Model-based Control Method Research Articles

Model Based Control with Online Automatic Adaptation by Neural Network for Advanced Diesel Combustion

Model based control with physical models are proposed as an alternative to conventional control methods to improve engine performance under real driving conditions including various transient condition. Even if models are built based on physical rules, the models still have several parameters which is desirable to adapt in real time according to driving condition. Therefore, the authors developed an online automatic adaptation method for model-based control of diesel engines, which is based on neural networks. The predictive accuracy of the adapted model has been evaluated by simulation, and the performance of the feed-forward controller based on the model is evaluated by experiment under actual engine.

Simultaneous position and stiffness control of a revolute joint using a biphasic media variable stiffness actuator

Safe interactions between humans and robots are needed in several industrial processes and service tasks. Compliance design and control of mechanisms is a way to increase safety. This article presents a compliant revolute joint mechanism using a biphasic media variable stiffness actuator. The actuator has a member configured to transmit motion that is connected to a fluidic circuit, into which a biphasic control fluid circulates. Stiffness is controlled by changing pressure of control fluid into distribution lines. A mathematical model of the actuator is presented, a model-based control method is implemented to track the desired position and stiffness, and equations relating to the dynamics of the mechanism are provided. Results from force loaded and unloaded simulations and experiments with a physical prototype are discussed. The additional information covers a detailed description of the system and its physical implementation.

Hierarchical ramp metering in freeways: An aggregated modeling and control approach

This paper develops a model-based hierarchical control method for coordinated ramp metering on freeway networks with multiple bottlenecks and on- and off-ramps. The controller consists of two levels where at the upper level, a Model Predictive Control (MPC) approach is developed to optimize total network travel time by manipulating total inflow from on-ramps to the freeway network. The lower level controller distributes the optimal total inflows to each on-ramp of the freeway based on local traffic state feedback. The control method is based on a parsimonious aggregated traffic model that relates the freeway total outflow to the number of vehicles on the freeway sections.Studies on aggregated traffic modeling of networks have shown the existence of a well-defined and low-scatter Macroscopic Fundamental Diagram (MFD) for urban networks. The MFD links network aggregated flow and density (accumulation). However, the MFD of freeway networks typically exhibits high scatter and hysteresis loops that challenge the control performance of MFD-based controllers for freeways. This paper addresses these challenges by modelling the effect of density heterogeneity along the freeway and capacity drop on characteristics of freeway MFD using field traffic data. In addition, we introduce a model to predict the evolution of density heterogeneity that is essential to reproduce the dynamics of freeway MFD accurately. The proposed model is integrated as the prediction model of the MPC in the hierarchical control method.The proposed coordinated ramp metering method shows desirable performance to reduce the vehicles total time spent and eliminate congestion. The control approach is compared with other coordinated ramp metering controllers based on the MPC framework with different traffic prediction models (e.g.CTM and METANET). The outcomes of numerical experiments highlight that the MFD-based hierarchical controller (i) is better able to overcome the modeling mismatch between the prediction model and the plant (process model) in the MPC framework and (ii) requires less computation effort than other nonlinear controllers.

Model-Based Robotic Cell Aspiration: Tackling Nonlinear Dynamics and Varying Cell Sizes

Aspirating a single cell from the outside to the inside of a micropipette is widely used for cell transfer and manipulation. Due to the small volume of a single cell (picoliter) and nonlinear dynamics involved in the aspiration process, it is challenging to accurately and quickly position a cell to the target position inside a micropipette. This letter reports the first mathematical model that describes the nonlinear dynamics of cell motion inside a micropipette, which takes into account oil compressibility and connecting tube's deformation. Based on the model, an adaptive controller was designed to effectively compensate for the cell position error by estimating the time-varying cell medium length and speed in real time. In experiments, small-sized cells (human sperm, head width: ~3 μm), medium-sized cells (T24 cancer cells, diameter: ~15 μm), and large-sized cells (mouse embryos, diameter: ~90 μm) were aspirated using different-sized micropipettes for evaluating the performance of the model and the controller. Based on aspirating 150 cells, the model-based adaptive control method was able to complete the positioning of a cell inside a micropipette within 6 seconds with a positioning accuracy of ±3 pixels and a success rate higher than 94%.

