Output-feedback predictive control of constrained linear systems via set-membership state estimation

Modern networked control systems are cyber-physical systems (CPSs), in which the cyber space is tightly integrated with the physical world and human intervention. Forming the basis of future smart services, CPSs are expected to significantly promote our life. However, CPSs also suffer from cyber-attacks due to their increasing connections to the Internet. This paper investigates secure model predictive control (MPC) for constrained CPSs subject to actuator attacks, which intentionally manipulate control commands from a controller to actuators. In our study, the theory of invariant set is used to construct a Luenberger observer for error-bounded state estimation in the presence of actuator attacks. Then, a robust output feedback MPC controller is designed for constrained and attacked systems based on a set-membership state estimator. After that, stability conditions are theoretically established for the proposed MPC controller. A numerical example is also given to demonstrate the effectiveness of the proposed secure MPC approach.

Model predictive control (MPC) is an efficient and growing approach to power converter control. This paper proposes an improved and simplified model predictive current control (MPCC) technique for a four-level nested neutral point clamped (4L-NNPC) converter. Conventional MPCC exhibits better performances as compared to the conventional linear control system such as fast dynamic response, consideration of the system constraints, and nonlinearities. However, the application of the conventional model predictive current control (MPCC) approach on complex systems provokes a significant number of calculations, which is the main hurdle to its practical implementation. To fix this flaw, this paper proposes an effective algorithm to shorten the execution time of the conventional MPCC. In this proposed technique, 216 current predictions of the conventional MPCC are skipped and converted into one required voltage vector (RVV) prediction. With this equivalent reference voltage transformation, the calculation burden of MPCC is significantly reduced, while the output performance is not influenced. The results of the simplified MPCC for the 4L-NNPC converter are analyzed and compared with the conventional MPCC. The computational time is reduced by 19.56% using the simplified MPCC, while keeping an approximately similar error of output currents. The switching frequency and total harmonic distortion (THD) of the proposed method are reduced by 8.16% and 0.07%, respectively, as compared to the conventional technique. These results demonstrate the fact that that the performance of a conventional MPCC is enhanced with the proposed MPCC. The proposed algorithm can be applied to several inverter topologies.

To estimate validated bounds on the actual state vector of uncertain non-linear systems, cooperative output injections methods are proposed in this contribution. The aim of the output injections is to design set-membership state estimators that ensure the order-preserving property between the lower, actual and upper state trajectories. Based on a special sensors placement, continuous-time and event-triggered output injections are proposed to cope with the conservatism of the classical bounding system methods. Furthermore, based on some properties of monotone dynamical systems, the convergence of the proposed set-membership state estimators is shown. It is worth pointing out that the proposed set-membership state estimation method allows one: on one hand, to avoid the conservatism related to the use of similarity transformations usually required in the framework of interval observer design approaches, and on the other hand, to circumvent the pessimism accumulation related to the wrapping effect of set-valued iterative numerical schemes.

In this paper, the set-membership state and parameter estimation problems are considered for linear time-varying systems. The noises in the system are unknown but bounded and the variation of parameters is considered as a bounded expansion. The entire estimator is an interactive estimation of parameters and states. In parameter estimation, the exact set of parameter estimates may be non-convex because of the uncertain states. A polytopic linear parameter varying (LPV) enclosure of the system regression expression is constructed as a convex relaxation. The result of parameter estimation is a set to include the true parameter of the model. Meanwhile, in the state estimation, a similar LPV enclosure of the state-space model is also used to bound the state matrix uncertainties. This LPV enclosure in the state estimation is narrowed by the parameter estimation, and then the set of state estimates is computed to include the true state. The constrained zonotope (CZ) is used to describe sets of interest and the necessary set operations are expanded to LPV mapping. Because of the highly adjustable accuracy of CZ, the proposed algorithm can make a better trade-off between computational accuracy and efficiency. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.

Effective recursive parallelotopic bounding for robust output-feedback control

The wide availability of microprocessors with much higher computational power capabilities has encouraged many researchers during the last decade to introduce the predictive control techniques to the field of power electronics and motor drives. One technique of predictive control, which is called model predictive control, is introduced in this paper and its application to control a five-phase induction motor is also presented. The mathematical formulation of both model predictive torque control and model predictive speed control algorithms are explained, which shows that the algorithms' equations are intuitive so they can be easily understood and implemented. Since a load torque estimate is needed in the predictive speed controller, an observer based on the minimal-order Gopinath's method is designed to get the load torque value. A comparison between the two techniques is carried out based on a simulation study. Also, the simulation results show the effectiveness of the predictive control in achieving a high dynamic performance control of multiphase induction motor drives.

