Showing papers on "Control theory published in 2015"

Control strategies for active lower extremity prosthetics and orthotics: a review

Abstract: Technological advancements have led to the development of numerous wearable robotic devices for the physical assistance and restoration of human locomotion. While many challenges remain with respect to the mechanical design of such devices, it is at least equally challenging and important to develop strategies to control them in concert with the intentions of the user. This work reviews the state-of-the-art techniques for controlling portable active lower limb prosthetic and orthotic (P/O) devices in the context of locomotive activities of daily living (ADL), and considers how these can be interfaced with the user’s sensory-motor control system. This review underscores the practical challenges and opportunities associated with P/O control, which can be used to accelerate future developments in this field. Furthermore, this work provides a classification scheme for the comparison of the various control strategies. As a novel contribution, a general framework for the control of portable gait-assistance devices is proposed. This framework accounts for the physical and informatic interactions between the controller, the user, the environment, and the mechanical device itself. Such a treatment of P/Os – not as independent devices, but as actors within an ecosystem – is suggested to be necessary to structure the next generation of intelligent and multifunctional controllers. Each element of the proposed framework is discussed with respect to the role that it plays in the assistance of locomotion, along with how its states can be sensed as inputs to the controller. The reviewed controllers are shown to fit within different levels of a hierarchical scheme, which loosely resembles the structure and functionality of the nominal human central nervous system (CNS). Active and passive safety mechanisms are considered to be central aspects underlying all of P/O design and control, and are shown to be critical for regulatory approval of such devices for real-world use. The works discussed herein provide evidence that, while we are getting ever closer, significant challenges still exist for the development of controllers for portable powered P/O devices that can seamlessly integrate with the user’s neuromusculoskeletal system and are practical for use in locomotive ADL.

Resilient, untethered soft robot

Abstract: A pneumatically powered, fully untethered mobile soft robot is described. Composites consisting of silicone elastomer, polyaramid fabric, and hollow glass microspheres were used to fabricate a sufficiently large soft robot to carry the miniature air compressors, battery, valves, and controller needed for autonomous operation. Fabrication techniques were developed to mold a 0.65 meter long soft body with modified Pneumatic network actuators capable of operating at the elevated pressures (up to 138 kPa) required to actuate the legs of the robot and hold payloads of up to 8 kg. The soft robot is safe to handle, and its silicone body is innately resilient to a variety of adverse environmental conditions including snow, puddles of water, direct (albeit limited) exposure to flames, and the crushing force of being run over by an automobile.

Distributed Cooperative Control of DC Microgrids

Abstract: A cooperative control paradigm is used to establish a distributed secondary/primary control framework for dc microgrids. The conventional secondary control, that adjusts the voltage set point for the local droop mechanism, is replaced by a voltage regulator and a current regulator. A noise-resilient voltage observer is introduced that uses neighbors’ data to estimate the average voltage across the microgrid. The voltage regulator processes this estimation and generates a voltage correction term to adjust the local voltage set point. This adjustment maintains the microgrid voltage level as desired by the tertiary control. The current regulator compares the local per-unit current of each converter with the neighbors’ and, accordingly, provides a second voltage correction term to synchronize per-unit currents and, thus, provide proportional load sharing. The proposed controller precisely handles the transmission line impedances. The controller on each converter communicates with only its neighbor converters on a communication graph. The graph is a sparse network of communication links spanned across the microgrid to facilitate data exchange. The global dynamic model of the microgrid is derived, and design guidelines are provided to tune the system's dynamic response. A low-voltage dc microgrid prototype is set up, where the controller performance, noise resiliency, link-failure resiliency, and the plug-and-play capability features are successfully verified.

Modeling of Soft Fiber-Reinforced Bending Actuators

Abstract: Soft fluidic actuators consisting of elastomeric matrices with embedded flexible materials are of particular interest to the robotics community because they are affordable and can be easily customized to a given application. However, the significant potential of such actuators is currently limited as their design has typically been based on intuition. In this paper, the principle of operation of these actuators is comprehensively analyzed and described through experimentally validated quasi-static analytical and finite-element method models for bending in free space and force generation when in contact with an object. This study provides a set of systematic design rules to help the robotics community create soft actuators by understanding how these vary their outputs as a function of input pressure for a number of geometrical parameters. Additionally, the proposed analytical model is implemented in a controller demonstrating its ability to convert pressure information to bending angle in real time. Such an understanding of soft multimaterial actuators will allow future design concepts to be rapidly iterated and their performance predicted, thus enabling new and innovative applications that produce more complex motions to be explored.

