Showing papers in "IEEE Transactions on Control Systems and Technology in 2018"

Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation

Abstract: This brief is concerned with the distributed maneuvering of multiple autonomous surface vehicles guided by a virtual leader moving along a parameterized path. In the guidance loop, a distributed guidance law is developed by incorporating a constant bearing strategy into a path-maneuvering design such that a prescribed formation pattern can be reached. To optimize the guidance signal under velocity constraint as well as minimize control torque during transient phase, an optimization-based command governor is employed to generate an optimal guidance signal for vehicle kinetics. The optimization problem is formulated as a bound-constrained quadratic programming problem, which is solved using a recurrent neural network. In the control loop, an estimator is developed where a fuzzy system is used to approximate unknown kinetics based on input and output data. Next, a kinetic control law is constructed based on the optimal command signal and the fuzzy-system-based estimator. By virtue of cascade stability analysis, it is proven that distributed maneuvering errors converge to a residual set. The simulation results illustrate the efficacy of the proposed method.

Adaptive Trajectory Tracking Control of a Fully Actuated Surface Vessel With Asymmetrically Constrained Input and Output

Abstract: This brief addresses the trajectory tracking control problem of a fully actuated surface vessel subjected to asymmetrically constrained input and output. The controller design process is based on the backstepping technique. An asymmetric time-varying barrier Lyapunov function is proposed to address the output constraint. To overcome the difficulty of nondifferentiable input saturation, a smooth hyperbolic tangent function is employed to approximate the asymmetric saturation function. A Nussbaum function is introduced to compensate for the saturation approximation and ensure the system stability. The command filters and auxiliary systems are integrated with the control law to avoid the complicated calculation of the derivative of the virtual control in backstepping. In addition, the bounds of uncertainties and disturbances are estimated and compensated with an adaptive algorithm. With the proposed control, the constraints will never be violated during operation, and all system states are bounded. Simulation results and comparisons with standard method illustrate the effectiveness and advantages of the proposed controller.

Active Adaptive Estimation and Control for Vehicle Suspensions With Prescribed Performance

Abstract: This paper proposes an adaptive control for vehicle active suspensions with unknown nonlinearities (eg, nonlinear springs and piecewise dampers) A prescribed performance function that characterizes the convergence rate, maximum overshoot, and steady-state error is incorporated into the control design to stabilize the vertical and pitch motions, such that both the transient and steady-state suspension response are guaranteed Moreover, a novel adaptive law is used to achieve precise estimation of essential parameters (eg, mass of vehicle body and moment of inertia for pitch motion), where the parameter estimation error is obtained explicitly and then used as a new leakage term Theoretical studies prove the convergence of the estimated parameters, and compare the suggested controller with generic adaptive controllers using the gradient descent and e-modification schemes In addition to motion displacements, dynamic tire loads and suspension travel constraints are also considered Extensive comparative simulations on a dynamic simulator consisting of commercial vehicle simulation software Carsim 81 and MATLAB Simulink are provided to show the efficacy of the proposed control, and to illustrate the improved performance

Game Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems

Abstract: Autonomous driving has been the subject of incre- ased interest in recent years both in industry and in academia. Serious efforts are being pursued to address legal, technical, and logistical problems and make autonomous cars a viable option for everyday transportation. One significant challenge is the time and effort required for the verification and validation of the decision and control algorithms employed in these vehicles to ensure a safe and comfortable driving experience. Hundreds of thousands of miles of driving tests are required to achieve a well calibrated control system that is capable of operating an autonomous vehicle in an uncertain traffic environment where interactions among multiple drivers and vehicles occur simultaneously. Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help to decrease the time and effort necessary for the development of the autonomous driving control algorithms by providing a venue where acceptable initial control calibrations can be achieved quickly and safely before actual road tests. In this paper, we present a game theoretic traffic model that can be used to: 1) test and compare various autonomous vehicle decision and control systems and 2) calibrate the parameters of an existing control system. We demonstrate two example case studies, where, in the first case, we test and quantitatively compare two autonomous vehicle control systems in terms of their safety and performance, and, in the second case, we optimize the parameters of an autonomous vehicle control system, utilizing the proposed traffic model and simulation environment.

