Showing papers in "Control Engineering Practice in 2016"
TL;DR: Based on the common Lyapunov functional approach and algebraic Riccati equation technique, an approach to design the formation protocol is presented in this paper, where an explicit expression of the formation reference function is derived to describe the macroscopic movement of the whole UAV formation.
Abstract: Time-varying formation control problems for unmanned aerial vehicle (UAV) swarm systems with switching interaction topologies are studied. Necessary and sufficient conditions for UAV swarm systems with switching interaction topologies to achieve predefined time-varying formations are proposed. Based on the common Lyapunov functional approach and algebraic Riccati equation technique, an approach to design the formation protocol is presented. An explicit expression of the formation reference function is derived to describe the macroscopic movement of the whole UAV formation. A quadrotor formation platform consisting of four quadrotors is introduced. Outdoor experiments are performed to demonstrate the effectiveness of the theoretical results.
233 citations
TL;DR: In this paper, canonical correlation analysis (CCA)-based fault detection methods are proposed for both static and dynamic processes, which are applied to an alumina evaporation process, and the achieved results show that both methods are applicable for fault detection.
Abstract: In this paper, canonical correlation analysis (CCA)-based fault detection methods are proposed for both static and dynamic processes. Different from the well-established process monitoring and fault diagnosis systems based on multivariate analysis techniques like principal component analysis and partial least squares, the core of the proposed methods is to build residual signals by means of the CCA technique for the fault detection purpose. The proposed methods are applied to an alumina evaporation process, and the achieved results show that both methods are applicable for fault detection, while the dynamic one delivers better detection performance.
110 citations
TL;DR: In this article, a semi-supervised data-driven approach is employed for fault detection and isolation that makes no use of a priori knowledge about abnormal phenomena for HVAC installations.
Abstract: Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller systems can lead to discomfort for the users, energy wastage, system unreliability and shorter equipment life Faults need to be early diagnosed to prevent further deterioration of the system behaviour and energy losses Since it is not a common practice to collect historical data regarding unforeseen phenomena and abnormal behaviours for HVAC installations, in this paper, a semi-supervised data-driven approach is employed for fault detection and isolation that makes no use of a priori knowledge about abnormal phenomena The proposed method exploits Principal Component Analysis (PCA) to distinguish anomalies from normal operation variability and a reconstruction-based contribution approach to isolate variables related to faults The diagnosis task is then tackled by means of a decision table that associates the influence of faults to certain characteristic features The Fault Detection and Diagnosis (FDD) algorithm performance is assessed by exploiting experimental datasets from two types of water chiller systems
102 citations
TL;DR: The results reveal that conventional trapezoidal stimulation intensity profiles may produce a safe foot lift, but often at the cost of too high intensities and an unphysiological foot pitch motion.
Abstract: Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift the foot and leads to a pathological gait. We consider treatment of this syndrome via Functional Electrical Stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. We highlight the role of feedback control for addressing the challenges that result from the large individuality and time-variance of muscle response dynamics. Unlike many previous approaches, we do not reduce the control problem to the scalar case. Instead, the entire pitch angle trajectory of the paretic foot is measured by means of a 6D Inertial Measurement Unit (IMU) and controlled by an Iterative Learning Control (ILC) scheme for variable-pass-length systems. While previously suggested controllers were often validated for the strongly simplified case of sitting or lying subjects, we demonstrate the effectiveness of the proposed approach in experimental trials with walking drop foot patients. Our results reveal that conventional trapezoidal stimulation intensity profiles may produce a safe foot lift, but often at the cost of too high intensities and an unphysiological foot pitch motion. Starting from such conservative intensity profiles, the proposed learning controller automatically achieves a desired foot motion within one or two strides and keeps adjusting the stimulation to compensate time-variant muscle dynamics and disturbances.
84 citations
TL;DR: In this article, a nonlinear pitch angle controller (N-PI) is proposed to reduce the power captured above the rated wind speed and release the mechanical stress of the drive train.
