Showing papers in "IEEE Transactions on Control Systems and Technology in 2015"
TL;DR: To achieve predefined time-varying formations, formation protocols are presented for UAV swarm systems first, where the velocities of UAVs can be different when achieving formations, and consensus-based approaches are applied to deal with the time-Varying formation control problems.
Abstract: Formation control analysis and design problems for unmanned aerial vehicle (UAV) swarm systems to achieve time-varying formations are investigated. To achieve predefined time-varying formations, formation protocols are presented for UAV swarm systems first, where the velocities of UAVs can be different when achieving formations. Then, consensus-based approaches are applied to deal with the time-varying formation control problems for UAV swarm systems. Necessary and sufficient conditions for UAV swarm systems to achieve time-varying formations are proposed. An explicit expression of the time-varying formation center function is derived. In addition, a procedure to design the protocol for UAV swarm systems to achieve time-varying formations is given. Finally, a quadrotor formation platform, which consists of five quadrotors is introduced. Theoretical results obtained in this brief are validated on the quardrotor formation platform, and outdoor experimental results are presented.
705 citations
TL;DR: A comprehensive comparative analysis of three velocity prediction strategies, applied within a model predictive control framework, and the prediction precision, computational cost, and resultant vehicular fuel economy are compared.
Abstract: The performance and practicality of predictive energy management in hybrid electric vehicles (HEVs) are highly dependent on the forecast of future vehicular velocities, both in terms of accuracy and computational efficiency. In this brief, we provide a comprehensive comparative analysis of three velocity prediction strategies, applied within a model predictive control framework. The prediction process is performed over each receding horizon, and the predicted velocities are utilized for fuel economy optimization of a power-split HEV. We assume that no telemetry or on-board sensor information is available for the controller, and the actual future driving profile is completely unknown. Basic principles of exponentially varying, stochastic Markov chain, and neural network-based velocity prediction approaches are described. Their sensitivity to tuning parameters is analyzed, and the prediction precision, computational cost, and resultant vehicular fuel economy are compared.
414 citations
TL;DR: A nonlinear adaptive path following controller that compensates for drift forces through vehicle sideslip that is motivated by a line-of-sight (LOS) guidance principle used by ancient navigators and intended for maneuvering in the horizontal-plane at given speeds.
Abstract: We present a nonlinear adaptive path following controller that compensates for drift forces through vehicle sideslip. Vehicle sideslip arises during path following when the vehicle is subject to drift forces caused by ocean currents, wind, and waves. The proposed algorithm is motivated by a line-of-sight (LOS) guidance principle used by ancient navigators, which is here extended to path following of Dubins paths. The unknown sideslip angle is treated as a constant parameter, which is estimated using an adaptation law. The equilibrium points of the cross-track and parameter estimation errors are proven to be uniformly semiglobally exponentially stable. This guarantees that the estimated sideslip angle converges to its true value exponentially. The adaptive control law is in fact an integral LOS controller for path following since the parameter adaptation law provides integral action. The proposed guidance law is intended for maneuvering in the horizontal-plane at given speeds and typical applications are marine craft, autonomous underwater vehicles, unmanned aerial vehicles as well as other vehicles and crafts, where the goal is to follow a predefined parametrized curve without time constraints. Two vehicle cases studies are included to verify the theoretical results.
359 citations
TL;DR: This paper provides a survey of results in linear parameter-varying (LPV) control that have been validated by experiments and/or high-fidelity simulations.
Abstract: This paper provides a survey of results in linear parameter-varying (LPV) control that have been validated by experiments and/or high-fidelity simulations. The LPV controller synthesis techniques employed in the references of this survey are briefly reviewed and compared. The methods are classified into polytopic, linear fractional transformation, and gridding-based techniques and it is reviewed how in each of these approaches, synthesis can be carried out as a convex optimization problem via a finite number of linear matrix inequalities (LMIs) for both parameter-independent and parameter-dependent Lyapunov functions. The literature is categorized with regard to the application, the complexity induced by the controlled system’s dynamic and scheduling orders, as well as the synthesis method. Exemplary cases dealing with specific control design problems are presented in more detail to point control engineers to possible approaches that have been successfully applied. Furthermore, key publications in LPV control are related to application achievements on a timeline.