Neural Network-Based State of Charge Observer Design for Lithium-Ion Batteries

Abstract: A new method for the state of charge (SOC) estimation of lithium-ion batteries is proposed based on an inclusive equivalent circuit model in this brief. In particular, we propose to utilize the neural network to estimate the uncertainties in the battery model online. A radial basis function neural network-based nonlinear observer is then designed to estimate the battery’s SOC. Following Lyapunov stability analysis, it is proved that the SOC estimation error is ultimately bounded and the error bound can be arbitrarily small. Experimental and simulation results illustrate the performance of the proposed approach. Furthermore, we compare the SOC estimation results of the extended Kalman filter with the proposed radial basis function neural network-based nonlinear observer. The proposed approach has faster convergence speed and higher precision.

Active Disturbance Rejection Attitude Control for a Dual Closed-Loop Quadrotor Under Gust Wind

Abstract: In this brief, attitude control is investigated for a quadrotor under gust wind via a dual closed-loop control framework. In the dual closed-loop framework, active disturbance rejection control and proportional–derivative control are used in the inner and outer loops, respectively. The perturbations of gust wind are considered as dynamic disturbances, which are estimated by an extended state observer in the inner loop. Both convergence and stabilization are given for the extended state observer and the closed-loop system, respectively. Experimental results are given to show the effectiveness of the proposed method for quadrotors.

Geometric Control of Quadrotor UAVs Transporting a Cable-Suspended Rigid Body

Abstract: This paper is focused on tracking control for a rigid body payload that is connected to an arbitrary number of quadrotor unmanned aerial vehicles via rigid links. An intrinsic form of the equations of motion is derived on the nonlinear configuration manifold, and a geometric controller is constructed such that the payload asymptotically follows a given desired trajectory for its position and attitude in the presence of uncertainties. The unique feature is that the coupled dynamics between the rigid body payload, links, and quadrotors are explicitly incorporated into control system design and stability analysis. These are developed in a coordinate-free fashion to avoid singularities and complexities that are associated with local parameterizations. The desirable features of the proposed control system are illustrated by a numerical example.

Flexible Spacing Adaptive Cruise Control Using Stochastic Model Predictive Control

Abstract: This paper proposes a stochastic model predictive control (MPC) approach to optimize the fuel consumption in a vehicle following context. The practical solution of that problem requires solving a constrained moving horizon optimal control problem using a short-term prediction of the preceding vehicle’s velocity. In a deterministic framework, the prediction errors lead to constraint violations and to harsh control reactions. Instead, the suggested method considers errors, and limits the probability of a constraint violation. A conditional linear Gauss model is developed and trained with real measurements to estimate the probability distribution of the future velocity of the preceding vehicle. The prediction model is used to evaluate two different stochastic MPC approaches. On the one hand, an MPC with individual chance constraints is applied. On the other hand, samples are drawn from the conditional Gaussian model and used for a scenario-based optimization approach. Finally, both developed control strategies are evaluated and compared against a standard deterministic MPC. The evaluation of the controllers shows a significant reduction of the fuel consumption compared with standard adaptive cruise control algorithms.

Adaptive Command-Filtered Backstepping Control of Robot Arms With Compliant Actuators

Abstract: Compliant actuators are significant for safe physical human-robot interaction. Series elastic actuator (SEA) is the most popular type of compliant actuators, which possesses several attractive features, such as low output impedance, back drivability, shock tolerance, smooth force transmission, and energy efficiency. This brief focuses on the adaptive command-filtered backstepping control (CFBC) design for robot arms driven by SEAs. The CFBC alleviates the complexity problem in integrator backstepping control and has attracted great attention. However, experimental results of adaptive CFBC have not been reported. In this brief, a simplified adaptive CFBC framework is developed for robot arms driven by SEAs, where discontinuous friction is compensated for by an adaptive mechanism. Closed-loop stability is rigorously established by the Lyapunov synthesis and time-scales separation. Experimental results based on a single-link SEA-driven robot arm have been provided to verify the effectiveness of the proposed control strategy.