Kinematic and dynamic vehicle models for autonomous driving control design

Abstract: We study the use of kinematic and dynamic vehicle models for model-based control design used in autonomous driving In particular, we analyze the statistics of the forecast error of these two models by using experimental data In addition, we study the effect of discretization on forecast error We use the results of the first part to motivate the design of a controller for an autonomous vehicle using model predictive control (MPC) and a simple kinematic bicycle model The proposed approach is less computationally expensive than existing methods which use vehicle tire models Moreover it can be implemented at low vehicle speeds where tire models become singular Experimental results show the effectiveness of the proposed approach at various speeds on windy roads

Distributed Secondary Voltage and Frequency Restoration Control of Droop-Controlled Inverter-Based Microgrids

Abstract: In this paper, restorations for both voltage and frequency in the droop-controlled inverter-based islanded microgrid (MG) are addressed. A distributed finite-time control approach is used in the voltage restoration which enables the voltages at all the distributed generations (DGs) to converge to the reference value in finite time, and thus, the voltage and frequency control design can be separated. Then, a consensus-based distributed frequency control is proposed for frequency restoration, subject to certain control input constraints. Our control strategies are implemented on the local DGs, and thus, no central controller is required in contrast to existing control schemes proposed so far. By allowing these controllers to communicate with their neighboring controllers, the proposed control strategy can restore both voltage and frequency to their respective reference values while having accurate real power sharing, under a sufficient local stability condition established. An islanded MG test system consisting of four DGs is built in MATLAB to illustrate our design approach, and the results validate our proposed control strategy.

Nonlinear Robust Adaptive Tracking Control of a Quadrotor UAV Via Immersion and Invariance Methodology

Abstract: This paper presents a novel asymptotic tracking controller for an underactuated quadrotor unmanned aerial vehicle using the robust integral of the signum of the error (RISE) method and an immersion and invariance (I&I)-based adaptive control methodology The control system is decoupled into two parts: the inner loop for attitude control and the outer loop for position control The RISE approach is applied in the inner loop for disturbance rejection, whereas the I&I approach is chosen for the outer loop to compensate for the parametric uncertainties The asymptotic tracking of the time-varying 3-D position and the yaw motion reference trajectories is proven via the Lyapunov-based stability analysis and LaSalle's invariance theorem Real-time experiment results, which are performed on a hardware-in-the-loop simulation testbed, are presented to illustrate the performance of the proposed control scheme

Optimization‐based autonomous racing of 1:43 scale RC cars

Abstract: Summary This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two-level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real-time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high-level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real-time feasibility and high performance of optimization-based approaches for autonomous racing. Copyright © 2014 John Wiley & Sons, Ltd.

Model Predictive Control: MPC's Role in the Evolution of Power Electronics

Abstract: The evolution of power electronics and its control has been mainly driven by industry applications and influenced by the development achieved in several technologies, such as power semiconductors, converter topologies, automatic control, and analog and digital electronics. Digital signal processors (DSPs), in particular, have experienced an exponential development in processing power, which until now has not been fully exploited for control purposes in power converters and drive applications. Presently, the control system technology finds itself in a paradigm-changing tipping point, in which more demanding control goals, system flexibility, and functionalities required by emerging applications are driving the control system technology development, in addition to stabilization and robustness, which was the main focus in the past. This article walks briefly through the history of the mainstream power converter control scene, with an emphasis on the more recent introduction of predictive control, and gives a glimpse on the challenges and possibilities ahead. Special attention is given to finite control set (FCS)-model predictive control (MPC), because of its simplicity, flexibility, inherent adaptation to power electronic circuits and their discrete nature, both in the finite amount of switching states and the digital implementation with microprocessors.

Event-Based Leader-following Consensus of Multi-Agent Systems with Input Time Delay

Abstract: The event-based control strategy is an effective methodology for tackling the distributed control of multi-agent systems with limited on-board resources. This technical note focuses on event-based leader-following consensus for multi-agent systems described by general linear models and subject to input time delay between controller and actuator. For each agent, the controller updates are event-based and only triggered at its own event times. A necessary condition and two sufficient conditions on leader-following consensus are presented, respectively. It is shown that continuous communication between neighboring agents can be avoided and the Zeno-behavior of triggering time sequences is excluded. A numerical example is presented to illustrate the effectiveness of the obtained theoretical results.