303 citations
TL;DR: The additional set of four control inputs actuating the propeller tilting angles is shown to yield full actuation to the quadrotor position/orientation in space, thus allowing it to behave as a fully actuated flying vehicle.
Abstract: Standard quadrotor unmanned aerial vehicles (UAVs) possess a limited mobility because of their inherent underactuation, that is, availability of four independent control inputs (the four propeller spinning velocities) versus the 6 degrees of freedom parameterizing the quadrotor position/orientation in space. Thus, the quadrotor pose cannot track arbitrary trajectories in space (e.g., it can hover on the spot only when horizontal). Because UAVs are more and more employed as service robots for interaction with the environment, this loss of mobility due to their underactuation can constitute a limiting factor. In this paper, we present a novel design for a quadrotor UAV with tilting propellers which is able to overcome these limitations. Indeed, the additional set of four control inputs actuating the propeller tilting angles is shown to yield full actuation to the quadrotor position/orientation in space, thus allowing it to behave as a fully actuated flying vehicle. We then develop a comprehensive modeling and control framework for the proposed quadrotor, and subsequently illustrate the hardware and software specifications of an experimental prototype. Finally, the results of several simulations and real experiments are reported to illustrate the capabilities of the proposed novel UAV design.
299 citations
TL;DR: Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.
Abstract: Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SoC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SoC trajectories that are utilized as final-state constraints in the MPC level. This PHEV energy management framework is evaluated under three different scenarios: 1) without traffic flow information; 2) with static traffic flow information; and 3) with dynamic traffic flow information. Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.
277 citations
TL;DR: This paper proposes a novel approach to the control of TCLs that allows for accurate modulation of the aggregate power consumption of a large collection of appliances through stochastic control and presents a particular implementation that results in analytically tractable solutions both for the global response and for the device-level control actions.
Abstract: Thermostatically controlled loads (TCLs), such as refrigerators, air-conditioners and space heaters, offer significant potential for short-term modulation of their aggregate power consumption. This ability can be used in principle to provide frequency response services, but controlling a multitude of devices to provide a measured collective response has proven to be challenging. Many controller implementations struggle to manage simultaneously the short-term response and the long-term payback, whereas others rely on a real-time command-and-control infrastructure to resolve this issue. In this paper, we propose a novel approach to the control of TCLs that allows for accurate modulation of the aggregate power consumption of a large collection of appliances through stochastic control. By construction, the control scheme is well suited for decentralized implementation, and allows each appliance to enforce strict temperature limits. We also present a particular implementation that results in analytically tractable solutions both for the global response and for the device-level control actions. Computer simulations demonstrate the ability of the controller to modulate the power consumption of a population of heterogeneous appliances according to a reference power profile. Finally, envelope constraints are established for the collective demand response flexibility of a heterogeneous set of TCLs.
235 citations
TL;DR: A high-gain disturbance observer (HGDOB)-based backstepping control with position tracking error constraint for electro-hydraulic systems to improve the position tracking performance in the presence of disturbances is proposed.
Abstract: We propose a high-gain disturbance observer (HGDOB)-based backstepping control with position tracking error constraint for electro-hydraulic systems to improve the position tracking performance in the presence of disturbances. The HGDOB is designed to estimate the disturbances that include the friction, the load force, and the parameter uncertainties. Auxiliary state variables are proposed to avoid amplification of the measurement noise in the HGDOB. To compensate for the disturbances while guaranteeing tolerance of the position tracking error, the backstepping controller is proposed by using the barrier Lyapunov function. As a result, the proposed method satisfies the output constraint and improves the position tracking performance in the presence of disturbances. Its performance is validated via simulations and experiments.
230 citations
TL;DR: The proposed adaptive unscented Kalman filtering method provides better accuracy both in battery model parameters estimation and the battery SoC estimation.
