Control Sampling Time Research Articles

A novel bi-level temporally-distributed MPC approach: An application to green urban mobility

Model predictive control (MPC) has been widely used for traffic management, such as for minimizing the total time spent or the total emissions of vehicles. When long-term green urban mobility is considered including e.g. a constraint on the total yearly emissions, the optimization horizon of the MPC problem is significantly larger than the control sampling time, and thus the number of the variables that should be optimized per control time step becomes very large. For systems with dynamics that involve nonlinear, non-convex, and non-smooth functions, including urban traffic networks, this results in optimization problems that are computationally intractable in real time. In this paper, we propose a novel bi-level temporal distribution of such complex MPC optimization problems, and we develop two mathematically linked short-term and long-term MPC formulations with small and large control sampling times that will be solved together instead of the original complex optimization problem. The resulting bi-level control architecture is used to solve the two MPC formulations online for real-time control of urban traffic networks with the objective of long-term green mobility. In order to assess the performance of the bi-level control architecture, we perform a case study where a rough version of the model of the urban traffic flow, S-model, is used by the long-term MPC level to estimate the states of the urban traffic networks, and a detailed version of the model is used by the short-term MPC level. The results of the simulations prove the effectiveness (with respect to the objective of control, as well as computational efficiency) of the proposed bi-level MPC approach, compared to state-of-the-art control approaches.

Torque/Speed Control of 3PH Synchronous Reluctance Motor Using Direct Torque Control

This paper presents an investigation of the dynamic performance of the synchronous reluctance motor (SynRM) controlled by the Direct Torque Control (DTC) technique to attain either torque or speed control. The system under study comprises synchronous reluctance motor driving a mechanical load. The inner control loop of the DTC algorithm is employed to achieve torque control through the torque hysteresis comparator in which the command torque is set externally as desired. The speed control is accomplished by adding an outer control loop comprising a speed comparator and PID controller. The controller parameters are accurately tuned to provide high transient and improved dynamic performance of the motor. The SynRM model, in the d-q synchronously rotating reference frame, and the overall components of the DTC algorithm are formulated in MATLAB/Simulink. The transient and dynamic response of the SynRM controlled by DTC is investigated when the overall system is subjected to various patterns of torque and speed commands. The results confirmed that the proposed DTC technique attained a stable, fast, and robust response for both torque and speed control loops of the controlled SynRM. Furthermore, the impact of varying the controller sampling time was explored, the outcomes revealed that as the sampling time reduces, the motor dynamic performance is significantly improved.

High-Precision Speed Control for Low-Speed Gimbal Systems Using Discrete Sliding Mode Observer and Controller

The high-precision control of angular speed in low-speed gimbal servo systems is an essential topic in the operation of the single gimbal control moment gyro (SGCMG). This article proposes a discrete control algorithm to suppress the influence of various disturbances on the low-speed gimbal servo system and the constraint between controller parameters and sampling time is established to ensure the stability of the system. To begin with, the disturbance analysis of the low-speed gimbal servo system is presented, including the torque ripple in the permanent magnet synchronous motor (PMSM), nonlinear friction, coupling torque caused by the spacecraft motion, and the parameter uncertainties. Then, a composite control method combining the sliding mode controller (SMC) with sliding mode observer (SMO) is applied to the speed loop and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> -axis current loop. The interferences are estimated by two SMOs, while SMCs are designed to eliminate the influence brought by the residual disturbances. The stability of the overall system is proven based on the Lyapunov criterion. Finally, simulation and experimental results verify the effectiveness of the proposed composite control method.

Research of controller of Permanent magnet linear synchronous motors via model predictive direct thrust control

How to achieve high-precision control is the core issues of the linear motor research. To solve this problem, this paper proposes a new control algorithm. on the one hand, through the sliding mode control for the unknown torque observations, in the process of observation, select a specific based function to approximate the sign function in order to eliminate the jitter of the vicinity of the switching surface; On the other hand, the use of model predictive control method (Model Predictive Control) to achieve the torque compensator track to achieve the velocity and displacement tracking. The constant sampling time of the model predictive control in the new method can effectively avoid the problems that the number of switching per unit time of the inverter can’t be expected.

