Showing papers on "Control theory published in 2011"

Controllability of complex networks

Abstract: The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent control that can guide the system's entire dynamics. We apply these tools to several real networks, finding that the number of driver nodes is determined mainly by the network's degree distribution. We show that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the high-degree nodes.

Minimum snap trajectory generation and control for quadrotors

Abstract: We address the controller design and the trajectory generation for a quadrotor maneuvering in three dimensions in a tightly constrained setting typical of indoor environments. In such settings, it is necessary to allow for significant excursions of the attitude from the hover state and small angle approximations cannot be justified for the roll and pitch. We develop an algorithm that enables the real-time generation of optimal trajectories through a sequence of 3-D positions and yaw angles, while ensuring safe passage through specified corridors and satisfying constraints on velocities, accelerations and inputs. A nonlinear controller ensures the faithful tracking of these trajectories. Experimental results illustrate the application of the method to fast motion (5–10 body lengths/second) in three-dimensional slalom courses.

Simulation and Hardware Implementation of Incremental Conductance MPPT With Direct Control Method Using Cuk Converter

Abstract: This paper presents simulation and hardware implementation of incremental conductance (IncCond) maximum power point tracking (MPPT) used in solar array power systems with direct control method. The main difference of the proposed system to existing MPPT systems includes elimination of the proportional-integral control loop and investigation of the effect of simplifying the control circuit. Contributions are made in several aspects of the whole system, including converter design, system simulation, controller programming, and experimental setup. The resultant system is capable of tracking MPPs accurately and rapidly without steady-state oscillation, and also, its dynamic performance is satisfactory. The IncCond algorithm is used to track MPPs because it performs precise control under rapidly changing atmospheric conditions. MATLAB and Simulink were employed for simulation studies, and Code Composer Studio v3.1 was used to program a TMS320F2812 digital signal processor. The proposed system was developed and tested successfully on a photovoltaic solar panel in the laboratory. Experimental results indicate the feasibility and improved functionality of the system.

Design and Analysis of the Droop Control Method for Parallel Inverters Considering the Impact of the Complex Impedance on the Power Sharing

Abstract: This paper investigates the characteristics of the active and reactive power sharing in a parallel inverters system under different system impedance conditions. The analyses conclude that the conventional droop method cannot achieve efficient power sharing for the case of a system with complex impedance condition. To achieve the proper power balance and minimize the circulating current in the different impedance situations, a novel droop controller that considers the impact of complex impedance is proposed in this paper. This controller can simplify the coupled active and reactive power relationships, which are caused by the complex impedance in the parallel system. In addition, a virtual complex impedance loop is included in the proposed controller to minimize the fundamental and harmonic circulating current that flows in the parallel system. Compared to the other methods, the proposed controller can achieve accurate power sharing, offers efficient dynamic performance, and is more adaptive to different line impedance situations. Simulation and experimental results are presented to prove the validity and the improvements achieved by the proposed controller.

Finite-Time Attitude Tracking Control of Spacecraft With Application to Attitude Synchronization

Abstract: This note investigates the finite-time attitude control problems for a single spacecraft and multiple spacecraft. First of all, a finite-time controller is designed to solve finite-time attitude tracking problem for a single spacecraft. Rigorous proof shows that the desired attitude can be tracked in finite time in the absence of disturbances. In the presence of disturbances, the tracking errors can reach a region around the origin in finite time. Then, based on the neighbor rule, a distributed finite-time attitude control law is proposed for a group of spacecraft with a leader-follower architecture. Under the finite-time control law, the attitude synchronization can be achieved in finite time.

