet dropout and time delay. This kind of stochastic distribution systems is described by the relationship between the input and the output PDF rather than the normal relationship between the input and the output. When the multiplicative fault occurs in the system, the design of fault diagnosis strategy and fault-tolerant controller becomes an important challenge, especially when the system is subject to packet dropout and time delay. To address the above problems, a new fault diagnosis and fault-tolerant control scheme is proposed, which can accurately estimate the time and size of the fault and obtain the satisfactory fault-tolerant control effect.

Predictor-based constrained fixed-time sliding mode control of multi-UAV formation flight

In this work, a predictor-based fixed-time sliding mode control is designed to tackle the problem of achieving precise trajectory tracking control of multiple unmanned aerial vehicle formation flight. The proposed approach simultaneously addresses various practical requirements, such as optimality, constraints, fixed-time convergence, external interferences, and computational burden. Initially, using the Taylor series concept, we derive the predictive models for the fixed-time sliding surface and reaching law. Subsequently, we formulate a constrained performance index by integrating these predictions with the control input. Ultimately, the optimal control input is computed through solving the constrained quadratic optimization problem. An outstanding feature of this study is the theoretical guarantee of closed-loop fixed-time system stability in the presence of external disturbances and constraints. Furthermore, the comparative analysis with sliding mode predictive control law and fixed-time sliding mode control methods demonstrates that the proposed approach achieves a straightforward control method with improved performance and acceptable accuracy. Finally, an experimental test is conducted to demonstrate the algorithm's potential for hardware implementation by employing the Hardware-in-the-Loop test.

Improving Steady State Accuracy in Field-Weakened Six-Phase Induction Machines with Integrator and Modulated Predictive Control

Finite-control-set model predictive control techniques are considered an exciting option for high-performance control multiphase drives due to their fast dynamic response, ability to handle multiple targets and constraints, and adaptability to different power converters or machine models. However, these techniques have some drawbacks, such as poor current reduction (x−y) and steady-state error (d−q), especially in the field weakening zone. Although some proposals have addressed these issues by adding modulation stages or designing new cost functions, there is still room for improvement, especially in steady-state error reduction. Therefore, this article proposes to include an integrator attached to a modulated predictive current controller applied to a six-phase induction machine to improve its performance throughout the entire speed range regarding steady-state error mitigation. Experimental tests were carried out to validate the effectiveness of the proposed controller. Tests were carried out evaluating the reduction of the steady-state error (d−q), the current tracking, the (x−y) currents reduction and the total harmonic distortion.

Predictive trajectory tracking control for the USV in networked environments with communication constraints

The trajectory tracking control problem of under-actuated unmanned surface vessels (USVs) suffering from communication constraints (network delay, packet loss, packet disorder) as well as external environment disturbance is investigated in this paper. To solve the aforementioned trajectory control problem, a nonlinear networked predictive control strategy based on the discrete sliding mode framework is proposed. Initially, using the forward Euler discretization method, a novel discrete virtual velocity control law is designed to transform trajectory tracking control into virtual velocity tracking control. Then, a sliding mode controller is suggested to suppress external disturbance of the USV. In addition, a networked sliding mode predictive control (NSMPC) scheme is proffered to guarantee active compensation of the communication constraints and stability of the trajectory tracking system simultaneously. Afterward, a theoretical analysis is given to show that the closed-loop system is uniformly bounded stable after the introduction of the digital network. Finally, extensive comparative simulation examples are provided to demonstrate the effectiveness of the proposed control strategy.

Predefined-Time Sliding Mode Predictive Current Control for Disturbance Suppression and Dynamic Response Improvement in SPMSM Drives

Predefined-Time Sliding Mode Predictive Current Control for Disturbance Suppression and Dynamic Response Improvement in SPMSM Drives

Jointed Observer-Based Sliding Mode Predictive Control for Interconnected Power Systems With Input Delays

Jointed Observer-Based Sliding Mode Predictive Control for Interconnected Power Systems With Input Delays

Sliding Mode Predictive Control for Enhanced Lateral Motion Stability in Independent Drive Electric Vehicle With Input Delay and Disturbance Compensation

Sliding Mode Predictive Control for Enhanced Lateral Motion Stability in Independent Drive Electric Vehicle With Input Delay and Disturbance Compensation

