A hierarchical MPC and sliding mode based two-level control for freeway traffic systems with partial demand information

An active magnetic bearing (AMB) is a kind of high-performance bearing that uses controllable electromagnetic force to levitate the rotor. Its control performance directly affects the operation characteristics of high-speed motors and other electromechanical products. The magnetic bearing control model is nonlinear and difficult to control. Sliding mode control algorithm can be used in the magnetic bearing control system, but the traditional sliding mode control has the problem of high-frequency chattering, which affects the operation stability of magnetic bearings. Based on the second-order sliding mode control algorithm, a new second-order sliding mode controller for active magnetic bearing control was designed, and the stability of the designed sliding mode control law was proven by Lyapunov criterion. On the basis of the established active magnetic bearing control model, the numerical analysis of the designed controller was carried out, and the control effect was compared with that obtained by the exponential reaching law for the sliding mode control algorithm. The experimental results show that the designed sliding mode controller has better dynamic performance and stability than the exponential reaching law for the sliding mode controller.

DC/DC converters are widely used in new generation applications such as microgrid electric vehicles and storage systems. Controllers which ensure the converters output voltage on the reference value are designed with many different methods. Sliding mode control is a prominent controller design for power converters that has recently attracted the attention of researchers. Although the conventional sliding mode control has many advantages chattering and relative degree restriction are its outstanding weaknesses. The second-order sliding mode control which eliminates the disadvantages of the conventional sliding mode control uses different algorithms according to the relative degree of the system controlled. Algorithm with prescribed convergence law twisting algorithm and super twisting algorithm are commonly implemented second-order sliding mode control algorithms. In this paper controllers using second-order sliding mode control algorithms have been designed for a DC/DC buck converter feeding a resistive load. Tests realized in the Matlab/Simulink environment have revealed the robustness and performance of the controllers designed different sliding mode controller algorithms under various operating scenarios such as input voltage variation and load variation. As a result of the simulations the dynamic responses of these control algorithms have been analyzed and compared with each other and.

This article presents an original motion control strategy for robot manipulators based on the coupling of the inverse dynamics method with the so-called second-order sliding mode control approach. Using this method, in principle, all the coupling non-linearities in the dynamical model of the manipulator are compensated, transforming the multi-input non-linear system into a linear and decoupled one. Actually, since the inverse dynamics relies on an identified model, some residual uncertain terms remain and perturb the linear and decoupled system. This motivates the use of a robust control design approach to complete the control scheme. In this article the sliding mode control methodology is adopted. Sliding mode control has many appreciable features, such as design simplicity and robustness versus a wide class of uncertainties and disturbances. Yet conventional sliding mode control seems inappropriate to be applied in robotics since it can generate the so-called chattering effect, which can be destructive for the controlled robot. In this article, this problem is suitably circumvented by designing a second-order sliding mode controller capable of generating a continuous control law making the proposed sliding mode controller actually applicable to industrial robots. To build the inverse dynamics part of the proposed controller, a suitable dynamical model of the system has been formulated, and its parameters have been accurately identified relying on a practical MIMO identification procedure recently devised. The proposed inverse dynamics-based second-order sliding mode controller has been experimentally tested on a COMAU SMART3-S2 industrial manipulator, demonstrating the tracking properties and the good performances of the controlled system.

For heavy-duty diesel engines, NOX emissions reduction is strongly constrained by fuel efficiency. This paper presents a hierarchical model predictive controller (H-MPC) for coordinated control of tailpipe NOX emissions and fuel consumption. The H-MPC uses the separation of slow and fast dynamics that exist in the engine and its aftertreatment system. The controller is synthesized with an architecture in which a high-level MPC uses a longer prediction horizon compared to the low-level predictive controller which tracks the high-level controller command and manages the thermal dynamics of the aftertreatment system. Engine load preview enables the high-level controller to estimate the desired catalyst temperature ahead of time and addresses the selective catalytic reduction (SCR) slow thermal dynamics. Calculated by the high-level controller, the intake manifold pressure, and the start of injection (SOI) crank angle is used as reference trajectories in the low-level controller that regulates fast dynamical behaviors such as engine out NOX emissions. Hardware-in-the-loop (HIL) validation of this integrated H-MPC on a rapid prototype controller shows that when the SCR catalyst temperature is above light-off temperature (warmed-up condition), the engine operation is shifted to operate with the best fuel economy since the warmed-up SCR can efficiently reduce the engine-out NOX emissions. Results indicate that up to 0.8% benefit in cycle averaged BSFC along with a 13% reduction in tailpipe NOX compared to a stock engine calibration can be achieved with the coordinated engine and aftertreatment system through H-MPC.

