Showing papers presented at "American Control Conference in 2009"

Predictive energy management of a power-split hybrid electric vehicle

Abstract: In this paper, a Model Predictive Control (MPC) strategy is developed for the first time to solve the optimal energy management problem of power-split hybrid electric vehicles. A power-split hybrid combines the advantages of series and parallel hybrids by utilizing two electric machines and a combustion engine. Because of its many modes of operation, modeling a power-split configuration is complex and devising a near-optimal power management strategy is quite challenging. To systematically improve the fuel economy of a power-split hybrid, we formulate the power management problem as a nonlinear optimization problem. The nonlinear powertrain model and the constraints are linearized at each sample time and a receding horizon linear MPC strategy is employed to determine the power split ratio based on the updated model. Simulation results over multiple driving cycles indicate better fuel economy over conventional strategies can be achieved. In addition the proposed algorithm is causal and has the potential for real-time implementation.

Fractional order control - A tutorial

Abstract: Many real dynamic systems are better characterized using a non-integer order dynamic model based on fractional calculus or, differentiation or integration of non-integer order. Traditional calculus is based on integer order differentiation and integration. The concept of fractional calculus has tremendous potential to change the way we see, model, and control the nature around us. Denying fractional derivatives is like saying that zero, fractional, or irrational numbers do not exist. In this paper, we offer a tutorial on fractional calculus in controls. Basic definitions of fractional calculus, fractional order dynamic systems and controls are presented first. Then, fractional order PID controllers are introduced which may make fractional order controllers ubiquitous in industry. Additionally, several typical known fractional order controllers are introduced and commented. Numerical methods for simulating fractional order systems are given in detail so that a beginner can get started quickly. Discretization techniques for fractional order operators are introduced in some details too. Both digital and analog realization methods of fractional order operators are introduced. Finally, remarks on future research efforts in fractional order control are given.

ECMS as a realization of Pontryagin's minimum principle for HEV control

Abstract: An analytical derivation of the Equivalent Consumption Minimization Strategy (ECMS) for energy management of hybrid electric vehicles (HEVs) is presented, based on Pontryagin's minimum principle. The derivation is obtained using a generic formulation of the energy management problem in HEVs and is valid for any powertrain architecture. Simulation results obtained for a series HEV are also provided.

A tutorial on the dynamics and control of wind turbines and wind farms

Abstract: Wind energy is currently the fastest-growing energy source in the world, with a concurrent growth in demand for the expertise of engineers and researchers in the wind energy field. There are still many unsolved challenges in expanding wind power, and there are numerous problems of interest to systems and control researchers. In this paper, we first review the basic structure of wind turbines and then describe wind turbine control systems and control loops. Of great interest are the generator torque and blade pitch control systems, where significant performance improvements are achievable with more advanced systems and control research. We describe recent developments in advanced controllers for wind turbines and wind farms, and we also outline many open problems in the areas of modeling and control of wind turbines.

Control of wind turbines: Past, present, and future

Abstract: We review the objectives and techniques used in the control of horizontal axis wind turbines at the individual turbine level, where controls are applied to the turbine blade pitch and generator. The turbine system is modeled as a flexible structure operating in the presence of turbulent wind disturbances. Some overview of the various stages of turbine operation and control strategies used to maximize energy capture in below rated wind speeds is given, but emphasis is on control to alleviate loads when the turbine is operating at maximum power. After reviewing basic turbine control objectives, we provide an overview of the common basic linear control approaches and then describe more advanced control architectures and why they may provide significant advantages.

A rule-based energy management strategy for Plug-in Hybrid Electric Vehicle (PHEV)

Abstract: Hybrid Electric Vehicles (HEV) combine the power from an electric motor with that from an internal combustion engine to propel the vehicle. The HEV electric motor is typically powered by a battery pack through power electronics. The HEV battery is recharged either by the engine or from regenerative braking. The electric drive mode is very limited for an HEV due to the limited battery power. A more powerful battery will increase the electric drive range of the vehicle, thus improving fuel economy. However, the battery will need to be recharged using an electric outlet since the regenerative braking and limited engine usage will not be sufficient to fully recharge the larger battery pack. In this paper, a rule-based energy management strategy for a Plug-in Hybrid Electric Vehicle (PHEV) is presented. Since large amount of electric energy is stored in the battery from the electric power grid, the fuel consumption is reduced significantly as compared with HEV counterpart. The proposed energy management strategy is implemented on a PHEV model in ADVISOR and the model is then simulated for a number of predefined drive cycles. The proposed PHEV algorithm results are compared with those for HEV with similar battery capacity as PHEV.