Modeling and Control of Irrigation Systems: A Short Survey and Future Directions

Modeling and control of irrigation systems has importance for the yield of the crops and especially, saving the water sources has prominent importance for the world. In this paper, recent modeling and control methods for the irrigation systems are surveyed for the future applications. Even though a mathematical model is constructed for the amount of water in soil, there are too many unpredictable disturbances on the irrigation systems so that the mathematical model cannot be exact. Therefore, the accuracy of the literature control methods is less than expected results. As a solution, the unknown model-based control methods and also accurate prediction methods are going to be promising approaches for irrigation systems in future.

On Model-Free Adaptive Control and Its Stability Analysis

In this paper, the main issues of model-based control methods are first reviewed, followed by the motivations and the state of the art of the model-free adaptive control (MFAC). MFAC is a novel data-driven control method for a class of unknown nonaffine nonlinear discrete-time systems since neither explicit physical model nor Lyapunov stability theory or key technical lemma is used in the controller design and theoretical analysis except only for the input/output (I/O) measurement data. The basis of MFAC is the dynamic linearization data modeling method at each operating point of the closed-loop system. The established dynamic linearization data model is a virtual equivalent data relationship in the I/O sense to the original nonlinear plant by means of a novel concept called pseudo-partial derivative (PPD) or pseudo-gradient (PG) vector. Based on this virtual equivalent dynamic linearization data model and the time-varying PPD or PG estimation algorithm designed merely using the I/O measurements of a controlled plant, the MFAC system is constructed. The main contribution of this paper is that the theoretical analysis of the bounded-input bounded-output stability, the monotonic convergence of the tracking error dynamics, and the internal stability of the full form dynamic linearization based MFAC scheme are rigorously presented by the contraction mapping principle; the well known PID control and the traditional adaptive control for linear time-invariant systems are explicitly shown as the special cases of this MFAC. The simulation results verify the effectiveness of the proposed approach.

A Novel Model-Based Robust Control for Position Tracking of Permanent Magnet Linear Motor

The main objective of this article is to address the position tracking control problem for permanent magnet linear motor by using a novel model-based robust control method. Specifically, based on the assumed upper bound of the lumped model uncertainties and external disturbances, we design a novel nonlinear control algorithm, which is characterized by error-based and model-based inheriting from traditional proportional-integral-derivative (PID) control and robust control. The proposed control can be considered as an improved PID control or a redesigned robust control, with simple implementation and practical effectiveness. Theoretical analysis is provided to demonstrate that the controller can guarantee the uniform boundedness and uniform ultimate boundedness of the system. Moreover, on the experimental platform, rapid controller prototyping cSPACE is designed to avoid long time programming and debugging, and provides great convenience for practical operation. Numerical simulations and real-time experimental results are finally presented to illustrate the effectiveness and the achievable control performance of the control design.

A High Efficiency Model-Based Adaptive Dead-Time Control Method for GaN HEMTs Considering Nonlinear Junction Capacitors in Triangular Current Mode Operation

Gallium nitride (GaN) high-electron-mobility transistor (HEMT) has the advantages of high switching speed and low ON-resistance, which make it widely used in high-frequency applications to realize high power density. Triangular current mode (TCM) modulation has been used in GaN-based converters to simultaneously achieve high power density and high efficiency through full-range zero voltage switching (ZVS). However, as switching frequency increases, the effect of dead time becomes more significant. Meanwhile, because of the junction capacitors of GaN HEMTs, the optimum dead time changes greatly with the turn-off current. Inappropriate dead time will induce extra loss that goes against the further improvement of power density. In order to minimize the dead-time loss in GaN HEMT-based TCM converter, this article proposes a strategy of adaptive dead-time control. An accurate turn-off transient model is proposed to calculate optimal dead time with consideration of the nonlinear capacitors. Based on this model, a loss model is established to evaluate the effect of dead time involving the short circuit in dead time. A new method of drive signal generation in TCM is proposed to increase the accuracy of dead-time control and reduce the current ripple. By reusing the detection signal in TCM, the adaptive dead-time control is realized without extra sensors. The proposed model is verified by a dual-pulse test, which shows the maximum error between the calculated and the experimental turn-off time is below 3 ns. The proposed adaptive dead-time control is implemented in a GaN HEMT-based boost converter. The experimental results show that the adaptive dead-time control can improve the efficiency over all operation conditions compared with using the fixed dead time, and the total loss is reduced by 26.7% at 800-W load and 70.8% at 50-W load.