Abstract: In this brief, to get a more accurate and robust state of charge (SoC) estimation, the lithium-ion battery model parameters are identified using an adaptive unscented Kalman filtering method, and based on the updated model, the battery SoC is estimated consequently. An adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the unscented Kalman filter (UKF) context. The effectiveness of the proposed method is evaluated through experiments under different power duties in the laboratory environment. The obtained results are compared with that of the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms. The comparison shows that the proposed method provides better accuracy both in battery model parameters estimation and the battery SoC estimation.
220 citations
TL;DR: Networked predictive control is employed to design a WADC for the generator exciter to enhance the damping of interarea oscillations in a large-scale power system and deal with the model uncertainties and variation of operating conditions.
Abstract: Wide-area damping controller (WADC) requires communication networks to transmit remote signals. The usage of communication networks will introduce time delays into the control loop of the WADC. Ignoring this time delay would deteriorate the damping performance provided by the WADC or even cause the whole system instability. This paper employs networked predictive control (NPC) to design a WADC for the generator exciter to enhance the damping of interarea oscillations in a large-scale power system. The NPC incorporates with a generalized predictive control (GPC) to generate optimal control predictions, and a network delay compensator to detect and compensate both constant and random delays. Moreover, model identification is used to obtain an equivalent reduced-order model of the large-scale power system and deal with the model uncertainties and variation of operating conditions. Case studies are based on the New England 10-machine 39-bus system. Effectiveness of the proposed WADC is verified by simulation studies and compared with a conventional WADC and a GPC-based WADC without delay compensation.
212 citations
TL;DR: This paper presents a stochastic model predictive control approach to building heating, ventilation, and air conditioning (HVAC) systems and focuses on the tradeoff between computational tractability and conservatism of the resulting SMPC scheme.
Abstract: This paper presents a stochastic model predictive control (SMPC) approach to building heating, ventilation, and air conditioning (HVAC) systems The building HVAC system is modeled as a network of thermal zones controlled by a central air handling unit and local variable air volume boxes In the first part of this paper, simplified nonlinear models are presented for thermal zones and HVAC system components The uncertain load forecast in each thermal zone is modeled by finitely supported probability density functions (pdfs) These pdfs are initialized using historical data and updated as new data becomes available In the second part of this paper, we present a SMPC design that minimizes expected energy cost and bounds the probability of thermal comfort violations SMPC uses predictive knowledge of uncertain loads in each zone during the design stage The complexity of a commercial building requires special handling of system nonlinearities and chance constraints to enable real-time implementation, minimize energy cost, and guarantee thermal comfort This paper focuses on the tradeoff between computational tractability and conservatism of the resulting SMPC scheme The proposed SMPC scheme is compared with alternative SMPC designs, and the effectiveness of the proposed approach is demonstrated by simulation and experimental tests
TL;DR: A stochastic model predictive control-based energy management strategy using the vehicle location, traveling direction, and terrain information of the area for HEVs running in hilly regions with light traffic is proposed and shown that the developed method can help maintaining the battery SoC within its boundaries and achieve good energy consumption performance.
Abstract: The energy efficiency of parallel hybrid electric vehicles (HEVs) can degrade significantly when the battery state-of-charge (SoC) reaches its boundaries. The road grade has a great influence on the HEV battery charging and discharging processes, and therefore the HEV energy management can be benefited from the road grade preview. In real-world driving, the road grade ahead can be considered as a random variable because the future route is not always available to the HEV controller. This brief proposes a stochastic model predictive control-based energy management strategy using the vehicle location, traveling direction, and terrain information of the area for HEVs running in hilly regions with light traffic. The strategy does not require a determined route being known in advance. The road grade is modeled as a Markov chain and stochastic HEV fuel consumption and battery SoC models are developed. The HEV energy management problem is formulated as a finite-horizon Markov decision process and solved using stochastic dynamic programming. The proposed method is evaluated in simulation and compared with an equivalent consumption minimization strategy and the dynamic programming results. It is shown that the developed method can help maintaining the battery SoC within its boundaries and achieve good energy consumption performance.