Dynamic dual decomposition for distributed control

Abstract: We show how dynamic price mechanisms can be used for decomposition and distributed optimization of feedback systems. A classical method to handle large scale optimization problems is dual decomposition, where the coupling between sub-problems is relaxed using Lagrange multipliers. These variables can be interpreted as prices in a market mechanism serving to achieve mutual agreement between solutions of the sub-problems. In this paper, the same idea is used for decomposition of feedback systems, with dynamics in both decision variables and prices. We show how the prices can be used for a decentralized test, to verify that the global feedback system stays within a prespecified distance from optimality.

Consensus in networked multi-agent systems via sampled control: Switching topology case

Abstract: In this paper, consensus problems of continuous-time networked multi-agent systems via sampled control are investigated. The sampled control protocols are induced from continuous-time linear consensus protocol by using periodic sampling technology and zero-order hold circuit. Two cases are considered: 1) networks without sampling delay; 2) networks with sampling delay. For each case, an algebraic-type necessary and sufficient condition is obtained under which consensus is achieved. Some numerical simulations are presented, which are consistent with our theoretical results.

Wind farm control: Addressing the aerodynamic interaction among wind turbines

Abstract: Wind farms help reduce the average wind cost of energy due to many economies of scale compared to individual turbines located far from each other. However, these groupings introduce the problem of aerodynamic interaction among turbines, which can decrease the total energy converted to electricity compared to the same number of isolated turbines operating under the same wind inflow conditions. In this paper, we describe a simulation model under development to examine the aerodynamic interaction among turbines and increase the total energy captured by an array of turbines. We then discuss various control strategies to maximize the energy capture for wind farms containing multiple turbines.

Capabilities of extended state observer for estimating uncertainties

Abstract: The capabilities of extended state observer (ESO) for estimating uncertainties is discussed in this paper. The scope of the disturbances that can be observed by LESO with bounded observing errors is given. The observing errors for several typical disturbances-constant disturbance, sine disturbance, ramp disturbance, and square wave disturbance are further analyzed. It is demonstrated that ESO can deal with a large class of disturbances. Finally, the results are tested by simulations.

MABEL, a new robotic bipedal walker and runner

Abstract: This paper introduces MABEL, a new platform for the study of bipedal locomotion in robots. One of the purposes of building the mechanism is to explore a novel powertrain design that incorporates compliance, with the objective of improving the power efficiency of the robot, both in steady state operation and in responding to disturbances. A second purpose is to inspire the development of new feedback control algorithms for running on level surfaces and walking on rough terrain. A third motivation for building the robot is science and technology outreach; indeed, it is already included in tours when K-through-12 students visit the College of Engineering at the University of Michigan. MABEL is currently walking at 1.1 m/s on a level surface, and a related monopod at Carnegie Mellon is hopping well, establishing that the testbed has the potential to realize its many objectives.

Effects of different PHEV control strategies on vehicle performance

Abstract: Foreign oil dependence, increased cost of fuel, pollution, global warming are buzz words of today's era. Automobiles have a large impact on increasing energy demand, pollution and related issues. As a consequence, many efforts are being concentrated on innovative systems for transportation that could replace petroleum with cleaner fuel, i.e. electricity from the power grid. The use of plug-in hybrid electric vehicles (PHEVs) can become a very important change in this direction, since such vehicles could benefit from the increasing availability of renewable energy. PHEVs requires new control and energy management algorithms, that are crucial for vehicle performance. This paper deals with evaluation of two modes, Electric Vehicle (EV) mode and Blended mode, for plug-in hybrid electric vehicles and their comparison with conventional and hybrid electric vehicle performance.

Adaptive control of an anti-stable wave PDE

Abstract: Past papers on adaptive control of unstable PDEs with unmatched parametric uncertainties have considered only parabolic PDEs and first-order hyperbolic PDEs. In this note we introduce several tools for approaching adaptive control problems of second-order-in-time PDEs. We present these tools through a benchmark example of an unstable wave equation with an unmatched (non-collocated) anti-damping term, which serves both as a source of instability and of parametric uncertainty. The key effort in the design is to avoid the appearance of the second time derivative of the parameter estimate in the error system.