Lattice hydrodynamic model-based feedback control method with traffic interruption probability

Connected vehicles are expected to become commercially available by the next decade, while traffic interruption is not uncommon in the real traffic environment. In this paper, we propose a feedback control method for lattice hydrodynamic model considering the traffic interruption probability effect. The stability criterion of the new model is explored through linear stability analysis of transfer function. When the stability conditions are not satisfied, a delay feedback controller is used to control the discharging flow to suppress traffic congestion. The impact of gain coefficient and delay time on the performance is discussed. We verify the effectiveness of the devised delay feedback controller by simulations. Results show that the traffic interruption probability effect has a considerable impact on the stability of traffic flow, while the controller is effective in suppressing traffic congestion.

A More Realistic Replica of a Walking Biped for Implying Dynamics and Control on Ascending and Descending Stairs by Considering Self-impact Joint Constraint

The aim of this study was to bring forth a model-based control method for trajectory tracking of a normal human-like biped on descending and ascending stairs using the straight knee state and to verify the controller performance in the straight knee state periods. Contemplating a straight state in knee joints does not consent to each leg’s shank link to undertake angular rotations greater than thigh link’s rotations. For this purpose, among suggested methods, the self-impact joint constraint has been recommended for energy-efficient (normal) bipedal walking with the implementation of straight knee constraint. To reach this intention, dynamical motion equations are derived, developed and modified to comprise self-impact joint constraint at the knee joint. For gaining control over this complex dynamical system, accessible anthropometric stair gait cycle data are used to derive desired trajectories of thigh and shank links of self-impact biped. Furthermore, for taking the stair ascent and descent of the biped under control, a nonlinear intelligent controller with adaptive neural network control method was put forward. In proportion to simulation results on stair ascending and descending of biped robot, the biped could track the desired angular positions and velocities of the links very well despite having complex nonlinear terms in its governing dynamical equations due to the presence of self-impact constraints. Moreover, during the activation period of joint self-impact, an error of link position angles was evidently improved in comparison to unconstrained biped.

A Decentralized Model Identification Scheme by Random-walk RLS Process for Robot Manipulators: Experimental Studies

In this paper, a parameter identification method by randomly excited trajectories for decentralized joints of robot manipulators is presented. Each joint of a robot manipulator is decoupled and identified as a second order linear equation by a recursive least square method. Although robot manipulators are a nonlinear and coupled system, decentralized models are required for either the independent joint control such as model-based linear control methods, a time-delayed control (TDC) method or the input torque estimation. The random walk-based parameter identification scheme of using a recursive least square (RLS) method is applied to a mobile manipulator, KOBOKER as a test-bed. Then the identified models are used for designing a state observer to estimate the states of KOBOKER more accurately when the robot follows the sinusoidal trajectory. The accuracies of the identified model and the estimated state are verified experimentally by comparing with the torque of a linearized motion equation.

Predictive Control of Flexible Resources for Demand Response in Active Distribution Networks

In this paper, a model-based predictive control method is proposed for utilization of flexible resources such as battery energy storage systems and heating systems effectively to provide demand response in low-voltage distribution networks with solar photovoltaic. The contributions of this paper are twofold. First, a linear power flow method based on relaxation of branch power losses applicable to radial distribution networks is proposed and formulated. Second, a flexible resources controller that solves a multi-objective linear optimization problem in receding-horizon fashion is formulated taking into account system states, forecasts of generation, and loads. Using the proposed control algorithm, flexibility from network resources can be utilized for low-voltage network management with assurance of quality of service to the customers. Simulations are conducted for summer and winter cases on a simplified Danish low-voltage network using Matlab/Simulink to study the performance of the proposed control method. Compared to the methods in state of the art, the proposed linear power flow method is proven to be accurate for the calculation of network power flows. Simulation results also show that proposed flexible resources controller can meet the network control objectives while satisfying the network constraints and operation limits of the flexible resources.