TL;DR: This paper presents a strategy and case studies of spacecraft relative motion guidance and control based on the application of linear quadratic model predictive control (MPC) with dynamically reconfigurable constraints with robust to estimator dynamics and measurement noise.
Abstract: This paper presents a strategy and case studies of spacecraft relative motion guidance and control based on the application of linear quadratic model predictive control (MPC) with dynamically reconfigurable constraints. The controller is designed to transition between the MPC guidance during a spacecraft rendezvous phase and MPC guidance during a spacecraft docking phase, with each phase having distinct requirements, constraints, and sampling rates. Obstacle avoidance is considered in the rendezvous phase, while a line-of-sight cone constraint, bandwidth constraints on the spacecraft attitude control system, and exhaust plume direction constraints are addressed during the docking phase. The MPC controller is demonstrated in simulation studies using a nonlinear model of spacecraft orbital motion. The implementation uses estimates of spacecraft states derived from relative angle and range measurements, and is robust to estimator dynamics and measurement noise.
TL;DR: Two fault-tolerant control schemes for spacecraft attitude stabilization with external disturbances are proposed in this brief, based on integral-type sliding mode control strategy to compensate for actuator faults without controller reconfiguration.
Abstract: Two fault-tolerant control (FTC) schemes for spacecraft attitude stabilization with external disturbances are proposed in this brief. The approach is based on integral-type sliding mode control strategy to compensate for actuator faults without controller reconfiguration. First, a basic integral-type sliding mode FTC scheme is designed so that sliding manifold can be maintained from the very beginning. Once the system enters the sliding mode, the dynamics of the closed-loop system with actuator fault is identical to that of the nominal healthy system. Second, the integral-type sliding mode fault-tolerant controller is incorporated with adaptive technique to accommodate actuator faults so that the required boundary information can be relaxed. The effectiveness of the proposed schemes against actuator faults is demonstrated in simulation.
TL;DR: An observer-based fault reconstruction method for PEM fuel cells using an adaptive-gain second-order sliding mode (SOSM) observer for observing the system states, where the adaptive law estimates the uncertain parameters.
Abstract: This paper presents an observer-based fault reconstruction method for PEM fuel cells. This method extends the results of a class of nonlinear uncertain systems with Lipschitz nonlinearities. An adaptive-gain second-order sliding mode (SOSM) observer is developed for observing the system states, where the adaptive law estimates the uncertain parameters. The inherent equivalent output error injection feature of SOSM algorithm is then used to reconstruct the fault signal. The performance of the proposed observer is validated through a hardware-in-loop emulator. The experimental results illustrate the feasibility and effectiveness of the proposed approach for application to fuel cell air-feed systems.
TL;DR: The proposed multivariable compensator for the hysteresis is based on the combination of the inverse multiplicative structure with the model, which permits to avoid additional calculation of its parameters.
Abstract: This paper is concerned with multivariable coupled hysteretic systems. The traditional Bouc–Wen monovariable hysteresis model devoted to 1 degree of freedom (DoF) actuated systems is extended to model the hysteresis in systems with multiple DoF, which typify strong cross-couplings. The proposed approach is able to model and to compensate for known hysteresis nonlinearities that affect smart materials. First, after presenting the new multivariable hysteresis Bouc–Wen model, a procedure of identification of its parameters is proposed. Then, we propose a multivariable compensator for the hysteresis. The compensator is based on the combination of the inverse multiplicative structure with the model, which permits to avoid additional calculation of its parameters. Such advantage is essential when the number of DoF is high. All along this paper, the cases of underactuated, overactuated, and fully actuated hysteretic systems are discussed. Finally, the proposed method is used to model and to compensate for the hysteresis in a 3-DoF piezoelectric tube actuator. The experimental results demonstrate its efficiency to linearize the hysteresis in the direct transfers and to minimize the hysteresis of the cross-couplings.