Hybrid MPC System for Platoon based Cooperative Lane change Control Using Machine Learning Aided Distributed Optimization

This study is devoted to developing a platoon-based cooperative lane-change control (PB-CLC). It coordinates the trajectories of a CAV platoon under a platoon-centered platooning control to accommodate the CAV lane-change requests from its adjacent lane, aiming to reduce the negative traffic impacts on the platoon resulting from lane-change maneuvers, on the premise of ensuring CAVs’ safety and mobility. Mathematically, the PB-CLC control is established using a hybrid model predictive control (MPC) system. The hybrid MPC system involves an MPC-based mixed integer nonlinear programming optimizer (MINLP-MPC) for optimal lane-change decisions, which considers multiple objectives such as traffic smoothness, driving comfort and lane-change response promptness subject to vehicle dynamics and safety constraints. To ensure the feasible lane-change, this study investigates and provides a lower bound of the lane-change time window by analyzing the MINLP-MPC model feasibility. Apart from the optimal lane-change decision consideration, the hybrid MPC system is well designed to ensure the control continuity and smoothness. In particular, the hybrid MPC system control feasibility and stability are proved to enable the platoon's back-and-forth state switchings between car-following and lane-change accommodation states. Next, we developed a machine learning aided distributed branch and bound algorithm (ML-DBB) to solve the MINLP-MPC model within a control sampling time interval (< 1 second). Specifically, built upon computer simulation and the c-LHS sampling technique, supervised machine learning models are developed offline to predict a reduced solution space of the integer variables, which is further integrated into the distributed branch and bound method to solve the MINLP-MPC model efficiently online. Extensive numerical experiments validate the effectiveness and applicability of the ML-DBB algorithm and the PB-CLC control.

The integration of scheduling and control: Top-down vs. bottom-up

The flexible operation of continuous processes often requires the integration of scheduling and control. This can be achieved by top-down or bottom-up approaches. We compare the two paradigms in-silico using an air separation unit as a benchmark process. To demonstrate the top-down paradigm, we identify data-driven models of the closed-loop process dynamics based on a mechanistic model and use them in scheduling calculations that are performed offline. The resulting target trajectories are passed to a linear model predictive control (LMPC) system and implemented in the process. To demonstrate the bottom-up paradigm, we define an economic nonlinear model predictive control (eNMPC) scheme, which performs dynamic optimization using the full model in closed-loop to directly obtain the control variable profiles to be implemented in the process. We provide implementations of the process model equations as both a gPROMS and a Modelica model to encourage future comparison of approaches for flexible operation, process control, and/or handling disturbances. The performance, advantages, and disadvantages of the two strategies are analyzed using demand-response scenarios with varying levels of fluctuations in electricity prices, as well as considering the cases of known, instantaneous, and completely unknown load changes. The similarities and differences of the two approaches as relevant to flexible operation of continuous processes are discussed. Integrated scheduling and control leverages existing infrastructure and can be immediately applied to real operation tasks. Both operation strategies achieve successful process operation with remarkable economic improvements (up to 8%) compared to constant operation. eNMPC requires more computational resources, and is – at the moment – not implementable in real-time due to maximum optimization times exceeding the controller sampling time. However, eNMPC achieves up to 2.5 times higher operating cost savings compared to the top-down approach, owing in part to the more accurate modeling of key process dynamics.

Remote sensing data acquisition system based on FPGA sampling time variable

The microwave imager is based on remote sensing data acquisition system to conduct remote sensing observation of the earth. The sampling mode and sampling time affect the Microwave Imager. After analyzing the traditional remote sensing data acquisition system, when collecting multi-channel data, the sampling interval time and sampling start time can’t be controlled, and multi-channel data acquisition can’t be carried out in parallel. The new remote sensing data acquisition system uses fully parallel mode and FPGA to control sampling time and sampling interval. Simulation results and test results show that the remote sensing data acquisition system is multichannel and controllable.

Practical stability analysis of sliding‐mode control with explicit computation of sampling time

AbstractSliding‐mode control is a wide‐spread approach to a number of practical problems. The classical stability analyses of sliding‐mode control had to face the difficulty of defining a trajectory of a system whose dynamics are discontinuous in the state variable. Different generalizations of the system trajectory were suggested, such as Filippov solutions. Another generalization is based on the sample‐and‐hold framework, where the system dynamics are in continuous time and the control is changed only at certain discrete times. The current work addresses the sample‐and‐hold stability analysis of sliding‐mode control with special attention to an explicit computation of the required controller sampling time. A computational example is provided.