Accurate Reactive Power Sharing in an Islanded Microgrid Using Adaptive Virtual Impedances

Abstract: In this paper, a reactive power sharing strategy that employs communication and the virtual impedance concept is proposed to enhance the accuracy of reactive power sharing in an islanded microgrid. Communication is utilized to facilitate the tuning of adaptive virtual impedances in order to compensate for the mismatch in voltage drops across feeders. Once the virtual impedances are tuned for a given load operating point, the strategy will result in accurate reactive power sharing even if communication is disrupted. If the load changes while communication is unavailable, the sharing accuracy is reduced, but the proposed strategy will still outperform the conventional droop control method. In addition, the reactive power sharing accuracy based on the proposed strategy is immune to the time delay in the communication channel. The sensitivity of the tuned controller parameters to changes in the system operating point is also explored. The control strategy is straightforward to implement and does not require knowledge of the feeder impedances. The feasibility and effectiveness of the proposed strategy are validated using simulation and experimental results from a 2-kVA microgrid.

A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems

Abstract: Maximum Power Point Tracking (MPPT) methods are used in photovoltaic (PV) systems to continually maximize the PV array output power which generally depends on solar radiation and cell temperature. MPPT methods can be roughly classified into two categories: there are conventional methods, like the Perturbation and Observation (P&O) method and the Incremental Conductance (IncCond) method and advanced methods, such as, fuzzy logic (FL) based MPPT method. This paper presents a survey of these methods in order to analyze, simulate, and evaluate a PV power supply system under varying meteorological conditions. Simulation results, obtained using MATLAB/Simulink, show that static and dynamic performances of fuzzy MPPT controller are better than those of conventional techniques based controller.

A Multiagent-Based Consensus Algorithm for Distributed Coordinated Control of Distributed Generators in the Energy Internet

Abstract: With the bidirectional power flow provided by the Energy Internet, various methods are promoted to improve and increase the energy utilization between Energy Internet and main grid (MG). This paper proposes a novel distributed coordinated controller combined with a multiagent-based consensus algorithm, which is applied to distributed generators in the Energy Internet. Then, the decomposed tasks, models, and information flow of the proposed method are analyzed. The proposed coordinated controller installed between the Energy Internet and MG keeps voltage angles and amplitudes consensus, while providing accurate power-sharing and minimizing circulating currents. Finally, the Energy Internet can be integrated into the MG seamlessly if necessary. Hence, the Energy Internet can be operated as a spinning reserve system. Simulation results are provided to show the effectiveness of the proposed controller in an Energy Internet.

A Supervisory Energy Management Control Strategy in a Battery/Ultracapacitor Hybrid Energy Storage System

Abstract: One of the major challenges in a battery/ultracapacitor hybrid energy storage system (HESS) is to design a supervisory controller for real-time implementation that can yield good power split performance. This paper presents the design of a supervisory energy management strategy that optimally addresses this issue. In this work, a multiobjective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. In this HESS energy management problem, a detailed dc–dc converter model is considered to include both the conduction losses and the switching losses. The optimization problem is numerically solved for various drive cycle data sets using dynamic programming (DP). Trained using the DP results, an effective and intelligent online implementation of the optimal power split is realized based on neural networks (NNs). The proposed online intelligent energy management controller is applied to a midsize electric vehicles (EV). A rule-based control strategy is also implemented in this work for comparison with the proposed energy management strategy. The proposed online energy management controller effectively splits the load demand and achieves excellent result of the energy efficiency. It is also estimated that the proposed online energy management controller can extend the battery life by over 60%, which greatly outperforms the rule-based control strategy.

Synchronous Generator Emulation Control Strategy for Voltage Source Converter (VSC) Stations

Abstract: The voltage source converter (VSC) station is playing a more important role in modern power systems, but the dynamic behavior of the VSC station is quite different from that of the synchronous generator. This paper presents the synchronous generator emulation control (SGEC) strategy for the VSC-HVDC station. The SGEC strategy is divided into the inner control loop and the outer control loop. The inner controller is developed for fast current and voltage regulations. An inertia element is introduced into the frequency-power droop to determine the command reference of the frequency, and the inertia response and the primary frequency regulation are emulated. In addition, the secondary frequency regulation can be achieved by modulating the scheduled power in the SGEC strategy. The time-domain simulation results demonstrate the VSC station with the proposed control strategy can provide desired frequency support to a low-inertia grid. Therefore, the SGEC strategy provides a simple and practical solution for the VSC station to emulate the behavior of a synchronous generator.