Data-Driven Model Free Adaptive Perimeter Control for Multi-Region Urban Traffic Networks With Route Choice

Recent studies have shown that a homogenous urban road network exists a well-defined macroscopic fundamental diagram (MFD), which can be used for perimeter control conveniently. Most of the existing perimeter control strategies are model-based control methods, leading to the result that the control effect may be not good enough if the traffic model is not accurate. In this paper, a novel data-driven strategy called model free adaptive control (MFAC) method is proposed for multi-region perimeter control in order to get rid of the dependency of the aforementioned model-based MFD-based perimeter control methods. Due to the fact that the multi-region urban traffic system (MRUTS) is a complex interconnected system, a decentralized estimation and decentralized MFAC (DED-MFAC) method is utilized to deal with the strong-coupled characteristic of the traffic system. In this framework, MFD is used to determine the desired accumulations in each region and generate the throughput data of the urban traffic system, since the acquisition of trip completion flow is more difficult than accumulations. In addition, route choice is also integrated in the proposed policy to further improve the performance of the urban traffic system. A key advantage of the proposed approach is that it can only use the traffic data instead of the traffic model for real-time perimeter control. The effectiveness of the proposed perimeter control scheme is tested in simulation for a multi-region system, and the results show that it is superior to some other commonly used perimeter control methods.

Model-based control for exoskeletons with series elastic actuators evaluated on sit-to-stand movements

BackgroundCurrently, control of exoskeletons in rehabilitation focuses on imposing desired trajectories to promote relearning of motions. Furthermore, assistance is often provided by imposing these desired trajectories using impedance controllers. However, lower-limb exoskeletons are also a promising solution for mobility problems of individuals in daily life. To develop an assistive exoskeleton which allows the user to be autonomous, i.e. in control of his motions, remains a challenge. This paper presents a model-based control method to tackle this challenge.MethodsThe model-based control method utilizes a dynamic model of the exoskeleton to compensate for its own dynamics. After this compensation of the exoskeleton dynamics, the exoskeleton can provide a desired assistance to the user. While dynamic models of exoskeletons used in the literature focus on gravity compensation only, the need for modelling and monitoring of the ground contact impedes their widespread use. The control strategy proposed here relies on modelling of the full exoskeleton dynamics and of the contacts with the environment. A modelling strategy and general control scheme are introduced.ResultsValidation of the control method on 15 non-disabled adults performing sit-to-stand motions shows that muscle effort and joint torques are similar in the conditions with dynamically compensated exoskeleton and without exoskeleton. The condition with exoskeleton in which the compensating controller was not active showed a significant increase in human joint torques and muscle effort at the knee and hip. Motor saturation occurred during the assisted condition, which limited the assistance the exoskeleton could deliver.ConclusionsThis work presents the modelling steps and controller design to compensate the exoskeleton dynamics. The validation seems to indicate that the presented model-based controller is able to compensate the exoskeleton.

Machine Direction Adaptive Control on a Paper Machine

Control of industrial sheet and film processes involves separate controllers and actuators for minimizing both temporal variations along the machine direction (MD) and spatial variations along the cross direction (CD). Model-based control methods such as model predictive control (MPC) have gained widespread implementation for controlling both the MD and CD processes. One limitation of model-based methods is that changes in the true process pose significant identification challenges for operators which are often resolved with costly open loop identification experiments. The predominant industrial measurement technology acquires a signal of mixed MD and CD variations that requires separation. This work compares various model-free approaches for MD-CD separation as a prerequisite to effective MD control. To address the challenges of model-based control, this paper introduces an adaptive control method for the MD process. Closed loop identification experiments are conducted and compared to benchmarks on an industrial paper machine simulator.

Preactuated Multirate Feedforward Control for Independent Stable Inversion of Unstable Intrinsic and Discretization Zeros

A plant with unstable zeros is known to be difficult to be controlled, because of the initial undershoot of its step response and the unstable poles in its inversion system. There are two reasons why a plant has unstable zeros in the discrete-time domain: 1) noncollocation of actuators and sensors; and 2) discretization by the zero-order hold. Addressing problems 1) and 2) simultaneously, approximate and discrete-time stable inversion methods have been proposed as model-based feedforward control for unstable zeros. However, the approximated inversion methods compromise tracking performance, and the discrete-time stable inversion methods obtain oscillatory control inputs owing to the direct cancellation of discretization zeros. Therefore, this paper proposes a preactuated multirate feedforward control, which is an independent stable inversion of the two types of zeros: unstable intrinsic zeros from noncollocation and unstable discretization zeros from the zero-order hold. The proposed method combines both a state trajectory generation based on a time- and imaginary-axis reversal to address problem 1) and a multirate feedforward implementation to address problem 2), independently. Moreover, this study verifies the tracking performance improvement by performing simulations and experiments in comparison with well-known model-based feedforward control methods using a high-precision motion stage. The role and importance of multirate feedforward control are also demonstrated by comparing it to a continuous-time domain approach with preactuation.