Model-free distributed integral sliding mode predictive control for multi-agent systems with communication delay

This paper explores the problem of distributed cooperative tracking control for networked nonlinear multi-agent systems (MASs) with communication delays. The difficulty of distributed control of subsystems across several geographic areas is addressed by utilizing MASs in this study. To overcome modeling difficulties, a model-free control algorithm is employed. To handle networked delay, a network predictive control approach is utilized. To address these issues in a unified manner, a novel distributed model-free integral sliding mode predictive control strategy is proposed. This approach requires simple data to make predictions to achieve distributed tracking performance while also actively compensating for delays. The effectiveness of the proposed method is demonstrated through simulations and experimental results.

Extended Kalman Filter design for sensorless sliding mode predictive control of induction motors without weighting factor: An experimental investigation.

Due to their simplicity, cheapness, and ease of maintenance, induction motors (IMs) are the most widely used motors in the industry. However, if they are not properly controlled, the load torque and motor speed will fluctuate in an unsatisfactory fashion. To effectively control the load torque and speed of these IMs, it is necessary to use specialized drives. The entire system (IMs + Drives) will experience uncertainty, nonlinearities, and disruptions, which calls for an outstanding performance control structure. The sensorless sliding mode predictive torque control (SSM-PTC) for both AC-DC converter and DC-AC inverter, which are utilized for feeding the IM, is investigated in this work. The AC-DC converter is controlled using the SSM-PTC method in order to follow the DC-link reference voltage throughout any changes in the operating point of the IM. While the DC-AC inverter is controlled using a sensorless predictive power control (SPPC). Within a unity power factor, this SPPC regulates the reactive power flow between the motor and the supply to account for the undesirable harmonic components of the grid current. In addition, an experimental performance improvement of SSM-PTC of IM supplied by a 5-leg AC-DC-AC power converter using extended Kalman filter (EKF) without weighting factor (WF) is also studied in this work. Design and implantation of the suggested control systems are performed using a dSPACE 1104 card. The experimental results of the proposed converter control demonstrate that the suggested approach effectively regulated the DC link, reducing load torque and speed fluctuations. In the context of inverter control, a prompt active power response yields a motor current waveform that resembles a sinusoidal pattern, exhibiting minimal levels of harmonic distortion.

Mode Predictive Direct Power Control Scheme with Fixed Operation Frequency for a Vienna Rectifier Based on Voltage‐Sensorless

Model predictive direct power control (MPDPC) is a suitable method for the highly nonlinear system of a three‐level Vienna rectifier. However, the traditional MPDPC suffers from irregular switching states, which can decrease current tracking accuracy and generate high current ripple and electromagnetic noise. Additionally, the use of sensors can increase system hardware costs and complexity. To enhance operational reliability and reduce the fault impact of AC voltage sensors, a double closed‐loop control strategy based on voltage‐sensorless MPDPC in the inner loop and sliding mode control (SMC) in the outer loop is proposed. First, the virtual flux model of the three‐phase Vienna rectifier is established in the two‐phase static coordinate system, and the second‐order low‐pass filter is used to improve the voltage observer to estimate the grid‐voltage. Meanwhile, an improved grid voltage observer based on the second‐order low‐pass filter is designed to improve the observation effect. Second, to solve the variable switching operation, current fluctuation and wide harmonic spectrum caused by the traditional MPDPC effectively, a double closed‐loop control strategy with constant switching frequency based on voltage‐sensorless MPDPC in the inner loop and SMC control in the outer loop is proposed. Based on the principle of SVPWM modulation, the modulation voltage of SVPWM is calculated directly, in order to verify the correctness of the theoretical analysis, extensive simulation and matching experimental is presented to verify the correctness of the theoretical analysis. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Disturbance-observer-based discrete-time sliding mode predictive control for overhead cranes with matched and unmatched disturbances

In actual working environments, underactuated overhead cranes are susceptible to the adverse effects of the mechanical friction, uncertain parameters, and other disturbances. In particular, if the payload is disturbed (i.e. the unmatched disturbance), unpredictable swings would occur. Therefore, a discrete time sliding mode predictive control for overhead cranes based on a disturbance observer is proposed. Specifically, to eliminate uncertain disturbances, we propose a disturbance observer to estimate and compensate the disturbance to the input channel. Then, a new discrete sliding mode control law is obtained by adding the disturbance error estimator to the discrete sliding mode surface. In order to reduce the chattering inherent in the sliding mode, we integrate the discrete sliding mode law into the model predictive control, and use the rolling optimization and feedback correction of the predictive control, and finally output the optimal control law to act on the system. This control method can not only further improve the control accuracy, but also has sufficient robustness. Simulation and robustness experiments under various conditions verify the effectiveness of the proposed controller.