A Two-Layer Stochastic Model Predictive Control Scheme for Microgrids

Abstract: A two-layer control scheme based on model predictive control (MPC) operating at two different timescales is proposed for the energy management of a grid-connected microgrid (MG), including a battery, a microturbine, a photovoltaic (PV) system, a partially non predictable load, and the input from the electrical network. The high-level optimizer runs at a slow timescale, relies on a simplified model of the system, and is in charge of computing the nominal operating conditions for each MG component over a long time horizon, typically one day, with sampling period of 15 min, so as to optimize an economic performance index on the basis of available predictions for the PV generation and load request. The low-level controller runs at higher frequency, typically 1 min, relies on a stochastic MPC (sMPC) algorithm, and adjusts the MG operation to minimize the difference, over each interval of 15 min, between the planned energy exchange and the real one, so avoiding penalties. The sMPC method is implemented according to a shrinking horizon strategy and ensures probabilistic constraints satisfaction. Detailed models and simulations of the overall control system are presented.

Reconfigurable Tolerant Control of Uncertain Mechanical Systems With Actuator Faults: A Sliding Mode Observer-Based Approach

Abstract: This paper studies a key issue of developing reconfigurable fault-tolerant control to retain a nominal feedback controller and simultaneously handles actuator faults and system uncertainty, while the closed-loop system is stabilized with all control objectives achieved. A theoretical architecture of a reconfigurable control design is presented for a class of uncertain mechanical systems by using an observer technique. As a stepping stone, a nonlinear observer-based estimation mechanism is designed to reconstruct uncertain dynamics and actuator faults with the estimation error converging to zero within finite time. A reconfigurable control effort is then synthesized from the reconstructed knowledge. This control power operates as a compensation control part, and it is added to the nominal control part to accommodate system uncertainties and actuator faults. It is proved that the overall system resulted from the developed control framework has the same control performance of the nominal closed-loop system, including certain system dynamics and the nominal control effort. The effectiveness of the scheme is validated on a serial robotic manipulator.

A Bioinspired Dynamics-Based Adaptive Tracking Control for Nonlinear Suspension Systems

Abstract: This paper investigates the energy-efficiency design of adaptive control for active suspension systems with a bioinspired nonlinearity approach To this aim, a bioinspired dynamics-based adaptive tracking control is proposed for nonlinear suspension systems In many existing techniques, one important effort is used for canceling vibration energy transmitted by suspension inherent nonlinearity to improve ride comfort Unlike existing methods, the proposed approach takes full advantage of beneficial nonlinear stiffness and damping characteristics inspired by the limb motion dynamics of biological systems to achieve advantageous nonlinear suspension properties with potentially less energy consumption The stability analysis of the desired bioinspired nonlinear dynamics is conducted within the Lyapunov framework Theoretical analysis and simulation results reveal that the proposed bioinspired nonlinear dynamics-based adaptive controller has a significant impact on the amount of energy consumption, considering the same basic control method and random excitation of road irregularity for a similar ride comfort performance

Estimation of the ECMS Equivalent Factor Bounds for Hybrid Electric Vehicles

Abstract: The strategy for energy management (EM) of a hybrid electric vehicle (HEV) has a considerable impact on the vehicle fuel economy. One well-known EM strategy is the equivalent consumption minimization strategy (ECMS) that is a form of Pontryagin’s minimum principle (PMP). PMP proves under certain conditions that ECMS yields the maximum fuel economy. However, even if the required conditions are met, the optimal value of the costate still has to be estimated. Many approaches have been suggested for estimating the optimal value of the costate, or the equivalent factor for using battery power in the ECMS cost function. Instead of direct estimation of ECMS optimal equivalent factor, this brief derives estimations for the upper and lower bounds of the optimal equivalent factor. The derived bounds are functions of the HEV configuration and independent of the drivecycle, verified by simulation results. The knowledge about these bounds can be employed in designing new types of adaptive ECMSs (A-ECMSs). To demonstrate the application of the bounds, this brief introduces a new A-ECMS. Finally, the simulation results are presented comparing the fuel economy of the introduced A-ECMS with the fuel economies of an existing A-ECMS and global optimal controller.

Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

Abstract: This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear $d$ – $q$ reference frame voltage-current model and linearized power flow approximations. This allows the optimal power flows to be solved as a convex optimization problem, for which fast and robust solvers exist. The proposed method does not assume that real and reactive power flows are decoupled, allowing line losses, voltage constraints, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real-time MPC implementation.

Obstacle Avoidance for Low-Speed Autonomous Vehicles With Barrier Function

Abstract: This paper presents an obstacle avoidance algorithm for low speed autonomous vehicles (AV), with guaranteed safety. A supervisory control algorithm is constructed based on a barrier function method, which works in a plug-and-play fashion with any lower level navigation algorithm. When the risk of collision is low, the barrier function is not active; when the risk is high, based on the distance to an “avoidable set,” the barrier function controller will intervene, using a mixed integer program to ensure safety with minimal control effort. This method is applied to solve the navigation and pedestrian avoidance problem of a low speed AV. Its performance is compared with two benchmark algorithms: a potential field method and the Hamilton–Jacobi method.

Spatial-Based Predictive Control and Geometric Corridor Planning for Adaptive Cruise Control Coupled With Obstacle Avoidance

Abstract: This paper presents an integrated control approach for autonomous driving comprising a corridor path planner that determines constraints on vehicle position, and a linear time-varying model predictive controller combining path planning and tracking in a road-aligned coordinate frame. The capabilities of the approach are illustrated in obstacle-free curved road-profile tracking, in an application coupling adaptive cruise control (ACC) with obstacle avoidance (OA), and in a typical driving maneuver on highways. The vehicle is modeled as a nonlinear dynamic bicycle model with throttle, brake pedal position, and steering angle as control inputs. Proximity measurements are assumed to be available within a given range field surrounding the vehicle. The proposed general feedback control architecture includes an estimator design for fusion of database information (maps), exteroceptive as well as proprioceptive measurements, a geometric corridor planner based on graph theory for the avoidance of multiple, potentially dynamically moving objects, and a spatial-based predictive controller. Switching rules for transitioning between four different driving modes, i.e., ACC, OA, obstacle-free road tracking (RT), and controlled braking (Brake), are discussed. The proposed method is evaluated on test cases, including curved and highway two-lane road tracks with static as well as moving obstacles.

Distributed Model Predictive Control for On-Connected Microgrid Power Management

Abstract: This paper considers the energy dispatching optimization of a grid-connected microgrid in a park in the city of Shanghai in a distributed framework, in order to improve its economic and environment-friendly performance. The microgrid is composed of distributed generations (DGs), energy storage systems, and shiftable loads. Some properties of the microgrid make the dispatching problem difficult. For example, the disturbance in renewable energy is unpredictable; the operation of shiftable loads is discrete and the relationship between the generated power and the total operation cost is nonlinear. A novel distributed model predictive control (MPC) is proposed for the power dispatching optimization of the microgrid in this paper, where there is a local MPC for each of the following entities: DGs, storage batteries, and shiftable loads. In this method, the centralized mix-integer programming problem of microgrid energy dispatching is converted into several interacted nonlinear programming problems and integer programming problems, and subsystem-based MPCs coordinate with each other via iteratively minimizing the cost over the entire system. In this way, the realization of plug-and-play property becomes easier and the computational load is reduced. The numerical results show the effectiveness of the proposed method.