In this article, two dual-loop control methods are proposed for attitude control and reaction wheels momentum management of the spacecraft simulator by assimilating the advantages of sliding mode and model predictive control (MPC). In the first method, by utilizing the high accuracy and constrained features of the MPC, an optimal controller is designed in the inner loop. MPC drives the angular velocity to the desired value while keeping the reaction wheel velocity to a small region near the origin, considering the constraints of the actuators. Furthermore, based on the fixed-time terminal sliding mode (TSM) the proper angular velocity is produced in the outer layer of this composite controller for the MPC part. In the second method, contrary to the first one, in the exterior loop MPC and in the interior loop finite-time TSM have been designed. The incorporated twofold nonlinear controllers’ policy insures a good dynamic and robustness against external disturbances. The high yielding of the control algorithms has been analyzed by simulation and experimental validation utilizing MATLAB software with real-time connection based on LabVIEW.

In this paper a hierarchical model predictive control framework for the energy management of a hybrid electric vehicle is extended by replacing the supervisory linear model predictive controller with a nonlinear model predictive controller. The nonlinear system dynamics of a parallel hybrid electric vehicle are incorporated into a nonlinear optimal control problem which is solved at each time instance. The discretization of the continuous time system dynamics is realized via direct multiple shooting. The low-level linear model predictive controller considers the actuator dynamics for optimal tracking of the driver's torque demand while the high-level controller considers the slow dynamics of the vehicle and the battery. The proposed strategy is validated in simulation with the worldwide harmonized light vehicles test cycle and compared to a hierarchical model predictive control framework with linearized system dynamics.

In this paper a novel hierarchical multi-level control scheme is proposed for freeway traffic systems. Relying on a coupled PDE-ODE nominal model, capturing the interaction between the macroscopic traffic flow and a platoon of connected and automated electric vehicles (CAVs) which acts as a moving bottleneck, a high-level model predictive controller (MPC) is adopted to reduce traffic congestion and vehicle fuel consumption. This controller generates, only when necessary, i.e., according to an event-triggered control logic, the most appropriate reference values for the platoon length and velocity. The platoon is in turn controlled, in an energy efficient way, by a distributed medium-level MPC, so as to track the reference speed values for its downstream and upstream end-points provided by the high-level MPC. The mismatch between the dynamics of the CAVs forming the platoon and their nominal dynamics is tackled via the design of local low-level robust integral sliding mode controllers, which have the capability of compensating for the mismatch. In the paper, the controlled platoon of CAVs is assumed to be immersed into a realistic traffic system with traffic demand not known in advance, which differs from the nominal prediction model used by the high-level MPC.

Sufficient optimality conditions for distributed, non-predictive ramp metering in the monotonic cell transmission model

High-precise and high-dynamic control is the most important requirement of permanent magnet linear synchronous motor (PMLSM) system. The main problem is to suppress the disturbances existing in PMLSM system. Sliding mode control (SMC) is one of the most robust control algorithms now, it can achieve high response speed and high robustness at the same time. But chattering problem is the main weakness of SMC. To solve that, higher order SMC algorithms have been developed. Super-twisting sliding mode control (STSMC) is a kind of second-order sliding mode control algorithm and has attract wide attention now. This paper presents a position controller for PMLSM based on STSMC, and demonstrates the advantages of this controller by comparing it with normal SMC controllers. Then a disturbance observer based on STSMC is presented, and with the observation result, the disturbance can be compensated, and the robustness of system can be further improved.