Fuzzy-Logic-Control Approach of a Modified Hill-Climbing Method for Maximum Power Point in Microgrid Standalone Photovoltaic System

Abstract: A new fuzzy-logic controller for maximum power point tracking of photovoltaic (PV) systems is proposed. PV modeling is discussed. Conventional hill-climbing maximum power-point tracker structures and features are investigated. The new controller improves the hill-climbing search method by fuzzifying the rules of such techniques and eliminates their drawbacks. Fuzzy-logic-based hill climbing offers fast and accurate converging to the maximum operating point during steady-state and varying weather conditions compared to conventional hill climbing. Simulation and experimentation results are provided to demonstrate the validity of the proposed fuzzy-logic-based controller.

Data-Driven Robust Approximate Optimal Tracking Control for Unknown General Nonlinear Systems Using Adaptive Dynamic Programming Method

Abstract: In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

System and method for providing closed loop infusion formulation delivery

Abstract: A system and method for providing closed loop infusion formulation delivery which accurately calculates a delivery amount based on a sensed biological state by adjusting an algorithm's programmable control parameters. The algorithm calculates a delivery amount having proportional, derivative, and basal rate components. The control parameters may be adjusted in real time to compensate for changes in a sensed biological state that may result from daily events. Safety limits on the delivery amount may be included in the algorithm. The algorithm may be executed by a computing element within a process controller for controlling closed loop infusion formulation delivery. The biological state is sensed by a sensing device which provides a signal to the controller. The controller calculates an infusion formulation delivery amount based on the signal and sends commands to an infusion formulation delivery device which delivers an amount of infusion formulation determined by the commands.

Robotic end effector system and method

Abstract: A robotic end effector system and method having a plurality of end effectors which are selectively suitable for particular applications on a workpiece. The end effectors include a resident controller adapted to execute tasks specific to the end effector and are rapidly attachable and removable from the robot for easy change over to different workpieces.

An Improved Maximum Power Point Tracking for Photovoltaic Grid-Connected Inverter Based on Voltage-Oriented Control

Abstract: In this paper, an improved maximum power point (MPP) tracking (MPPT) with better performance based on voltage-oriented control (VOC) is proposed to solve a fast-changing irradiation problem. In VOC, a cascaded control structure with an outer dc link voltage control loop and an inner current control loop is used. The currents are controlled in a synchronous orthogonal d,q frame using a decoupled feedback control. The reference current of proportional-integral (PI) d-axis controller is extracted from the dc-side voltage regulator by applying the energy-balancing control. Furthermore, in order to achieve a unity power factor, the q-axis reference is set to zero. The MPPT controller is applied to the reference of the outer loop control dc voltage photovoltaic (PV). Without PV array power measurement, the proposed MPPT identifies the correct direction of the MPP by processing the d-axis current reflecting the power grid side and the signal error of the PI outer loop designed to only represent the change in power due to the changing atmospheric conditions. The robust tracking capability under rapidly increasing and decreasing irradiance is verified experimentally with a PV array emulator. Simulations and experimental results demonstrate that the proposed method provides effective, fast, and perfect tracking.

FPGAs in Industrial Control Applications

Abstract: The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.

Stability Analysis of Networked Control Systems Using a Switched Linear Systems Approach

Abstract: In this paper, we study the stability of networked control systems (NCSs) that are subject to time-varying transmission intervals, time-varying transmission delays, and communication constraints. Communication constraints impose that, per transmission, only one node can access the network and send its information. The order in which nodes send their information is orchestrated by a network protocol, such as, the Round-Robin (RR) and the Try-Once-Discard (TOD) protocol. In this paper, we generalize the mentioned protocols to novel classes of so-called “periodic” and “quadratic” protocols. By focusing on linear plants and controllers, we present a modeling framework for NCSs based on discrete-time switched linear uncertain systems. This framework allows the controller to be given in discrete time as well as in continuous time. To analyze stability of such systems for a range of possible transmission intervals and delays, with a possible nonzero lower bound, we propose a new procedure to obtain a convex overapproximation in the form of a polytopic system with norm-bounded additive uncertainty. We show that this approximation can be made arbitrarily tight in an appropriate sense. Based on this overapproximation, we derive stability results in terms of linear matrix inequalities (LMIs). We illustrate our stability analysis on the benchmark example of a batch reactor and show how this leads to tradeoffs between different protocols, allowable ranges of transmission intervals and delays. In addition, we show that the exploitation of the linearity of the system and controller leads to a significant reduction in conservatism with respect to existing approaches in the literature.