Abstract: Wind turbine uses a pitch angle controller to reduce the power captured above the rated wind speed and release the mechanical stress of the drive train. This paper investigates a nonlinear PI (N-PI) based pitch angle controller, by designing an extended-order state and perturbation observer to estimate and compensate unknown time-varying nonlinearities and disturbances. The proposed N-PI does not require the accurate model and uses only one set of PI parameters to provide a global optimal performance under wind speed changes. Simulation verification is based on a simplified two-mass wind turbine model and a detailed aero-elastic wind turbine simulator (FAST), respectively. Simulation results show that the N-PI controller can provide better dynamic performances of power regulation, load stress reduction and actuator usage, comparing with the conventional PI and gain-scheduled PI controller, and better robustness against of model uncertainties than feedback linearization control.
76 citations
TL;DR: In this paper, an adaptive observer based on sliding mode method is used to estimate the state of charge (SOC) and state of health (SOH) of the Li-ion battery.
Abstract: As the demand for electric vehicle (EV)'s remaining operation range and power supply life, Lithium-ion (Li-ion) battery state of charge (SOC) and state of health (SOH) estimation are important in battery management system (BMS). In this paper, a proposed adaptive observer based on sliding mode method is used to estimate SOC and SOH of the Li-ion battery. An equivalent circuit model with two resistor and capacitor (RC) networks is established, and the model equations in specific structure with uncertainties are given and analyzed. The proposed adaptive sliding mode observer is applied to estimate SOC and SOH based on the established battery model with uncertainties, and it can avoid the chattering effects and improve the estimation performance. The experiment and simulation estimation results show that the proposed adaptive sliding mode observer has good performance and robustness on battery SOC and SOH estimation.
70 citations
TL;DR: In this article, an iterative learning control law design for plants modeled by discrete linear dynamics using repetitive process stability theory is proposed. And the results from an experimental implementation are given, where the performance requirements include specifications over various finite frequency ranges.
Abstract: This paper considers iterative learning control law design for plants modeled by discrete linear dynamics using repetitive process stability theory. The resulting one step linear matrix inequality based design produces a stabilizing feedback controller in the time domain and a feedforward controller that guarantees convergence in the trial-to-trial domain. Additionally, application of the generalized Kalman–Yakubovich–Popov (KYP) lemma allows a direct treatment of differing finite frequency range performance specifications. The results are also extended to plants with relative degree greater than unity. To support the algorithm development, the results from an experimental implementation are given, where the performance requirements include specifications over various finite frequency ranges.
69 citations
TL;DR: In this paper, an aircraft trajectory controller, which uses the Incremental Nonlinear Dynamic Inversion, is proposed to achieve fault-tolerant trajectory control in the presence of model uncertainties and actuator faults.
Abstract: This paper deals with aircraft trajectory control in the presence of model uncertainties and actuator faults. Existing approaches, such as adaptive backstepping and nonlinear dynamic inversion with online model identi_cation, can be applied. However, since there are a number of unknown aerodynamic derivatives, the tuning of parameter update law gains is time-consuming. Methods with online model identi_cation require excitation and the selection of a threshold. Furthermore, to deal with highly nonlinear aircraft dynamics, the aerodynamic model structure needs to be designed. In this paper, a novel aircraft trajectory controller, which uses the Incremental Nonlinear Dynamic Inversion, is proposed to achieve fault-tolerant trajectory control. The detailed control law design of four loops is presented. The idea is to design the loops with uncertainties using the incremental approach. The tuning of the approach is straightforward and there is no requirement for excitation and selection of a threshold. The performance of the proposed controller is compared with existing approaches using three scenarios. The results show that the proposed trajectory controller can follow the reference even when there are model uncertainties and actuator faults. Keywords: Trajectory control, Fault-Tolerant Control, nonlinear ight control, Incremental Nonlinear Dynamic Inversion, model identi_cation
64 citations
TL;DR: In this article, an integrated vehicle and wheel stability control is developed and experimentally evaluated, which can be applied to a wide variety of vehicle driveline and actuation configurations such as: four, front and rear wheel drive systems.