Explicit analytical tuning rules for digital PID controllers via the magnitude optimum criterion

Analytical tuning rules for digital PID type–I controllers are presented regardless of the process complexity. This explicit solution allows control engineers 1) to make an accurate examination of the effect of the controller's sampling time to the control loop's performance both in the time and frequency domain 2) to decide when the control has to be I, PI and when the derivative, D, term has to be added or omitted 3) apply this control action to a series of stable benchmark processes regardless of their complexity. The former advantages are considered critical in industry applications, since 1) most of the times the choice of the digital controller's sampling time is based on heuristics and past criteria, 2) there is little a–priori knowledge of the controlled process making the choice of the type of the controller a trial and error exercise 3) model parameters change often depending on the control loop's operating point making in this way, the problem of retuning the controller's parameter a much challenging issue. Basis of the proposed control law is the principle of the PID tuning via the Magnitude Optimum criterion. The final control law involves the controller's sampling time Ts within the explicit solution of the controller's parameters. Finally, the potential of the proposed method is justified by comparing its performance with the conventional PID tuning when controlling the same process. Further investigation regarding the choice of the controller's sampling time Ts is also presented and useful conclusions for control engineers are derived.

Deadbeat Model-Predictive Torque Control With Discrete Space-Vector Modulation for PMSM Drives

This paper proposes an alternative strategy of finite-control-set model-predictive torque control (MPTC) to reduce the computational burden and the torque ripple and decouple the switching frequency from the controller sampling time. An improved discrete space-vector modulation (DSVM) technique is utilized to synthesize a large number of virtual voltage vectors. The deadbeat (DB) technique is used to optimize the voltage vector selection process, avoiding enumerating all the feasible voltage vectors. With this proposed method, only three voltage vectors are tested in each predictive step. Based on the improved DSVM method, the three candidate voltage vectors are calculated by using a novel algebraic way. This new strategy has the benefits of both the MPTC method and the DB method. The effectiveness of the proposed strategy is validated based on a test bench.

A Novel Current Control Technique for Photo Voltaic Integrated Single Phase Shunt Active Power Filter

In this paper, a Model Predictive Current Control (MPCC) of single phase Photo Voltaic (PV) integrated Shunt Active Power Filter (SAPF) is proposed. A PV integrated single phase Voltage Source Inverter (VSI) is used to generate a part of the real power and compensate harmonics, reactive power of a Non-Linear Load (NLL). In this paper, a DC link voltage regulation based PI control algorithm is adopted for determining the reference current at the filter side. The performance of SAPF with PV and associated control methods like switch on response and change of nonlinear load were carried out. Also, the performance of the controller has been examined with variation in irradiance, filter inductance value and variation in controller sampling time through a simulation in MATLAB software.

Neural network control design considerations for the active damping of a smart beam

In this study, possible options for the active damping of a smart beam with piezoelectric patches using neural network control algorithm, are presented. The algorithms used for the control are Neural Direct Inverse and Feedback Linearisation (NARMA-L2). Additionally, several possible modifications used for the purpose of improving the control, such as different values of control gain or sampling time of the training data, as well as step-wise control are tested.

Experimental Comparisons Between Implicit and Explicit Implementations of Discrete-Time Sliding Mode Controllers: Toward Input and Output Chattering Suppression

This brief presents a set of experimental results concerning the sliding mode control of an electropneumatic system. Two discrete-time control strategies are considered: an explicit and an implicit (that is very easy to implement with a projection on the interval [-1,1]) Euler discretizations. While the explicit implementation is known to generate numerical chattering, the implicit one is expected to significantly reduce chattering while keeping the accuracy. The experimental results reported in this brief remarkably confirm that the implicit discrete-time sliding mode supersedes the explicit ones, with several important features: chattering in the control input is almost eliminated (while the explicit and saturated controllers behave like high-frequency bang-bang inputs), the input magnitude depends only on the perturbation size and is independent of the controller gain and sampling time.

Type-III closed loop control systems-Digital PID controller design

The problem of tuning digital PID controllers for type-III control loops is investigated in this work. Type-III control loops are capable of achieving perfect tracking of step, ramp and parabolic reference signals with zero steady state position, velocity and acceleration error. The proposed PID control law involves any dominant time constants of the process itself, and any parasitic dynamics introduced by both the process and the controller, i.e. time delays within the closed control system. The development of the proposed control law takes place in the frequency domain and basis of the theory is the principle of the Magnitude Optimum criterion. The final control law consists of closed form expressions which involve also the controller's sampling time Ts. The potential of the proposed theory is justified for the control of several benchmark process models throughout simulation examples. The affect of the choice of the controller's sampling time is investigated further to the step and frequency response of the control loop both for the output of the control loop and the controller's command signal.