Model Predictive Multi-Objective Vehicular Adaptive Cruise Control

Abstract: This paper presents a novel vehicular adaptive cruise control (ACC) system that can comprehensively address issues of tracking capability, fuel economy and driver desired response. A hierarchical control architecture is utilized in which a lower controller compensates for nonlinear vehicle dynamics and enables tracking of desired acceleration. The upper controller is synthesized under the framework of model predictive control (MPC) theory. A quadratic cost function is developed that considers the contradictions between minimal tracking error, low fuel consumption and accordance with driver dynamic car-following characteristics while driver longitudinal ride comfort, driver permissible tracking range and rear-end safety are formulated as linear constraints. Employing a constraint softening method to avoid computing infeasibility, an optimal control law is numerically calculated using a quadratic programming algorithm. Detailed simulations with a heavy duty truck show that the developed ACC system provides significant benefits in terms of fuel economy and tracking capability while at the same time also satisfying driver desired car following characteristics.

Synchronization of Networks of Nonidentical Euler-Lagrange Systems With Uncertain Parameters and Communication Delays

Abstract: This paper addresses the problem of synchronizing networks of nonidentical, nonlinear dynamical systems described by Euler-Lagrange equations, which are assumed fully-actuated, with their states available for measurement, but with unknown parameters. The only assumption made on the communication graph is that it is connected. Moreover, the communication is subject to constant time delays, which are also unknown. The main result of the paper is the construction of an adaptive controller that achieves global full-state synchronization, i.e., the difference between the agents positions and velocities asymptotically converges to zero. If a desired trajectory for all systems is given, a slight modification to the proposed scheme achieves also full-state synchronization. Simulations using a ten robot manipulator network are used to illustrate the performance of the proposed schemes.

Centralized control for optimizing microgrids operation

Abstract: Microgrids are Low Voltage distribution networks comprising various distributed generators (DG), storage devices and controllable loads that can operate either interconnected or isolated from the main distribution grid as a controlled entity. This paper describes the operation of a Central Controller for Microgrids. The controller aims to optimize the operation of the Microgrid during interconnected operation, i.e. maximize its value by optimizing production of the local DGs and power exchanges with the main distribution grid. Two market policies are assumed including Demand Side Bidding options for controllable loads. The developed optimization algorithms are applied on a typical LV study case network operating under various market policies and assuming realistic spot market prices and DG bids reflecting realistic operational costs. The effects on the Microgrid and the Distribution network operation are presented and discussed.

Reinforcement Learning for Partially Observable Dynamic Processes: Adaptive Dynamic Programming Using Measured Output Data

Abstract: Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q-learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.

Practical String Stability of Platoon of Adaptive Cruise Control Vehicles

Abstract: In this paper, the practical string stability of both homogeneous and heterogeneous platoons of adaptive cruise control (ACC) vehicles, which apply the constant time headway spacing policy, is investigated by considering the parasitic time delays and lags of the actuators and sensors when building the vehicle longitudinal dynamics model. The proposed control law based on the sliding-mode controller can guarantee both homogeneous and heterogeneous string stability, if the control parameters and system parameters meet certain requirements. The analysis of the negative effect of the parasitic time delays and lags on the string stability indicates that the negative effect of the time delays is larger than that of the time lags. This paper provides a practical means to evaluate the ACC systems applying the sliding-mode controller and provides a reasonable proposal to design the ACC controller from the perspective of the practical string stability.