Abstract: In this paper, an integrated vehicle and wheel stability control is developed and experimentally evaluated. The integrated structure provides a more accurate solution as the output of the stability controller is not altered by a separate unit, therefore its optimality is not compromised. Model predictive control is used to find the optimal control actions. The proposed control scheme can be applied to a wide variety of vehicle driveline and actuation configurations such as: four, front and rear wheel drive systems. Computer simulations as well as experiments are provided to show the effectiveness of the proposed control algorithm.
62 citations
TL;DR: In this article, the authors proposed a microgrid structure which consists of a detailed photovoltaic (PV) array model, a solid oxide fuel cell (SOFC) and various loads.
Abstract: Control strategies of distributed generation (DG) are investigated for different combination of DG and storage units in a microgrid. In this paper the authors proposed a microgrid structure which consists of a detailed photovoltaic (PV) array model, a solid oxide fuel cell (SOFC) and various loads. Real and reactive power (PQ) control and droop control are developed for microgrid operation. In grid-connected mode, PQ control is developed by controlling the active and reactive power output of DGs in accordance with assigned references. Two PI controllers were used in the PQ controller, and a novel heuristic method, artificial bee colony (ABC), was adopted to tune the PI parameters. DGs can be controlled by droop control both under grid-connected and islanded modes. Droop control implements power reallocation between DGs based on predefined droop characteristics whenever load changes or the microgrid is connected/disconnected to the grid, while the microgrid voltage and frequency is maintained at appropriate levels. Through voltage, frequency, and power characteristics in the simulation under different scenarios, the proposed control strategies have demonstrated to work properly and effectively. The simulation results also show the effectiveness of tuning PI parameters by the ABC.
60 citations
TL;DR: In this paper, a new method for leak localization in water distribution networks (WDNs) is proposed, where residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model, and a classifier is applied to the residuals with the aim of determining the leak location.
Abstract: This paper proposes a new method for leak localization in water distribution networks (WDNs). In a first stage, residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model. In a second stage, a classifier is applied to the residuals with the aim of determining the leak location. The classifier is trained with data generated by simulation of the WDN under different leak scenarios and uncertainty conditions. The proposed method is tested both by using synthetic and experimental data with real WDNs of different sizes. The comparison with the current existing approaches shows a performance improvement.
TL;DR: In this paper, the FOPI controller is designed using the frequency domain approach to maintain a constant level in the first tank while making the level of the second tank to follow a sinusoidal and square wave reference signal.
Abstract: Often in a coupled two tank MIMO system, the level of the first tank is required to be kept at a constant level while the level of the second tank is required to follow a time varying reference signal. Sometimes controllers of the PID family along with conventional feedforward controllers may not be able to maintain the constant level in the first tank rejecting the disturbance due to the change in the level of the second tank without deteriorating the tracking performance of the level of the second tank. This paper shows analytically as well as experimentally that Fractional Order Proportional Integral (FOPI) controllers along with conventional feedforward controllers work better than PI/PID/2DOF-PI/3DOF-PI with feedforward controllers in such situation. FOPI controller is designed using the frequency domain approach. Effectiveness of the controllers is tested to maintain a constant level in the first tank while making the level of the second tank to follow a sinusoidal and square wave reference signals. Experimental results validate the objective of the paper.
TL;DR: In this article, a two-state thermal model describing the dynamics of the surface and the core temperature of a battery cell is proposed to diagnose thermal faults in Li-ion batteries.
Abstract: Ensuring safety and reliability is a critical objective of advanced Battery Management Systems (BMSs) for Li-ion batteries. In order to achieve this objective, advanced BMS must implement diagnostic algorithms that are capable of diagnosing several battery faults. One set of such critical faults in Li-ion batteries are thermal faults which can be potentially catastrophic. In this paper, a diagnostic algorithm is presented that diagnoses thermal faults in Lithium-ion batteries. The algorithm is based on a two-state thermal model describing the dynamics of the surface and the core temperature of a battery cell. The residual signals for fault detection are generated by nonlinear observers with measured surface temperature and a reconstructed core temperature feedback. Furthermore, an adaptive threshold generator is designed to suppress the effect of modelling uncertainties. The residuals are then compared with these adaptive thresholds to evaluate the occurrence of faults. Simulation and experimental studies are presented to illustrate the effectiveness of the proposed scheme.