Discrete‐Time Multioverlapping Controller Design for Structural Vibration Control of Tall Buildings under Seismic Excitation

In this paper, a computationally effective strategy to obtain multioverlapping controllers via the Inclusion Principle is applied to design discrete‐time state‐feedback multioverlapping LQR controllers for seismic protection of tall buildings. To compute the corresponding control actions, the proposed semidecentralized controllers only require state information from neighboring stories. This particular configuration of information exchange allows introducing a dramatic reduction in the transmission range required for a wireless implementation of the communication system. To investigate the behavior of the proposed semidecentralized multioverlapping controllers, a proper simulation model has been designed. This model includes semiactive actuation devices with limited force capacity, control sampling times consistent with the communication latency, time‐delayed state information, and communication failures. The performance of the proposed multioverlapping controllers has been assessed through numerical simulations of the seismic response of a 20‐story building with positive results.

Figure Error Control for Microgrooving on Ordinary Lateral Milling Machines Using a Reference Surface to Control Cutting

This paper proposes improving microgrooving system on an ordinary lateral milling machine by using controlled cutting with reference surface (CCRS). CCRS improves ultra-precision machining accuracy through in-process measurement and control. CCRS controls relative displacement between the tool and workpiece, effectively improving table motion error accuracy. CCRS was confirmed in simulation and experiments to (1) improve accuracy if digital control sampling time is enough shorter than the error motion period and (2) worsen surface roughness because the feed rate equals cutting speed in shaping if the shorter sampling time for the error motion period cannot be used. In other words, the relationship between improvement in table error motion accuracy and desirable surface roughness is a trade-off solved by using fly cutting with CCRS.

Predicting road crashes from a mathematical model of alertness regulation—The Sleep/Wake Predictor

Sleepiness is related to factors such as the time of day, the time since awakening and the duration of prior sleep. The present study investigated whether actual road crashes could be predicted from a mathematical model based on these three factors (the Sleep/Wake Predictor—SWP). Data were derived from a population-based case-control study of serious injury crashes. Data on accident time (or control sampling time) and start and end of prior sleep were entered into the model (blind). The predicted sleepiness values were used in logistic regressions. The results showed a highly significant odds ratio (OR) = 1.72 (confidence interval = 1.41–2.09) for each incremental step of sleepiness on the output sleepiness scale (nine steps). There was also a significant interaction with blood alcohol level, showing high OR values for high sleepiness levels and alcohol levels above 50 mg% (0.05 g/dl). It was concluded that the model is a good predictor of road crashes beyond that of alcohol level, and that interaction between the two carries a very high risk.

Modeling and control of an electric servo drive with strong restrictions in the control variable

Detailed mathematical model of a DC servo drive is established and validated by measurements. Due to battery power supply, a pulse-width modulated (PWM) voltage signal, and a microcontroller with low sampling rate (typical for embedded systems) a comprehensive switched non-linear description results. Linear and sliding-mode controllers (SMC) are designed for the drive. Robustness with respect to parameter variations and measurement noise is investigated by simulation. In contrast to many other reports the linear controller outperforms the SMC because of the given restrictions in control input and sampling time.

Cooperative flow dynamics in production lines with buffer level dependent production rates

We study, in the fluid flow framework, the cooperative dynamics of a buffered production line in which the production rate of each work-cell does depend on the content of its adjacent buffers. Such state dependent fluid queueing networks are typical for people based manufacturing systems where human operators adapt their working rates to the observed environment. We unveil a close analogy between the flows delivered by such manufacturing lines and cars in highway traffic where the driving speed is naturally adapted to the actual headway. This close analogy is thoroughly explored. In particular, by investigating the dynamic response of small perturbations around free flow stationary regimes, we can draw a “phase diagram”. This diagram exhibits two different flow patterns, namely the free and jamming production regimes. The transitions between these regimes are tuned by the production control parameters (i.e. the buffer capacities, the reaction sensitivity, the control sampling time, etc.). We finally extract a dimensionless dynamic parameter directly relevant for design purposes.

Visual Feedback Control with Compensation of Image Information Delay (Verification by Using Three-Link Space Robot)

Time delay by image processing exists in the information acquired from a CCD camera. Since renewal time of image information is longer than sampling time of control, control delay occurs and lowers the performance of feedback control. In this paper, in order to compensate image information delay, we propose the method that combined Kalman filter, time delay compensation, and state estimation between the sample points of image information. Advantages of the proposed method are demonstrated by simulation and experiments for the three-link space robot.