This article is concerned with the set-membership state estimation problem for power distribution networks (PDNs) over a resource-constrained communication network under the influence of unknown but bounded (UBB) noises. Firstly, in order to alleviate the pressure of information communication network (ICN) while meeting the state estimation requirements, the event-triggered mechanism is adopted to send data containing more valid information to estimation center, reasonably reducing the signal transmission frequency. Secondly, an event-triggered dual set-membership filter (ET-DSMF) is designed to improve the performance of state estimation. The proposed filter performs a discrete approximation for a semi-infinite programming problem by the random sampling technique, and a compact linearization error bounding ellipsoid is obtained by solving the dual problem of the nonlinear programming. Subsequently, a sufficient condition for the existence of the estimated ellipsoid is derived depending on the mathematical induction method. The key time-varying filter gain matrix and optimal estimated ellipsoid are determined recursively by solving a convex optimization problem, according to the minimum trace criterion. Finally, the effectiveness of the proposed filtering algorithm is demonstrated by performing simulation experiments on the IEEE 13 distribution test system.

<div class="section abstract"><div class="htmlview paragraph">This article presents an autonomous steering control scheme for articulated heavy vehicles (AHVs). Despite economic and environmental benefits in freight transportation, lateral stability is always a concern for AHVs in high-speed highway operations due to their multi-unit vehicle structures, and high centers of gravity (CGs). In addition, North American harsh winter weather makes the lateral stability even more challenging. AHVs often experience amplified lateral motions of trailing vehicle units in high-speed evasive maneuvers. AHVs represent a 7.5 times higher risk than passenger cars in highway operation. Human driver errors cause about 94% of traffic collisions. However, little attention has been paid to autonomous steering control of AHVs. To improve the directional performance of AHVs under a high-speed lane-change maneuvers, an autonomous steering control scheme is proposed for a tractor/semi-trailer using a model predictive control (MPC) technique, which controls the steering angle of the tractor front wheels. Various control methods are developed to improve the path following of AHVs, but they only focus on the trajectory tracking of the tractor. The current MPC-based scheme considers both the tractor and trailer for path tracking to improve directional performance of the AHV. The effectiveness of the proposed scheme is examined using co-simulations, in which the MPC controller is designed using MatLab/SimuLink and the virtual tractor/semi-trailer is generated in TruckSim. Simulation results demonstrate the effectiveness of the proposed autonomous steering control scheme.</div></div>

This paper compares two torque control methodologies, namely Field Oriented and Model Predictive ones, for monitoring Interior Permanent Magnet Motor (IPMM) over a wide speed range. IPMM magnetic saturation and cross coupling effects are taken into consideration, increasing significantly the precision of the developed controllers. Maximum Torque per Ampere (MTPA) and Field Weakening (FW) operating modes below and above the rated speed, respectively, are considered for an Electric Vehicle (EV) application. In the Field Oriented Control (FOC) case, this is achieved by implementing convenient Look-Up Tables (LUT) and PI controllers, while in the Model Predictive Control (MPC) case it is obtained via a particular cost function. Moreover, in the MPC case, an additional power loss tracking factor is introduced in the cost function, in order to minimize the total motor losses over the entire drive cycle. The proposed control techniques are evaluated under both steady state and dynamic conditions, illustrating the relative advantages of MPC in terms of robustness and dynamic behavior.