TL;DR: A novel weighted adaptive recursive fault detection technique based on Principal Component Analysis (PCA) is proposed to address the issue of the increment in false alarm rate in process monitoring schemes due to the natural, slow and normal process changes (aging), which often occurs in real processes.
Abstract: A novel weighted adaptive recursive fault detection technique based on Principal Component Analysis (PCA) is proposed to address the issue of the increment in false alarm rate in process monitoring schemes due to the natural, slow and normal process changes (aging), which often occurs in real processes. It has been named as weighted adaptive recursive PCA (WARP). The aforementioned problem is addressed recursively by updating the eigenstructure (eigenvalues and eigenvectors) of the statistical detection model when the false alarm rate increases given the awareness of non-faulty condition. The update is carried out by incorporating the new available information within a specific online process dataset, instead of keeping a fixed statistical model such as conventional PCA does. To achieve this recursive updating, equations for means, standard deviations, covariance matrix, eigenvalues and eigenvectors are developed. The statistical thresholds and the number of principal components are updated as well. A comparison between the proposed algorithm and other recursive PCA-based algorithms is carried out in terms of false alarm rate, misdetection rate, detection delay and its computational complexity. WARP features a significant reduction of the computational complexity while maintaining a similar performance on false alarm rate, misdetection rate and detection delay compared to that of the other existing PCA-based recursive algorithms. The computational complexity is assessed in terms of the Floating Operation Points (FLOPs) needed to carry out the update.
TL;DR: Numerical experiments and an industrial case are given to show that the proposed procedure for the optimal design of industrial alarm systems based on evidence theory has a better performance than the classical design methods.
Abstract: This paper presents a procedure for the optimal design of industrial alarm systems based on evidence theory to deal with epistemic and aleatory uncertainties of the monitored process variable. First, the upper and lower fuzzy thresholds are designed, and then the sampled value of the process variable is transformed into a piece of alarm evidence to measure the degrees of uncertainty about whether an alarm should be triggered or not by the sampled value. Second, a linear updating rule of evidence is recursively applied to combine the updated alarm evidence at t −1 step with the incoming alarm evidence at t step to generate the updated alarm evidence at t step. In the process of evidence updating, the weights of evidence for linear combination can be obtained by dynamically minimizing the distance between the updated alarm evidence and the true mode (i.e., “alarm” or “no-alarm”). An alarm decision can then be made according to a pignistic probability transformed from the updated alarm evidence at each time step. Finally, numerical experiments and an industrial case are given to show that the proposed procedure has a better performance than the classical design methods.
TL;DR: A static convex hull-based PCA algorithm (SCHPCA) is proposed which replaces the traditional T2-based abnormality detection logic with the convex Hull-based abnormalities detection logic, and its moving window version, called the moving window conveX hull- based PCA algorithms (MWCH PCA) are proposed, respectively.
Abstract: Incidents happening in the blast furnace will strongly affect the stability and smoothness of the iron-making process. Thus far, diagnosis of abnormalities in furnaces still mainly relies on the personal experiences of individual workers in many iron works. In this paper, principal component analysis (PCA)-based algorithms are developed to monitor the iron-making process and achieve early abnormality detection. Because the process exhibits a non-normal distribution and a time-varying nature in the measurement data, a static convex hull-based PCA algorithm (SCHPCA) which replaces the traditional T2-based abnormality detection logic with the convex hull-based abnormality detection logic, and its moving window version, called the moving window convex hull-based PCA algorithm (MWCHPCA) are proposed, respectively. These two algorithms are tested on the real process data to verify their effectiveness in the early abnormality detection of iron-making process.
TL;DR: In this article, a model-based approach to detect and isolate non-concurrent multiple leaks in a pipeline is proposed, only using pressure and flow sensors placed at the pipeline ends, which relies on a nonlinear modeling derived from Water-Hammer equations, and related Extended Kalman Filters used to estimate leak coefficients.