Fault-Tolerant Cooperative Control Design of Multiple Wheeled Mobile Robots

Abstract: This brief investigates fault-tolerant cooperative control (FTCC) strategies for multiple differentially driven autonomous wheeled mobile robots (WMRs) in the presence of actuator faults during formation operation. First, for normal/fault-free cases and for preparation to the faults occurrence cases, an integrated approach combining input-output feedback linearization and distributed linear model predictive control techniques is designed and implemented on a team of WMRs to accomplish a formation task. Second, when actuator faults occur in one of the robots of the team, two cases are explicitly considered: 1) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are reassigned to the remaining healthy robots to complete the mission with graceful performance degradation and 2) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure their controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, a fault detection and diagnosis unit using a two-stage Kalman filter to detect and diagnose actuator faults is presented. Then, the FTCC problem is formulated as an optimal assignment problem, where a Hungarian algorithm is applied. Moreover, a collision avoidance algorithm based on mechanical impedance principle is proposed to avoid the potential collision between the healthy robots and the faulty ones. Formation operation of the robot team is based on a leader-follower approach, while the control algorithm is implemented in a distributed manner. The results of real experiments demonstrate the effectiveness of the proposed FTCC scheme in different fault scenarios.

Spline-Based Motion Planning for Autonomous Guided Vehicles in a Dynamic Environment

Abstract: Autonomous vehicles require a collision-free motion trajectory at every time instant. This brief presents an optimization-based method to calculate such trajectories for autonomous vehicles operating in an uncertain environment with moving obstacles. The proposed approach applies to linear system models, as well as to a particular class of nonlinear models, including industrially relevant vehicles, such as autonomous guided vehicles with front wheel, differential wheel, and rear-wheel steering. The method computes smooth motion trajectories, satisfying the vehicle’s kinematics, by using a spline parameterization. Furthermore, it exploits spline properties to keep the resulting nonlinear optimization problem small scale and to guarantee constraint satisfaction, without the need for time gridding. The resulting problem is solved sufficiently fast for online motion planning, dealing with uncertainties and changes in the environment. This brief demonstrates the potential of the method with extensive numerical simulations. In addition, it presents an experimental validation in which a KUKA youBot , steered as a holonomic or differential drive vehicle, drives through an environment with moving obstacles. To facilitate the further development and the numerical and experimental validation of the presented method, it is embodied in a user-friendly open-source software toolbox.

Gain Scheduled Attitude Control of Fixed-Wing UAV With Automatic Controller Tuning

Abstract: Fixed-wing unmanned aerial vehicles (UAVs) have become increasingly important in military, civil, and scientific sectors. Because of the existing nonlinearities, effective control this type of UAV remains a challenge. This paper proposes a gain scheduled proportional-integral derivative (PID) control system for fixed-wing UAVs where a family of PID cascade control systems is designed for several operating conditions of airspeed. This is done using an automatic tuning algorithm, where the controllers are automatically selected by deploying an airspeed sensor positioned ahead of the aircraft. Furthermore, the actual gain scheduling is carried out by forming an interpolation between the family members of the linear closed-loop system, which ensures a smooth transition from one operating point to another. Experimental results are conducted in a wind tunnel to show the successful design and implementation of the gain scheduled control system for the fixed-wing UAV and the significant performance improvement over a linear control system without controller adaptation.

Optimal Oxygen Excess Ratio Control for PEM Fuel Cells

Abstract: In this paper, a feedback linearization controller is proposed for the van compressor in the air supply system of a proton exchange membrane fuel cell. The control goal is to avoid oxygen starvation and reduce power consumption by tracking an optimal reference oxygen excess ratio. Specifically, an improved control-oriented third-order model of the air supply system is proposed with the model identification of the air compressor. The optimal reference oxygen excess ratio is obtained from experiments to maintain a maximum net power. Based on the air supply system model, a nonlinear controller is designed to track the optimal oxygen excess ratio using feedback linearization. Lyapunov-based technique is utilized to analyze the stability of the closed-loop system. Effectiveness of the proposed approach is illustrated by experimental results.