TL;DR: A dynamic model of a mobile wheeled inverted pendulum (MWIP) system is improved considering friction forces, and a nonlinear disturbance observer (NDO)-based dynamic surface controller is investigated to control the MWIP system.
Abstract: In this brief, a dynamic model of a mobile wheeled inverted pendulum (MWIP) system is improved considering friction forces, and a nonlinear disturbance observer (NDO)-based dynamic surface controller is investigated to control the MWIP system. Using a coordinate transformation, this non-Class-I type underactuated system is presented as a semistrict feedback form, which is convenient for dynamic surface controller design. A dynamic surface controller together with an NDO is designed to stabilize the underactuated plant. The proposed approach can compensate the external disturbances and the model uncertainties to improve the system performance significantly. The stability of the closed-loop MWIP system is proved by Lyapunov theorem. Experiment results are presented to illustrate the feasibility and efficiency of the proposed method.
TL;DR: The formulation of an integral suboptimal second-order sliding mode ((ISSOSM) control algorithm, oriented to solve motion control problems for robot manipulators, is presented in this paper and satisfactory experimental results confirm that the new algorithm can actually be used in an industrial context.
Abstract: The formulation of an integral suboptimal second-order sliding mode ((ISSOSM) control algorithm, oriented to solve motion control problems for robot manipulators, is presented in this paper. The proposed algorithm is designed so that the so-called reaching phase , normally present in the evolution of a system controlled via the sliding mode approach, is reduced to a minimum. This fact makes the algorithm more suitable to be applied to a real industrial robot, since it enhances its robustness, by extending it also to time intervals during which the classical sliding mode is not enforced. Moreover, since the algorithm generates second-order sliding modes, while the model of the controlled electromechanical system has a relative degree equal to one, the control action actually fed into the plant is continuous, which provides a positive chattering alleviation effect. The assessment of the proposal has been carried out by experimentally testing it on a COMAU SMART3-S2 anthropomorphic industrial robot manipulator. The satisfactory experimental results, also compared with those obtained with a standard proportional-derivative controller and with the original suboptimal algorithm, confirm that the new algorithm can actually be used in an industrial context.
TL;DR: New switched constraints are constructed to describe the different modes (charging and discharging) of the battery, such that the burden of using a switched multiple-input-multiple-output state-space model could be circumvented.
Abstract: A new adaptive switched model predictive control (MPC) strategy is designed in this brief for energy dispatching of a photovoltaic-diesel-battery hybrid power system, where the battery is unpermitted to charge and discharge simultaneously. The distinguishing feature of the proposed switched MPC is that, new switched constraints are constructed to describe the different modes (charging and discharging) of the battery, such that the burden of using a switched multiple-input-multiple-output state-space model could be circumvented. Parameters of the battery are unknown constants, and are estimated online with an adaptive updating law. In the switched MPC algorithm, predictive horizon and control horizon vary according to the predefined switching schedule. On the basis of optimization with the switched constraints, receding horizon control is used to obtain the dispatching strategy for the hybrid power system. Performances of the closed-loop system with the proposed switched MPC are verified by simulation results.
TL;DR: A multirobot system that integrates reinforcement learning and flocking control to allow robots to learn collaboratively to avoid predator/enemy is proposed and can conduct concurrent learning in a distributed fashion.
Abstract: Multirobot collaboration has great potentials in tasks, such as reconnaissance and surveillance. In this paper, we propose a multirobot system that integrates reinforcement learning and flocking control to allow robots to learn collaboratively to avoid predator/enemy. Our system can conduct concurrent learning in a distributed fashion as well as generate efficient combination of high-level behaviors (discrete states and actions) and low-level behaviors (continuous states and actions) for multirobot cooperation. In addition, the combination of reinforcement learning and flocking control enables multirobot networks to learn how to avoid predators while maintaining network topology and connectivity. The convergence and scalability of the proposed system are investigated. Simulations and experiments are performed to demonstrate the effectiveness of the proposed system.