Abstract: A model-based approach to detect and isolate non-concurrent multiple leaks in a pipeline is proposed, only using pressure and flow sensors placed at the pipeline ends. The approach relies on a nonlinear modeling derived from Water–Hammer equations, and related Extended Kalman Filters used to estimate leak coefficients. This extends former results developed for the single leak case, but with the difficulty that the model is modified at each new leak occurrence. A model adaptation strategy is thus proposed, allowing us to monitor indeed each new leak, and no matter where it appears. Experimental results illustrate the performance of the proposed algorithm.
TL;DR: In this paper, an asymmetric relay autotuning with features such as a startup procedure and adaptive relay amplitudes is proposed for industrial air handling units, which is implemented and tested on an industrial air-handling unit.
Abstract: The relay autotuner provides a simple way of finding PID controller parameters. Even though relay autotuning is much investigated in the literature, the practical aspects are not that well-documented. In this paper an asymmetric relay autotuner with features such as a startup procedure and adaptive relay amplitudes is proposed. Parameter choices and handling of noise, disturbances, start in non-steady state and other possible error sources are discussed. The autotuner is implemented and tested on an industrial air handling unit to show its use in practice. The experiments show good results, and prove that the proposed simple autotuner is well-suited for industrial use. But the experiments also enlighten possible error sources and remaining problems.
TL;DR: In this paper, a cooperative adaptive cruise controller that controls vehicles along a planned route in a possibly hilly terrain, while keeping safe distances among the vehicles, is presented, which consists of two predictive layers that may operate with different update frequencies, horizon lengths and model abstractions.
Abstract: This paper presents a cooperative adaptive cruise controller that controls vehicles along a planned route in a possibly hilly terrain, while keeping safe distances among the vehicles. The controller consists of two predictive layers that may operate with different update frequencies, horizon lengths and model abstractions. The top layer plans kinetic energy in a centralized manner by solving a quadratic program, whereas the bottom layer optimizes gear in a decentralized manner by solving a dynamic program. The efficiency of the proposed controller is shown through several case studies with different horizon lengths and number of vehicles in the platoon.
TL;DR: Experimental results verify the benefits of ILC of its wide control bandwidth, enabling a faster, more linear, and more accurate scanning without a phase lag and a gain mismatch.
Abstract: Iterative learning control (ILC) for a galvanometer scanner is proposed to achieve high speed, linear, and accurate bidirectional scanning for scanning laser microscopy. A galvanometer scanner, as a low stiffness actuator, is first stabilized with a feedback control compensating for disturbances and nonlinearities at low frequencies, and ILC is applied for the control of the fast scanning motion. For stable inversion of the non-minimum phase zeros, a time delay approximation and a zero phase approximation are used for design of ILC, and their attainable bandwidths are analyzed. Experimental results verify the benefits of ILC of its wide control bandwidth, enabling a faster, more linear, and more accurate scanning without a phase lag and a gain mismatch. At the scan rate of 4112 lines per second, the root mean square (RMS) error of the ILC can be reduced by a factor of 73 in comparison with the feedback controlled galvanometer scanner of the commercial system.
TL;DR: In this paper, a robust distributed model predictive control (RDMPC) based on linear matrix inequalities is proposed to solve a series of local convex optimization problems to minimize an attractive range for a robust performance objective by using a time-varying statefeedback controller for each control area.
Abstract: Reliable Load frequency control (LFC) is crucial to the operation and design of modern electric power systems. However, the power systems are always subject to uncertainties and external disturbances. Considering the LFC problem of a multi-area interconnected power system, this paper presents a robust distributed model predictive control (RDMPC) based on linear matrix inequalities. The proposed algorithm solves a series of local convex optimization problems to minimize an attractive range for a robust performance objective by using a time-varying state-feedback controller for each control area. The scheme incorporates the two critical nonlinear constraints, e.g., the generation rate constraint (GRC) and the valve limit, into convex optimization problems. Furthermore, the algorithm explores the use of an expanded group of adjustable parameters in LMI to transform an upper bound into an attractive range for reducing conservativeness. Good performance and robustness are obtained in the presence of power system dynamic uncertainties.
TL;DR: In this paper, the authors present the application of control strategies for wastewater treatment plants with the goal of effluent limits violations removal as well as achieving a simultaneous improvement in effluent quality and reduction of operational costs.