Finite-Time Attitude Tracking Control for Spacecraft Using Terminal Sliding Mode and Chebyshev Neural Network

Abstract: A finite-time attitude tracking control scheme is proposed for spacecraft using terminal sliding mode and Chebyshev neural network (NN) (CNN). The four-parameter representations (quaternion) are used to describe the spacecraft attitude for global representation without singularities. The attitude state (i.e., attitude and velocity) error dynamics is transformed to a double integrator dynamics with a constraint on the spacecraft attitude. With consideration of this constraint, a novel terminal sliding manifold is proposed for the spacecraft. In order to guarantee that the output of the NN used in the controller is bounded by the corresponding bound of the approximated unknown function, a switch function is applied to generate a switching between the adaptive NN control and the robust controller. Meanwhile, a CNN, whose basis functions are implemented using only desired signals, is introduced to approximate the desired nonlinear function and bounded external disturbances online, and the robust term based on the hyperbolic tangent function is applied to counteract NN approximation errors in the adaptive neural control scheme. Most importantly, the finite-time stability in both the reaching phase and the sliding phase can be guaranteed by a Lyapunov-based approach. Finally, numerical simulations on the attitude tracking control of spacecraft in the presence of an unknown mass moment of inertia matrix, bounded external disturbances, and control input constraints are presented to demonstrate the performance of the proposed controller.

Method and apparatus for controlling an infusion pump or the like

Abstract: A method and apparatus for controlling IV medication delivery and monitoring, the method including providing information tags on IV bags that specify delivery parameters, obtaining delivery parameters for at least one bag, associating a controller with a particular patient, comparing patient information for the particular patient with the delivery parameters, determining the efficacy of delivering the medicant to the patient and affecting pump control as a function of the comparison. The method also includes various timing rules and other verification procedures.

Human-Inspired Robotic Grasp Control With Tactile Sensing

Abstract: We present a novel robotic grasp controller that allows a sensorized parallel jaw gripper to gently pick up and set down unknown objects once a grasp location has been selected. Our approach is inspired by the control scheme that humans employ for such actions, which is known to centrally depend on tactile sensation rather than vision or proprioception. Our controller processes measurements from the gripper's fingertip pressure arrays and hand-mounted accelerometer in real time to generate robotic tactile signals that are designed to mimic human SA-I, FA-I, and FA-II channels. These signals are combined into tactile event cues that drive the transitions between six discrete states in the grasp controller: Close, Load, Lift and Hold, Replace, Unload, and Open. The controller selects an appropriate initial grasping force, detects when an object is slipping from the grasp, increases the grasp force as needed, and judges when to release an object to set it down. We demonstrate the promise of our approach through implementation on the PR2 robotic platform, including grasp testing on a large number of real-world objects.

Receding Horizon Control

Abstract: In this article we have shown that receding horizon control offers a straightforward method for designing feedback controllers that deliver good performance while respecting complex constraints. A designer specifies the RHC controller by specifying the objective, constraints, prediction method, and horizon, each of which has a natural choice suggested directly by the application. In more traditional approaches, such as PID control, a designer tunes the controller coefficients, often using trial and error, to handle the objectives and constraints indirectly. In contrast, RHC con trollers can often obtain good performance with little tuning. In addition to the straightforward design process, we have seen that RHC controllers can be implemented in real time at kilohertz sampling rates. These speeds are useful for both real-time implementation of the controller as well as rapid Monte Carlo simulation for design and testing purposes. Thus, receding horizon control can no longer be considered a slow, computationally intensive policy. Indeed, RHC can be applied to a wide range of control problems, including applications involving fast dynamics.