On the Convergence Conditions of Distributed Dynamic State Estimation Using Sensor Networks: A Unified Framework

Abstract: In this paper, the problem of distributed state estimation using sensor networks is considered for a very general scenario, where: 1) the process model of the target and the sensing models of local agents are assumed to be linear and time varying; 2) the communication topology between agents is modeled as a general directed graph, and is subject to change with time; and 3) there might exist a time-varying set of agents not directly sensing the target. A distributed hybrid information fusion (DHIF) algorithm, which requires no global parameter and only one communication iteration per time instant, is proposed. The DHIF algorithm computes confident but consistent estimates. The convergence of the proposed algorithm is guaranteed with very mild sufficient conditions formulated in such a general scenario. Specifically, the directed switching graphs do not necessarily need to be (jointly) strongly connected nor balanced. Then the conditions are shown to be “almost” necessary. In the special case where the process/sensing models and the topology are both time invariant, the conditions are necessary after certain relaxation. Comparisons with existing algorithms are shown both analytically and numerically. The convergence results are illustrated in simulations. In the end, the proposed algorithm is also extended to the situation with nonlinearities involved, and illustrated in simulation.

Nonlinear Adaptive Fault-Tolerant Quadrotor Altitude and Attitude Tracking With Multiple Actuator Faults

Abstract: This brief presents the design, analysis, and implementation of a nonlinear robust adaptive fault-tolerant altitude and attitude tracking method for accommodating actuator faults in quadrotor unmanned aerial vehicles without the need of a fault diagnosis mechanism. Actuator faults are modeled as a constant loss of effectiveness in the thrust generated by the rotors. The fault-tolerant control (FTC) algorithm guarantees asymptotic convergence of the altitude and attitude tracking error even in the presence of possible multiple actuator faults and modeling uncertainties. The FTC method is implemented using a real-time indoor quadrotor test environment. Experimental results are shown to illustrate the effectiveness of the algorithm.

Predefined-Time Convergence Control for High-Order Integrator Systems Using Time Base Generators

Abstract: In this brief, we propose a control method for high-order integrator systems that achieves predefined-time convergence, i.e., the system is driven to the origin in a desired settling time that can be set as an explicit parameter of the controller and it is achieved independently of the initial conditions. Our method can be applied to any single-input single-output (SISO) controllable linear system, to any SISO nonlinear system that can be transformed to the normal form with stable zero dynamics and to multiple-input multiple-output systems that can be decoupled into SISO subsystems in the previously mentioned forms. The proposed approach is based on the so-called time base generators (TBGs), which are time-dependent functions used to build time-varying control laws. The contribution of this brief is the generalization of the TBGs to develop predefined-time controllers for high-order systems, providing procedures to build the required time-dependent functions. The performance of the proposed controllers is evaluated and compared with finite-time and fixed-time controllers in simulations and experiments. We show the applicability of the proposed approach to control electromechanical systems, in particular for the dynamic control of robotic systems.

Consensus Control for Vehicular Platooning With Velocity Constraints

Abstract: In this paper, a distributed consensus control approach for vehicular platooning systems is proposed. In formalizing the underlying consensus problem, a realistic vehicle dynamics model is considered and a velocity-dependent spacing policy between two consecutive vehicles is realized. As a particular case, the approach allows to consider bidirectional vehicle interaction, which improves the cohesion between vehicles in the platoon. Exponential stability of the platoon dynamics is evaluated, also in the challenging scenario in which a limitation on the velocity of one of the vehicles in the platoon is introduced. The theoretical results are experimentally validated using a three-vehicle platoon consisting of (longitudinally) automated vehicles equipped with wireless intervehicle communication and radar-based sensing.