Recently, the increase of burden to operators and lack of skilled operators are the issue in the work of the hydraulic excavator. These problems are expected to be improved by autonomous control. In this paper, we present experimental results of hydraulic excavators using model predictive control (MPC) which incorporates servo mechanism. MPC optimizes digging operations by the optimal control input which is calculated by predicting the future states and satisfying the constraints. However, it is difficult for MPC to cope with the reaction force from soil when a hydraulic excavator performs excavation. Servo mechanism suppresses the influence of the constant disturbance using the error integration. However, the bucket tip deviates from a specified shape by the sudden change of the disturbance. We can expect that the tracking performance is improved by combining MPC and servo mechanism. Path-tracking controls of the bucket tip are performed using the optimal control input. We apply the proposed method to the Komatsu- made micro hydraulic excavator PC01 by experiments. We show the effectiveness of the proposed method through the experiment of digging soil by comparing servo mechanism and pure MPC with the proposed method.

Flutter suppression of wind turbine blade section based on two types of model prediction control (MPC) algorithms is investigated. The pre-twist blade, exhibiting displacements of flap-wise bending and lead-lag bending, employs 2D airfoil analysis method, and is subjected to destructive divergent instability displacements. Dynamic modelling of two-dimensional (2D) blade section is based on data analysis characterized by data fitting. The MPC methods including standard MPC and terminal-weight MPC are used to realize flutter suppression for divergent instability. Vibration control and control effects of terminal-weight MPC on divergent instability are analyzed in detail, with different parameters of prediction horizon and control horizon discussed. The superiority of terminal-weight MPC can be apparently demonstrated by comparison of standard MPC and sliding mode control with disturbance observer (SMCDO). The feasibility of hardware implementation of MPC algorithm is discussed by human-computer interaction platform. The performance improvement method of the terminal-weight MPC based on offset control has been refined from the point of view of practical application, with its remarkable effect compared with the original terminal-weight MPC.

In this paper, a nonlinear robust control technique is proposed for a class of affine nonlinear systems. This technique tries to increase tracking control performance by combining the benefits of sliding mode control (SMC) with model predictive control (MPC), for a class of nonlinear systems, in terms of smooth control law, a zero steady-state error, a faster convergence rate, and more robustness for the closed-loop system against external disturbances and model uncertainties. Furthermore, this paper provides a comparative study with the previously mentioned control methods. The controllers SMC and Model Predictive Sliding Mode Control (MPSMC) have been tested in simulation under Matlab/Simulink environment. Simulations are carried out on a nonlinear affine benchmark to evaluate the suggested technique and demonstrate its efficacy in achieving the required control performances.

Filters have become almost indispensable to counter the harmonics injection by the widely used power converters and other non-linear devices. Shunt active power filters (SAPF) controlled by direct power control (DPC) serve as a good option as they offer various advantages. The performance of the existing controllers deteriorates when the three-phase supply system deviates from its ideal situation of balanced and sinusoidal voltages. Unlike the existing methods, this work develops and employs a detailed and accurate system model based on the extended pq theory. With its proven high accuracy, the model predictive control (MPC) is used as the control algorithm for PWM generation. An in-depth comparison between MPC and other predictive methods is presented, aiming to substantiate the superior performance of MPC. Furthermore, an adaptive sliding mode control-based reactive power reference is proposed to eliminate the steady-state error in the reactive power. The proposed controller’s feasibility is rigorously evaluated through meticulous MATLAB simulations and real-world laboratory experiments. Comparative analysis against existing SAPF controllers in similar conditions undeniably establishes better grid power quality by lowering source current THD and overall reactive power consumption.

Adaptive estimation-based hierarchical model predictive control methodology for battery active equalization topologies: Part II - equalizer control

In this paper combination of standard and second order sliding mode controller are used and investigated for congestion control problem of differentiated Services (Diff-Serv) networks. Robustness against modeling uncertainties and disturbances are major features of sliding mode controller. Chattering phenomenon affects sliding mode congestion control; to overcome this problem, second order sliding mode control (SOSMC) is proposed as a congestion controller but this controller has slower response to SMC. Combination SMC and SOSMC gives better response. There are three types of traffics in Diff-Serv networks; i.e. three types of queues and based on that, the quality of their services are categorized. Each of these types of traffic has its own congestion control mechanisms. Stability of each controller is proved by Lyapunov stability law. To evaluate the capabilities of our proposed robust control strategy, simulation results are provided for both sliding mode controllers and second order slide mode controllers and their corresponding efficiencies are compared.