Abstract: This paper presents the application of control strategies for wastewater treatment plants with the goal of effluent limits violations removal as well as achieving a simultaneous improvement of effluent quality and reduction of operational costs. The evaluation is carried out with the Benchmark Simulation Model No. 2. The automatic selection of the suitable control strategy is based on risk detection of effluent violations by Artificial Neural Networks. Fuzzy Controllers are implemented to improve the denitrification or nitrification process based on the proposed objectives. Model Predictive Control is applied for the improvement of dissolved oxygen tracking.
TL;DR: In this paper, a model-based fault diagnosis scheme was proposed to detect and isolate the current, voltage and temperature sensor fault, which relies on the sequential residual generation using structural analysis theory and statistical inference residual evaluation.
Abstract: The battery sensors fault diagnosis is of great importance to guarantee the battery performance, safety and life as the operations of battery management system (BMS) mainly depend on the embedded current, voltage and temperature sensor measurements. This paper presents a systematic model-based fault diagnosis scheme to detect and isolate the current, voltage and temperature sensor fault. The proposed scheme relies on the sequential residual generation using structural analysis theory and statistical inference residual evaluation. Structural analysis handles the pre-analysis of sensor fault detectability and isolability possibilities without the accurate knowledge of battery parameters, which is useful in the early design stages of diagnostic system. It also helps to find the analytical redundancy part of the battery model, from which subsets of equations are extracted and selected to construct diagnostic tests. With the help of state observes and other advanced techniques, these tests are ensured to be efficient by taking care of the inaccurate initial State-of-Charge (SoC) and derivation of variables. The residuals generated from diagnostic tests are further evaluated by a statistical inference method to make a reliable diagnostic decision. Finally, the proposed diagnostic scheme is experimentally validated and some experimental results are presented.
TL;DR: In this article, the authors provide a tutorial view on airship path-tracking under wind disturbances, including the relevant aspects towards this objective, namely the airship modelling, the dynamics analysis over the flight envelope, and the step-by-step design of a gainscheduling control.
Abstract: This paper provides a tutorial view on airship path-tracking under wind disturbances. It addresses the relevant aspects towards this objective, namely the airship modelling, the dynamics analysis over the flight envelope, and the step-by-step design of a gain-scheduling control. The required parts to build a proper airship simulator are given: airship dynamics and actuation, and wind disturbances. A path-tracking gain-scheduling controller is designed and its performance and robustness evaluated in the simulation environment described for a complete airship mission consisting of vertical takeoff and landing, cruise flight and ground-hover, under realistic wind disturbances. Throughout the paper, considerations are done regarding the airship behavior and limitations, as well as what can be accomplished and how.
TL;DR: In this article, the power plant is divided into three transformation modules and, using conservation laws, a dynamic model is developed to describe each module within reasonable assumptions, numerous module equations are combined to yield a 6th-order nonlinear model.
Abstract: Direct energy balance (DEB) coordinated control scheme is widely used by field engineers in coal-fired power plants while attracting little attention in the academic community. This paper aims to derive a mathematical model that is suitable for DEB research. To balance the model’s fidelity and simplicity, the power plant is divided into three transformation modules and, using conservation laws, a dynamic model is developed to describe each module. Within reasonable assumptions, numerous module equations are combined to yield a 6th-order nonlinear model. Time constants of the model are identified based on Pareto optimization. Model accuracy is confirmed using field measurements from a 300 MW coal-fired power plant. Based on the linearized model, the merits of the DEB control structure are analyzed. It is confirmed that the DEB control is sufficient to fulfill the fundamental goals of power plant regulation. An illustration of performance improvement is given by introducing gain scheduling techniques to the DEB structure. The proposed model can provide groundwork for future development of advanced control algorithms under the DEB structure.
TL;DR: In this paper, a model predictive control (MPC) based motion planning controller for automated driving on a motorway using a vehicle traffic simulator is presented, where the desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon.
Abstract: This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a vehicle traffic simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the disturbance rejection of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an model predictive control (MPC) approach. The developed control algorithm has been successfully implemented on a vehicle electronic control unit (ECU). The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving.