Robust Team Formation Control for Quadrotors

Abstract: In this brief, we develop a suboptimal $H_{\infty }$ controller for a leader-follower formation problem of quadrotors with the consideration of external disturbances and model parameter uncertainties. We also compare the control performances between this $H_{\infty }$ controller and an integral backstepping (IBS) controller for this problem. The resultant state feedback controller establishes the asymptotically stability of the closed-loop nonlinear system. Simulation results show a good performance for both controllers in normal circumstance, and the $H_{\infty }$ controller performs much better than the IBS controller under the disturbances. Experimental results of using $H_{\infty }$ controller show its stability and robustness against the disturbances.

Adaptive Trajectory Tracking for Quadrotor MAVs in Presence of Parameter Uncertainties and External Disturbances

Abstract: This paper presents an adaptive trajectory tracking control strategy for quadrotor micro aerial vehicles (MAVs). The proposed approach, while maintaining the common assumption of an orientation dynamics faster than the translational one, removes the assumption of absence of external disturbances and of geometric center coincident with the Center of Mass (CoM). In particular, the trajectory tracking control law is made adaptive with respect to the presence of external forces and moments (e.g., due to wind) and to the uncertainty of parameters of the dynamic model, such as the position of the CoM. A stability analysis is presented to analytically support the proposed controller, while numerical simulations are provided in order to validate its performance.

Dynamic Magnetometer Calibration and Alignment to Inertial Sensors by Kalman Filtering

Abstract: Magnetometer and inertial sensors are widely used for orientation estimation. Magnetometer usage is often troublesome, as it is prone to be interfered by onboard or ambient magnetic disturbance. The onboard soft-iron material distorts not only the magnetic field, but also the magnetometer sensor frame coordinate and the cross-sensor misalignment relative to inertial sensors. It is desirable to conveniently put magnetic and inertial sensors information in a common frame. Existing methods either split the problem into successive intrinsic and cross-sensor calibrations, or rely on stationary accelerometer measurements which are infeasible in dynamic conditions. This brief formulates the magnetometer calibration and alignment to inertial sensors as a state estimation problem, and collectively solves the magnetometer intrinsic and cross-sensor calibrations, as well as the gyroscope bias estimation. Sufficient conditions are derived for the problem to be globally observable, even when no accelerometer information is used at all. An extended Kalman filter is designed to implement the state estimation and comprehensive test data results show the superior performance of the proposed approach. It is immune to acceleration disturbance and applicable potentially in any dynamic conditions.

Line-Independent Plug-and-Play Controllers for Voltage Stabilization in DC Microgrids

Abstract: We consider the problem of stabilizing voltages in Direct Current (DC) microgrids given by the interconnection of Distributed Generation Units (DGUs), power lines, and loads We propose a decentralized control architecture where the primary controller of each DGU can be designed in a plug-and-play fashion, allowing the seamless addition of new DGUs Differently from several other approaches to primary control, local design is independent of the parameters of power lines and the only global quantity used in the synthesis algorithm is a scalar parameter Moreover, differently from the plug-and-play control scheme in [1] , the plug-in of a DGU does not require to update the controllers of neighboring DGUs Local control design is cast into a linear matrix inequality problem that, if infeasible, allows one to deny plug-in requests that might be dangerous for microgrid stability The proof of closed-loop stability of voltages exploits structured Lyapunov functions, the LaSalle invariance theorem and the properties of graph Laplacians Theoretical results are backed up by simulations in PSCAD

MIMO Sliding Mode Controller for Gait Exoskeleton Driven by Pneumatic Muscles

Abstract: In the past decade, pneumatic muscle (PM) actuated rehabilitation robotic devices have been widely researched, mainly due to the actuators’ intrinsic compliance and high power to weight ratio. However, the PMs are highly nonlinear and subject to hysteresis behavior. Hence, robust trajectory and compliance control are important to realize different training strategies and modes for improving the effectiveness of the rehabilitation robots. This paper presents a multi-input–multioutput sliding mode controller, which is developed to simultaneously control the angular trajectory and compliance of the knee joint mechanism of a gait rehabilitation exoskeleton. Experimental results indicate good multivariable tracking performance of this controller, which provides a good foundation for the further development of assist-as-needed training strategies in gait rehabilitation.