TL;DR: Simulation studies and comprehensive comparisons with traditional adaptive control schemes demonstrate remarkable performance and superiority of the SARFNC scheme in terms of tracking errors and online approximation.
Abstract: In this paper, a novel self-constructing adaptive robust fuzzy neural control (SARFNC) scheme for tracking surface vehicles, whereby a self-constructing fuzzy neural network (SCFNN) is employed to approximate system uncertainties and unknown disturbances, is proposed. The salient features of the SARFNC scheme are as follows: 1) unlike the predefined-structure approaches, the SCFNN is able to online self-construct dynamic-structure fuzzy neural approximator by generating and pruning fuzzy rules, and achieve accurate approximation; 2) an adaptive approximation-based controller (AAC) is designed by combining sliding-mode control with SCFNN approximation using improved projection-based adaptive laws, which avoid parameter drift and singularity in membership functions simultaneously; 3) to compensate for approximation errors, a robust supervisory controller (RSC) is presented to enhance the robustness of the overall SARFNC control system; and 4) the SARFNC consisting of AAC and RSC can achieve an excellent tracking performance, whereby tracking errors and their first derivatives are globally uniformly ultimately bounded. Simulation studies and comprehensive comparisons with traditional adaptive control schemes demonstrate remarkable performance and superiority of the SARFNC scheme in terms of tracking errors and online approximation.
TL;DR: A distributed method is proposed such that all of the individual PEVs simultaneously update their own best charging behaviors with respect to a common electricity price curve, which is updated as the generation marginal cost withrespect to the aggregated charging behaviors of the PEV populations implemented at the last step.
Abstract: Much research has focused on the issue of how to effectively coordinate the charging behaviors of large-scale plug-in electric vehicles (PEVs), like the valley-fill strategy, to minimize their impacts on the power grid. However, high charging rates under the valley-fill strategy may result in a higher battery degradation cost. Consequently, in this brief, we formulate a class of PEV charging coordination problems that deal with the tradeoff between the total generation cost and the accumulated battery degradation cost of PEV populations. Due to the autonomy of individual PEVs and the computational complexity of the system with large-scale PEVs, it is impractical to implement the solution in a centralized way. Alternatively, in this brief, we propose a distributed method such that all of the individual PEVs simultaneously update their own best charging behaviors with respect to a common electricity price curve, which is updated as the generation marginal cost with respect to the aggregated charging behaviors of the PEV populations implemented at the last step. The iteration procedure terminates in case the price curve does not update any longer. We show that by applying the proposed distributed method and under certain mild conditions, the system can converge to a unique charging strategy, which is nearly socially optimal. Simulation examples are studied to illustrate the results developed in this brief.
TL;DR: Two nonlinear observer designs are presented based on a reduced order electrochemical model that consists of a Luenberger term acting on nominal errors and a variable structure term for handling model uncertainty.
Abstract: Advanced battery management systems rely on accurate cell- or module-level state-of-charge (SOC) information for effective control, monitoring, and diagnostics. Electrochemical models provide arguably the most accurate and detailed information about the SOC of lithium-ion cells. In this brief, two nonlinear observer designs are presented based on a reduced order electrochemical model. Both observers consist of a Luenberger term acting on nominal errors and a variable structure term for handling model uncertainty. Using Lyapunov’s direct method, the design of the Luenberger term in each observer is formulated as a linear matrix inequality problem, whereas the variable structure term is designed assuming uncertainty bounds. Simulation and experimental studies are included to demonstrate the performance of the proposed observers.
TL;DR: The adaptive control design to solve the trajectory tracking problem of a Delta robot with uncertain dynamical model showed a better performance than the regular proportional-integral-derivative (PID) controller with feed-forward actions as well as a nonadaptive active disturbance rejection controller.