Determining the Optimal Window Length for Pattern Recognition-Based Myoelectric Control: Balancing the Competing Effects of Classification Error and Controller Delay

Abstract: Pattern recognition-based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis using pattern recognition of electromyogram (EMG) signals. Classification error and controller delay were varied by training different classifiers with a variety of analysis window lengths ranging from 50 to 550 ms and either two or four EMG input channels. Offline analysis showed that classification error decreased with longer window lengths ( p <; 0.01). Real-time controllability was evaluated with the target achievement control (TAC) test, which prompted users to maneuver the virtual prosthesis into various target postures. The results indicated that user performance improved with lower classification error (p <; 0.01 ) and was reduced with longer controller delay ( p <; 0.01), as determined by the window length. Therefore, both of these effects should be considered when choosing a window length; it may be beneficial to increase the window length if this results in a reduced classification error, despite the corresponding increase in controller delay. For the system employed in this study, the optimal window length was found to be between 150 and 250 ms , which is within acceptable controller delays for conventional multistate amplitude controllers.

Quaternion-Based Hybrid Control for Robust Global Attitude Tracking

Abstract: It is well known that controlling the attitude of a rigid body is subject to topological constraints. We illustrate, with examples, the problems that arise when using continuous and (memoryless) discontinuous quaternion-based state-feedback control laws for global attitude stabilization. We propose a quaternion-based hybrid feedback scheme that solves the global attitude tracking problem in three scenarios: full state measurements, only measurements of attitude, and measurements of attitude with angular velocity measurements corrupted by a constant bias. In each case, the hybrid feedback is dynamic and incorporates hysteresis-based switching using a single binary logic variable for each quaternion error state. When only attitude measurements are available or the angular rate is corrupted by a constant bias, the proposed controller is observer-based and incorporates an additional quaternion filter and bias observer. The hysteresis mechanism enables the proposed scheme to simultaneously avoid the “unwinding phenomenon” and sensitivity to arbitrarily small measurement noise that is present in discontinuous feedbacks. These properties are shown using a general framework for hybrid systems, and the results are demonstrated by simulation.

Adaptive Sliding Mode Control for Attitude Stabilization With Actuator Saturation

Abstract: The problem of attitude stabilization for a spacecraft system which is nonlinear in dynamics with inertia uncertainty and external disturbance is investigated in this paper. An adaptive law is applied to estimate the disturbances, where a sliding mode controller is designed to force the state variables of the closed-loop system to converge to the origin. Then, the spacecraft system subjected to control constraints is further considered, and another adaptive sliding mode control law is designed to achieve the attitude stabilization. No prior knowledge of inertia moment is required for both of the proposed adaptive control laws, which implies that the designed control schemes can be applied in spacecraft systems with a large parametric uncertainty existing in inertial matrix or even in unknown inertial matrix. Also, simulation results are presented to illustrate the effectiveness of the control strategies.

Tube-based robust nonlinear model predictive control

Abstract: This paper extends tube-based model predictive control of linear systems to achieve robust control of nonlinear systems subject to additive disturbances. A central or reference trajectory is determined by solving a nominal optimal control problem. The local linear controller, employed in tube-based robust control of linear systems, is replaced by an ancillary model predictive controller that forces the trajectories of the disturbed system to lie in a tube whose center is the reference trajectory thereby enabling robust control of uncertain nonlinear systems to be achieved. Copyright © 2011 John Wiley & Sons, Ltd.

Nonlinear Control of a Variable-Speed Wind Turbine Using a Two-Mass Model

Abstract: The paper presents a nonlinear approach, using a two-mass model and a wind speed estimator, for variable-speed wind turbine (WT) control. The use of a two-mass model is motivated by the need to deal with flexible modes induced by the low-speed shaft stiffness. The main objective of the proposed controllers is the wind power capture optimization while limiting transient loads on the drive-train components. This paper starts by an adaptation of some existing control strategies. However, their performance are weak, as the dynamics aspects of the wind and aeroturbine are not taken into consideration. In order to bring some improvements, nonlinear static and dynamic state feedback controllers, with a wind speed estimator, are then proposed. Concerning the wind speed estimator, the idea behind this is to exploit the WT dynamics by itself as a measurement device. All these methods have been first tested and validated using an aeroelastic WT simulator. A comparative study between the proposed controllers is performed. The results show better performance for the nonlinear dynamic controller with estimator in comparison with the adapted existing methods.