Mobility control of unmanned aerial vehicle as communication relay in airborne multi-user systems

In this paper, a model-based adaptive mobility control method for an Unmanned Aerial Vehicle (UAV) acting as a communication relay is presented, which is intended to improve the network performance in airborne multi-user systems. The mobility control problem is addressed by jointly considering unknown Radio Frequency (RF) channel parameters, unknown multi-user mobility, and non-available Angle of Arrival (AoA) information of the received signal. A Kalman filter and a least-square-based estimation algorithm are used to predict the future user positions and estimate the RF channel parameters between the users and the UAV, respectively. Two different relay application cases are considered: end-to-end and multi-user communications. A line search algorithm is proposed for the former, with its stability given and proven, whereas a simplified gradient-based algorithm is proposed for the latter to provide a target relay position at each decision time step, decreasing the two-dimensional search to a one-dimensional search. Simulation results show that the proposed mobility control algorithms can drive the UAV to reach or track the optimal relay position movement, as well as improving network performance. The proposed method reflects the properties of using different metrics as objective network performance functions.

Economy-oriented Control Strategy for Dual-motor Four-wheel-drive Electric Vehicle in a Car-following Maneuver

This paper presents an online economy-oriented controller to improve energy consumption of a two-axel dual-motor 4WD electric bus (E-bus) in a car-following maneuver. The nonlinear model-based predictive control (NMPC) method is applied in this controller to realize a deep fusion of adaptive cruise control (ACC) and torque distribution strategy (TDS). Some search skills are adopted in receding horizon optimization to reduce computing burden. The proposed method reduces energy consumption compared with ACC+TDS method on the condition of ensuring tracking capability.

Model-based control of continuous emulsion co-polymerization in a lab-scale tubular reactor

Model-based control methods like nonlinear model predictive control (NMPC) is a promising approach to control complex chemical processes. While there are many publications on theoretical aspects and simulation studies, the experimental evaluation of NMPC is limited. We present the implementation and experimental validation of model-based control for continuous emulsion co-polymerization in a tubular reactor. We compare two schemes to control the total monomer conversion and the polymer composition at the reactor outlet. The two control schemes are (i) an NMPC scheme and (ii) a heuristic control approach, which consists of PI control, ratio control and feed forward control. The process model consists of a set of differential algebraic equations which capture the polymerization kinetics as well as the dynamics of the auxiliary equipment in the setup. We evaluate the performance of the NMPC scheme in silico for different constraints on the maximum reactor temperature. The simulation studies show that the NMPC maintains the temperature constraints. However, decreasing the maximum allowable temperature in the reactor results in a deviation of the total monomer conversion and polymer composition from the desired specifications. We also experimentally investigate the performance of both control schemes. As expected, the experimental studies show that both control schemes are able to reduce the variation of the polymer composition compared to the case without supervisory control. Although a deviation of the controlled variables from their setpoint values is observed in both control schemes, this deviation from setpoint is less with the NMPC scheme than with the heuristic control scheme.

On-Line Gait Adjustment for Humanoid Robot Robust Walking Based on Divergence Component of Motion

As the first step for biped robots to enter the human life, robust walking is a difficult problem to be solved owing to the various algorithms and practical engineering issues being involved. This paper studies the robust walking problem for humanoid robots by using the divergence component of motion (DCM) method based on linear inverted pendulum model. Firstly, we implement a DCM trajectory planning method to simplify the planning process. It calculates the DCM trajectories under the requirements of walking speed and initial state of the system. Then, a DCM feedback controller with anti-disturbance ability is proposed to realize the tracking control of the planned trajectory. Finally, the optimization method and DCM feedback control are integrated into a hybrid optimization controller, which takes into account the step adjustment and the step duration adjustment of the robot. Simulation results demonstrates that the technique can act naturally stable inside an enormous scope of effect unsettling influences, the maximum recoverable impact of a humanoid robots with a mass of 70Kg can reach 85Ns, which is much better than the 20Ns of the existing model-based prediction control method.