Repetitive model predictive control applied to a simulated moving bed chromatography system

This study introduces a novel approach, combining radial basis function neural network (RBFNN) and model predictive control (MPC) techniques to enhance energy efficiency in HVAC systems for sustainable buildings. The proposed methodology is evaluated in a single-story commercial and residential building in Najran, Saudi Arabia, utilizing new input parameters such as ambient temperature, cooling load, and compressor speed, alongside output metrics including room temperature and total exergy destruction and coefficient of performance (CoP) of the HVAC system. Significant improvements in energy management practices were observed, with a reduction in energy consumption by approximately 15% compared to conventional control models. The model’s predictive capabilities were validated against real-world electricity consumption data, demonstrating a high correlation with discrepancies ranging from 0.2% to 2.5%. Furthermore, the integration of machine learning techniques enabled more precise control of HVAC operations, addressing concerns regarding the system’s dynamic behavior and optimizing performance under varying occupancy patterns. While in the commercial building, the model achieves RMSE and CV values of approximately 1.0 and 0.61 for room temperature, 1.21 and 0.48 for exergy destruction, and 0.65 and 0.30 for CoP. However, for the residential building, RMSE and CV values are approximately 0.95 and 0.69 for room temperature, 1.08 and 0.31 for exergy destruction, and 0.55 and 0.27 for CoP.

Model predictive control (MPC) techniques have been applied to the modelling of a motorcycle rider, believed to offer more realistic representation of the riding strategy compared with previous methods, notably optimal control. The results from the MPC model have been compared with the optimal control results, showing good similarities and also some notable differences. The results of the application of MPC techniques to the simulation of a motorcycle rider suggest that the approach has wider applicability to rider modelling, and allows greater scope for the definition of the rider's control approach. Notably, for limited rider preview, shortcomings using the optimal control approach are overcome using the MPC method. Furthermore, the approach is believed to more accurately reflect the control actions taken by a human motorcycle rider.

Conventional deterministic set-membership (SM) estimation is limited to unknown-but-bounded uncertainties. In order to exploit distributional information of probabilistic uncertainties, a probability-guaranteed SM state estimation approach is proposed for uncertain linear time-invariant systems. This approach takes into account polynomial dependence on probabilistic uncertain parameters as well as additive stochastic noises. The purpose is to compute, at each time instant, a bounded set that contains the actual state with a guaranteed probability. The proposed approach relies on the extended form of an observer representation over a sliding window. For the offline observer synthesis, a polynomial-chaos-based method is proposed to minimize the averaged H2 estimation performance with respect to probabilistic uncertain parameters. It explicitly accounts for the polynomial uncertainty structure, whilst most literature relies on conservative affine or polytopic overbounding. Online state estimation restructures the extended observer form, and constructs a Gaussian mixture model to approximate the state distribution. This enables computationally efficient ellipsoidal calculus to derive SM estimates with a predefined confidence level. The proposed approach preserves time invariance of the uncertain parameters and fully exploits the polynomial uncertainty structure, to achieve tighter SM bounds. This improvement is illustrated by a numerical example with a comparison to a deterministic zonotopic method.

This work investigates the event‐driven model predictive control problem for perturbed nonlinear systems with input and state constraints. Firstly, a new event‐driven triggering condition, considering the gradient and accumulation of the error between the optimal and actual states at adjacent moments with a state‐dependent relative threshold is designed with a guaranteed positive inter‐event time to avoid Zeno behaviour. Secondly, an error gradient and accumulation type event‐driven model predictive control framework is designed based on the new triggering condition and the dual‐mode strategy, for effectively reducing the calculation and communication burden of the model predictive control controller while ensuring desired control performance. Furthermore, the feasibility and the input‐to‐state practical stability (ISpS) of gradient and accumulation‐type event‐driven model predictive control are rigorously guaranteed theoretically. At last, the simulation results are provided to validate the this algorithm.

For a single-phase Shunt Active Power Filter (SAPF) with a two-step prediction, this research presents a modified current control based on a Model Predictive Current Control (MPCC) technique. An H-bridge inverter, a DC link capacitor, and a filter inductor comprise the single-phase SAPF topology. The SAPF reference current is computed using the DC-link capacitor voltage regulation-based PI control technique. The weighting factor-based model predictive current controller is used to track the current commands. The essential dynamic index for evaluating waveform quality is the Total Harmonic Distortion (THD) of a source current and switching frequency of power switches. The conventional methods the THD and switching frequency are not considered as an objective function, so that a weighting factor-based MPCC technique is used to obtain a good compromise between the THD of the source current and switching frequency of power switches. Through MATLAB simulation and experimentation with the Cyclone-IV EP4CE30F484 FPGA board, the usefulness of the proposed control technique is proven. As compared with hysteresis, predictive PWM, and conventional MPCC control methods, the cost function-based MPCC algorithm provides a lower switching frequency (13.4 kHz) with an optimal source current THD value.