Control of Nonlinear Networked Systems With Packet Dropouts: Interval Type-2 Fuzzy Model-Based Approach

Abstract: In this paper, the problem of fuzzy control for nonlinear networked control systems with packet dropouts and parameter uncertainties is studied based on the interval type-2 fuzzy-model-based approach. In the control design, the intermittent data loss existing in the closed-loop system is taken into account. The parameter uncertainties can be represented and captured effectively via the membership functions described by lower and upper membership functions and relative weighting functions. A novel fuzzy state-feedback controller is designed to guarantee the resulting closed-loop system to be stochastically stable with an optimal performance. Furthermore, to make the controller design more flexible, the designed controller does not need to share membership functions and amount of fuzzy rules with the model. Some simulation results are provided to demonstrate the effectiveness of the proposed results.

Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle

Abstract: This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

An Enhanced Islanding Microgrid Reactive Power, Imbalance Power, and Harmonic Power Sharing Scheme

Abstract: To address inaccurate power sharing problems in autonomous islanding microgrids, an enhanced droop control method through online virtual impedance adjustment is proposed. First, a term associated with DG reactive power, imbalance power, or harmonic power is added to the conventional real power-frequency droop control. The transient real power variations caused by this term are captured to realize DG series virtual impedance tuning. With the regulation of DG virtual impedance at fundamental positive sequence, fundamental negative sequence, and harmonic frequencies, an accurate power sharing can be realized at the steady state. In order to activate the compensation scheme in multiple DG units in a synchronized manner, a low-bandwidth communication bus is adopted to send the compensation command from a microgrid central controller to DG unit local controllers, without involving any information from DG unit local controllers. The feasibility of the proposed method is verified by simulated and experimental results from a low-power three-phase microgrid prototype.

ADRC With Adaptive Extended State Observer and its Application to Air–Fuel Ratio Control in Gasoline Engines

Abstract: This paper proposes the adaptive extended state observer (AESO)-based active disturbance rejection control (ADRC) to deal with the uncertainties, both in the plant and in the sensors. The gain of ESO is automatically timely tuned to reduce the estimation errors of both states and “total disturbance” against the measurement noise. Furthermore, the satisfactory performance of the closed-loop system is achieved by compensation for uncertainties. This novel controller is applied to the air–fuel ratio (AFR) control of gasoline engine, which has large nonlinear uncertainties due to the unknown speed change, fuel film dynamics, etc. In addition, the measurement of AFR is polluted by sensor noise. The experimental results demonstrate that the proposed controller can ensure high deviation precision of AFR despite both uncertain dynamics and measurement noise. Moreover, the experimental comparison validates the effectiveness of the AESO's gain by which the performance of ADRC on mitigating uncertainties can be improved.

Energy Sharing Control Scheme for State-of-Charge Balancing of Distributed Battery Energy Storage System

Abstract: This paper presents an energy sharing state-of-charge (SOC) balancing control scheme based on a distributed battery energy storage system architecture where the cell balancing system and the dc bus voltage regulation system are combined into a single system. The battery cells are decoupled from one another by connecting each cell with a small lower power dc–dc power converter. The small power converters are utilized to achieve both SOC balancing between the battery cells and dc bus voltage regulation at the same time. The battery cells' SOC imbalance issue is addressed from the root by using the energy sharing concept to automatically adjust the discharge/charge rate of each cell while maintaining a regulated dc bus voltage. Consequently, there is no need to transfer the excess energy between the cells for SOC balancing. The theoretical basis and experimental prototype results are provided to illustrate and validate the proposed energy sharing controller.

Neural-Based Adaptive Output-Feedback Control for a Class of Nonstrict-Feedback Stochastic Nonlinear Systems

Abstract: In this paper, we consider the problem of observer-based adaptive neural output-feedback control for a class of stochastic nonlinear systems with nonstrict-feedback structure. To overcome the design difficulty from the nonstrict-feedback structure, a variable separation approach is introduced by using the monotonically increasing property of system bounding functions. On the basis of the state observer, and by combining the adaptive backstepping technique with radial basis function neural networks’ universal approximation capability, an adaptive neural output feedback control algorithm is presented. It is shown that the proposed controller can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in the sense of mean quartic value. Simulation results are provided to show the effectiveness of the proposed control scheme.