Abstract: This paper describes the adaptive control design to solve the trajectory tracking problem of a Delta robot with uncertain dynamical model. This robot is a fully actuated, parallel closed-chain device. The output-based adaptive control was designed within the active disturbance rejection framework. An adaptive nonparametric representation for the uncertain section of the robot model was obtained using an adaptive least mean squares procedure. The adaptive algorithm was designed without considering the velocity measurements of the robot joints. Therefore, a simultaneous observer–identifier scheme was the core of the control design. A set of experimental tests were developed to prove the performance of the algorithm presented in this paper. Some reference trajectories were proposed which were successfully tracked by the robot. In all the experiments, the adaptive scheme showed a better performance than the regular proportional-integral-derivative (PID) controller with feed-forward actions as well as a nonadaptive active disturbance rejection controller. A set of numerical simulations was developed to show that even under five times faster reference trajectories, the adaptive controller showed better results than the PID controller.
TL;DR: Two novel approaches oriented to the design of fault-tolerant control schemes for reliable regulation of generator torque in a wind turbine that can be affected by both model uncertainties and actuator faults in its generator/converter.
Abstract: Wind turbines are designed to generate electrical energy as efficiently and reliably as possible. Advanced fault detection, diagnosis, and accommodation schemes are necessary to realize the required levels of reliability and availability in modern wind turbines. This paper presents two novel approaches oriented to the design of fault-tolerant control (FTC) schemes for reliable regulation of generator torque in a wind turbine that can be affected by both model uncertainties and actuator faults in its generator/converter. The first approach is based on fuzzy model reference adaptive control in which a fuzzy inference mechanism is used for parameter adaptation without any explicit knowledge of the potential faults in the system. The second approach exploits fuzzy modeling and identification method to develop an integrated model-based fault detection and diagnosis, and automatic signal correction mechanism to accommodate potential faults in the system based on online diagnostic information. Finally, the effectiveness of the proposed FTC schemes is illustrated and compared by a series of simulations on a well-known large offshore wind turbine benchmark in the presence of wind turbulences, measurement noises, and realistic fault scenarios in the generator/converter torque actuator.
TL;DR: Analytical and experimental results are provided that verify the effectiveness of the proposed architecture for generation control in islanded microgrids, and illustrate the performance of the aforementioned distributed algorithms under a variety of scenarios.
Abstract: In this paper, we propose a distributed architecture for generation control in islanded ac microgrids with both synchronous generators and inverter-interfaced power supplies. Although they are smaller and have lower ratings, the generation control objectives for an islanded microgrid are similar to those in large power systems, e.g., bulk power transmission networks; specifically, without violating limits on generator power output, frequency must be regulated and generation costs should be minimized. However, in large power systems, the implementation of the generation control functions is centralized, i.e., there is a computer that resides in a centralized location, e.g., a control center, with measurements and control signals telemetered between the generating units and the centrally located computer. The architecture for generation control that we propose in this paper does not rely on such a centrally located computer. Instead, the implementation of the control functions is distributed and relies on iterative algorithms that combine local measurements and certain information acquired from neighboring generating units with local, low-complexity computations. We provide analytical and experimental results that verify the effectiveness of the proposed architecture for generation control in islanded microgrids, and illustrate the performance of the aforementioned distributed algorithms under a variety of scenarios.
TL;DR: In this brief, a new iterative optimization algorithm is proposed that enables the use of rational basis functions in ILC for single-input single-output systems and an experimental case study confirms the advantages ofrational basis functions compared with preexisting results, as well as the effectiveness of the proposed iterative algorithm.
Abstract: Iterative learning control (ILC) approaches often exhibit poor extrapolation properties with respect to exogenous signals, such as setpoint variations. This brief introduces rational basis functions in ILC. Such rational basis functions have the potential to both increase performance and enhance the extrapolation properties. The key difficulty that is associated with these rational basis functions lies in a significantly more complex optimization problem when compared with using preexisting polynomial basis functions. In this brief, a new iterative optimization algorithm is proposed that enables the use of rational basis functions in ILC for single-input single-output systems. An experimental case study confirms the advantages of rational basis functions compared with preexisting results, as well as the effectiveness of the proposed iterative algorithm.