TL;DR: In this paper, the generalized isodamping technique was used to achieve the invariance of the control loop phase margin with respect to the plant operating point through a properly designed fractional-order proportional-integral-derivative controller.
Abstract: This paper deals with the fractional control of the temperature in solar furnace plants. As for all the concentrated solar plants, the solar furnace can be modeled as a nonlinear system, where the dynamics strongly depends on the operating temperature. However, to improve the effectiveness of this technology, the control system should guarantee an acceptable performance independently from the operating point. In order to overcome this problem, we propose to use the generalized isodamping technique, where we aim at achieving the invariance of the control loop phase margin with respect to the plant operating point through a properly designed fractional-order proportional-integral-derivative controller. A gain-scheduling algorithm is also introduced to cope with wide plant variations. Simulation and experimental results show the effectiveness of the proposed methodology.
TL;DR: An implementation of the sliding mode twisting controller on an electropneumatic plant for a tracking control problem and the analysis of the performance sustains the theoretical superiority of the implicitly discretized version.
Abstract: In this paper, we present an implementation of the sliding mode twisting controller on an electropneumatic plant for a
tracking control problem. To this end, implicitly and explicitly discretized twisting controllers are considered. We discuss
their structure, properties and implementations, as well as the experimental results. The analysis of the performance
sustains the theoretical superiority of the implicitly discretized version, as shown in previous works. The main advantages
of the implicit method are better tracking performance and drastic reduction in the input and output chattering. This is
achieved without modifying the structure of the controller compared to its continuous-time version. The tracking error
cannot be used as the sliding variable: it has a relative degree 3 w.r.t. the control input. The tuning of the sliding surface
has well as some other parameters in the control loop was instrumental in achieving good performance. We detail the
selection procedure of those parameters and their influence on the closed-loop behaviour. Finally we also present some
results with an implicitly discretized EBC-SMC controller.
TL;DR: In this paper, an approach for Inertial Measurement Unit (IMU) sensor fault reconstruction by exploiting a ground speed-based kinematic model of the aircraft flying in a rotating earth reference system is proposed.
Abstract: This paper proposes an approach for Inertial Measurement Unit sensor fault reconstruction by exploiting a ground speed-based kinematic model of the aircraft flying in a rotating earth reference system. Two strategies for the validation of sensor fault reconstruction are presented: closed-loop validation and open-loop validation. Both strategies use the same kinematic model and a newly-developed Adaptive Two-Stage Extended Kalman Filter to estimate the states and faults of the aircraft. Simulation results demonstrate the effectiveness of the proposed approach compared to an approach using an airspeed-based kinematic model. Furthermore, the major contribution is that the proposed approach is validated using real flight test data including the presence of external disturbances such as turbulence. Three flight scenarios are selected to test the performance of the proposed approach. It is shown that the proposed approach is robust to model uncertainties, unmodeled dynamics and disturbances such as time-varying wind and turbulence. Therefore, the proposed approach can be incorporated into aircraft Fault Detection and Isolation systems to enhance the performance of the aircraft.
TL;DR: In this paper, a robust predictive fault-tolerant strategy is developed that is applied to the advanced battery assembly system, which is tested against single as well as simultaneous faults concerning processing, transportation and mobile robots.
Abstract: The paper concerns fault-tolerant control of a real battery assembly system which is under a pilot implementation at RAFI GmbH Company (one of the leading electronic manufacturing service providers in Germany). The proposed framework is based on an interval analysis approach, which along with max-plus algebra, allows describing uncertain discrete event system such as the production one being considered in this paper. Having a mathematical system description, a model predictive control-based fault tolerant strategy is developed which can cope with both processing, transportation and mobile robot faults. In particular, it enables tolerating (up to some degree) the influence of these faults on the overall system performance. As a result, a novel robust predictive fault-tolerant strategy is developed that is applied to the advanced battery assembly system. The final part of the paper shows the implementation and experimental validation of the proposed strategy. The proposed approach is tested against single as well as simultaneous faults concerning processing, transportation and mobile robots.