A nonsingular fast terminal sliding mode predictive controller for underwater soft crawling robots with parameter uncertainty

Unknown disturbances and material properties affect the trajectory tracking of underwater soft clawing robots. A nonsingular fast terminal sliding mode predictive controller is proposed to eliminate these effects. To characterize hysteresis and creep in dielectric elastomer materials, a dynamic model incorporating an improved dead-zone operator algorithm is constructed. Meanwhile, a nonsingular fast terminal sliding mode predictive controller that combines the Lie derivative function and sliding mode controller surface is developed to improve the accuracy and robustness of trajectory tracking. Additionally, the dynamic response and convergence speed of the proposed control system are improved by using an adaptive nonlinear disturbance observer to estimate the disturbance and uncertainty terms. Finally, the superiority and effectiveness of the proposed control method are verified by numerical simulations and experiments.

An improved standalone photovoltaic system with hybrid dual integral sliding mode and model predictive control for MPPT

AbstractIn this research paper, a double diode model‐based photovoltaic (PV) system with a proposed hybrid dual integral sliding mode control (DISMC) and model predictive control (MPC) based maximum power point tracker (MPPT) has been started for a standalone power system. Compared with the single diode model, the advanced model dual diode provides accurate maximum power point (MPP) estimation with high velocity and more searching efficiency. It also delivers the extrapolation of current‐voltage characteristics of the photovoltaic system under any weather situation. Compared to classical sliding mode control (SMC) and dual integral sliding mode controller (DISMC) based MPPT, the proposed hybrid dual integral sliding mode controller (DISMC) and model predictive control (MPC) MPPT provide high, robust behavior with minimized steady‐state error. A high gain multilevel boost converter is employed, which provides an output voltage directly proportional to the level number. Moreover, the predictive model controller extracts peak power from the PV module by predicting errors in further sampling periods and switching pulses. The proposed hybrid MPPT method provides a fixed switching frequency of a high gain multilevel boost converter under varying environmental and loading conditions. Practical responses justify the capability of advanced MPPT control using the dSPACEDS1104 platform.

A multivariable sliding mode predictive control method for the air management system of automotive fuel cells

The proton exchange membrane fuel cell gas control has been one key point in fuel cell management systems. The complexity and coupling of the air management system make it difficult to achieve precise air intake adjustment. In this paper, an accurate joint control method for the air flow and pressure regulation is proposed. The nonlinear mathematical model of the air management system is developed to describe the output characteristic and state change. Based on this, the feedback linearization method is proposed to obtain the direct correspondence between control variables and controlled variables. In addition, to solve the problem that the controlled variables cannot be measured directly, an extended state observer is applied to estimate the stack cathode pressure. The sliding mode predictive control method is proposed to control the oxygen excess ratio and cathode pressure simultaneously. The relative order of the system is used to design the sliding mode surface, and the corresponding predictive model is proposed. The results obtained by simulation experiments show that pressure and mass flow have little effect on each other through decoupling. The proposed algorithm has been verified to have high precision, fast response, and robustness through comparative experiments.

Design of Sliding Mode Controller Based on 7 nm Gate Logic

In this paper, a new discrete-time sliding mode predictive control (DSMPC) strategy with a PID sliding function is proposed for synchronous DC–DC Buck converter. The model predictive control, along with digital sliding mode control (DSMC) is able to further reducing the chattering phenomenon, steady-state error, overshoot, and undershoot of the converter output voltage. The proposed control method implementation only requires output error voltage evaluation. The effectiveness of the proposed DSMPC is proved through simulation results executed by the MATLAB/SIMULINK software. These results demonstrate its performance is superior to DSMC. The selected synchronous Buck converter in this paper has 380 V input voltage and 48 V output voltage that can be applied in sections of DC distribution systems.