TL;DR: Experiments on a quadrotor testbed show that the proposed robust DOB can suppress the external disturbances and measurement noise, with the robustness against system uncertainties.
Abstract: This brief proposes a disturbance rejection control strategy for attitude tracking of an aircraft with both internal uncertainties and external disturbances. The proposed control strategy consists of a robust disturbance observer (DOB) and a nonlinear feedback controller. Specifically, a robust DOB is proposed to compensate the uncertain rotational dynamics into a nominal plant, based on which a nonlinear feedback controller is implemented for desired tracking performance. We first divide the practical rotational dynamics into the nominal part, external disturbances, and equivalent internal disturbances. Then, property of equivalent internal disturbances is explored for stability analysis. A robust DOB is optimized based on $H_{\infty }$ theory to guarantee disturbance rejection performance and robustness against system uncertainties. A practical nonlinear feedback controller is hence applied to stabilize the compensated system based on backstepping approach. Experiments on a quadrotor testbed show that the proposed robust DOB can suppress the external disturbances and measurement noise, with the robustness against system uncertainties.
TL;DR: An efficient linear matrix inequality (LMI) method for determining a solution to the general case of this inverse LQR problem when both the weighting matrices Q and R are unknown and results show that it can recover a cost function which may provide a useful insight on the human motor control goal.
Abstract: In this brief, we present a set of techniques for finding a cost function to the time-invariant linear quadratic regulator (LQR) problem in both continuous- and discrete-time cases. Our methodology is based on the solution to the inverse LQR problem, which can be stated as: does a given controller $K$ describe the solution to a time-invariant LQR problem, and if so, what weights $Q$ and $R$ produce $K$ as the optimal solution? Our motivation for investigating this problem is the analysis of motion goals in biological systems. We first describe an efficient linear matrix inequality (LMI) method for determining a solution to the general case of this inverse LQR problem when both the weighting matrices $Q$ and $R$ are unknown. Our first LMI-based formulation provides a unique solution when it is feasible. In addition, we propose a gradient-based, least-squares minimization method that can be applied to approximate a solution in cases when the LMIs are infeasible. This new method is very useful in practice since the estimated gain matrix $K$ from the noisy experimental data could be perturbed by the estimation error, which may result in the infeasibility of the LMIs. We also provide an LMI minimization problem to find a good initial point for the minimization using the proposed gradient descent algorithm. We then provide a set of examples to illustrate how to apply our approaches to several different types of problems. An important result is the application of the technique to human subject posture control when seated on a moving robot. Results show that we can recover a cost function which may provide a useful insight on the human motor control goal.
TL;DR: A virtual coordinator together with a communication protocol between it and subsystems is developed in order to achieve two aims: to coordinate subsystems with an optimal coordination solution using judgement matrices while multiple subsystems require global reconfigurations and to reduce exchanged messages between the coordinator and these subsystems.
Abstract: Dynamic reconfigurability is receiving more and more attention from both academy and industry, which means the ability to flexibly modify system functions by adding/removing hardware/software components, modifying logic relation between components, or updating particular system data at runtime without sacrificing the system performance. A distributed reconfigurable discrete event control system (DRDECS) is composed of several networked reconfigurable subsystems. In order to realize system functions, these reconfigurable subsystems communicate and coordinate with each other, since any casually reconfiguration applied to a subsystem may cause risks to others, or even to the safety of the whole system. This brief proposes a new coordination method for a DRDECS, where each subsystem is modeled by a reconfigurable timed net condition/event system. A virtual coordinator together with a communication protocol between it and subsystems is developed in order to achieve two aims: 1) to coordinate subsystems with an optimal coordination solution using judgement matrices while multiple subsystems require global reconfigurations and 2) to reduce exchanged messages between the coordinator and these subsystems. Furthermore, for the purpose of checking functional and temporal properties of a DRDECS with this virtual coordinator, a computation tree logic-based model checking method is applied. Finally, a hypothetic manufacturing plant is used as a running example to illustrate this brief.