Synchronization of Complex Networks via Aperiodically Intermittent Pinning Control

Abstract: In this note, synchronization for linearly coupled network is investigated by pinning a simple aperiodically intermittent controller. Nodes' dynamical behaviors in the network are described by continuous-time ordinary differential equations. Some sufficient conditions to guarantee global synchronization are presented. Furthermore, an adaptive algorithm is designed for the pinning control gain and its validity is also proved rigorously. Finally, numerical simulations are given to demonstrate the correctness of obtained results.

Human–Robot Interaction Control of Rehabilitation Robots With Series Elastic Actuators

Abstract: Rehabilitation robots, by necessity, have direct physical interaction with humans. Physical interaction affects the controlled variables and may even cause system instability. Thus, human–robot interaction control design is critical in rehabilitation robotics research. This paper presents an interaction control strategy for a gait rehabilitation robot. The robot is driven by a novel compact series elastic actuator, which provides intrinsic compliance and backdrivablility for safe human–robot interaction. The control design is based on the actuator model with consideration of interaction dynamics. It consists mainly of human interaction compensation, friction compensation, and is enhanced with a disturbance observer. Such a control scheme enables the robot to achieve low output impedance when operating in human-in-charge mode and achieve accurate force tracking when operating in force control mode. Due to the direct physical interaction with humans, the controller design must also meet the stability requirement. A theoretical proof is provided to show the guaranteed stability of the closed-loop system under the proposed controller. The proposed design is verified with an ankle robot in walking experiments. The results can be readily extended to other rehabilitation and assistive robots driven with compliant actuators without much difficulty.

Game theoretic approach for predictive lane-changing and car-following control

Abstract: This contribution puts forward a receding horizon control approach for automated driving systems, where tactical-level lane change decisions and control-level accelerations are jointly evaluated under a central mathematical framework. The key idea is that controlled vehicles predictively determine discrete desired lane sequences and continuous accelerations to minimise a cost function reflecting undesirable future situations. The interactions between controlled vehicles and surrounding vehicles are captured in the cost function. The approach is flexible in terms of application to controller design for both non-cooperative control systems where controlled vehicles only optimise their own cost and cooperative control systems where controlled vehicles coordinate their decisions to optimise the collective cost. To determine the controller behaviour, the problem is formulated as a differential game where controlled vehicles make decisions based on the expected behaviour of other vehicles. The control decisions are updated at regular frequency, using the newest information regarding the state of controlled vehicles and surrounding vehicles available. A problem decomposition technique is employed to reduce the dimensionality of the original problem by introducing a finite number of sub-problems and an iterative algorithm based on Pontryagin’s Principle is used to solve sub-problems efficiently. The proposed controller performance is demonstrated via numerical examples. The results show that the proposed approach can produce efficient lane-changing manoeuvres while obeying safety and comfort requirements. Particularly, the approach generates optimal lane change decisions in the predicted future, including strategic overtaking, cooperative merging and selecting a safe gap.

A review on hybrid renewable energy systems

Abstract: The world is witnessing a change-over from its present centralized generation to a future with greater share of distributed generation. Hybrid energy systems are inter-connected with wind power, photovoltaic power, fuel cell and micro-turbine generator to generate power to local load and connecting to grid/micro-grids that decrease the dependence on fossil fuels. The hybrid system is a better option for construction of modern electrical grids that includes economic, environmental and social benefits. An overview of different distributed generation technologies has been presented. This paper puts forward a comprehensive review of optimal sizing, energy management, operating and control strategies and integration of different renewable energy sources to constitute a hybrid system. The feasibility of the different controllers such as microcontroller, proportional integral controller, hysteresis controller and fuzzy controller are presented. The controller is a closed loop feedback mechanism used for power regulation which achieves zero steady state error and the output signal generated from the controller produces desired output response.

Reactive synthesis from signal temporal logic specifications

Abstract: We present a counterexample-guided inductive synthesis approach to controller synthesis for cyber-physical systems subject to signal temporal logic (STL) specifications, operating in potentially adversarial nondeterministic environments. We encode STL specifications as mixed integer-linear constraints on the variables of a discrete-time model of the system and environment dynamics, and solve a series of optimization problems to yield a satisfying control sequence. We demonstrate how the scheme can be used in a receding horizon fashion to fulfill properties over unbounded horizons, and present experimental results for reactive controller synthesis for case studies in building climate control and autonomous driving.