An overview on dynamics and controls modelling of hypersonic vehicles

Abstract: In order to appropriately design control laws for hypersonic vehicles, it is paramount to understand how the flight dynamics are impacted by the interactions between the aerothermodynamics, propulsion system, structural dynamics, and control system. To this end, there has been a significant investment into the modelling of these sub-systems and their integration into a comprehensive model that can be used to the characterize the flight dynamics of scramjet-powered hypersonic aircraft and still remain amenable to control law design and analysis. In this paper, the development of a comprehensive model of the longitudinal dynamics of generic hypersonic vehicle with an outward-turning, two-dimensional inlet is described. The sub-system models, for the most part, are simple models derived from first-principles and are intended to capture the interactions between the different sub-systems to provide a representative vehicle model. We also will discuss the areas that are important to realizing a hypersonic modelling approach that can take any given vehicle geometry and permit a thorough analysis of its stability and control characteristics and any practical constraints on its operability, the design of a control law, an assessment of it closed-loop performance.

Further results on the stability of distance-based multi-robot formations

Abstract: An important class of multi-robot formations is specified by desired distances between adjacent robots. In previous work, we showed that distance-based formations can be globally stabilized by negative gradient, potential field based, control laws, if and only if the formation graph is a tree. In this paper, we further examine the relation between the cycle space of the formation graph and the resulting equilibria of cyclic formations. In addition, the results are extended to the case of distance based formation control for nonholonomic agents. The results are supported through computer simulations.

Adaptive control of hypersonic vehicles in the presence of modeling uncertainties

Abstract: This paper proposes an adaptive controller for a hypersonic cruise vehicle subject to aerodynamic uncertainties, center-of-gravity movements, actuator saturation, failures, and time-delays. The adaptive control architecture is based on a linearized model of the underlying rigid body dynamics and explicitly accommodates for all uncertainties. It also includes a baseline proportional integral filter commonly used in optimal control designs. The control design is validated using a high-fidelity HSV model that incorporates various effects including coupling between structural modes and aerodynamics, and thrust pitch coupling. An elaborate comparative analysis of the proposed Adaptive Robust Controller for Hypersonic Vehicles (ARCH) is carried out using a control verification methodology. In particular, we study the resilience of the controller to the uncertainties mentioned above for a set of closed-loop requirements that prevent excessive structural loading, poor tracking performance and engine stalls. This analysis enables the quantification of the improvements that result from using and adaptive controller for a typical maneuver in the V - h space under cruise conditions.

Distributed information filtering using consensus filters

Abstract: In this paper we present a new information consensus filter for distributed dynamic-state estimation. Estimation is handled by the traditional information filter, while communication of measurements is handled by a consensus filter. First and second-order statistics of local estimates are discussed. It is shown that local information consensus filter estimates are unbiased, and the actual variance of the local estimation errors is comparable to a centralized estimate. However, local agents believe their local estimates are less accurate.

Structural controllability of multi-agent systems

Abstract: In this paper, the controllability problem for multi-agent systems is investigated. In particular, the case of a single leader under a fixed topology is considered. In contrast to the existing literature on this topic, we assume that the graph is weighted and we may freely assign the weights. Under this setup, the system is controllable if one may find a set of weights so as to satisfy the classical controllability rank condition. It turns out that this controllability notation purely depends on the topology of the communication scheme, and the multi-agent system is controllable if and only if the graph is connected. Moreover, some simulation results and numerical examples are presented to illustrate the approach.

Coherent H ∞ control for a class of linear complex quantum systems

Abstract: This paper considers a coherent H∞ control problem for a class of linear quantum systems which can be defined by complex quantum stochastic differential equations in terms of annihilation operators only. For this class of quantum systems, a solution to the H∞ control problem can be obtained in terms of a pair of complex Riccati equations. The paper also considers complex versions of the Bounded Real Lemma, the Strict Bounded Real Lemma and the Lossless Bounded Real Lemma. For the class of quantum systems under consideration, the question of physical realizability is related to the Bounded Real and Lossless Bounded Real properties.

Power management of plug-in hybrid electric vehicles using neural network based trip modeling

Abstract: The plug-in hybrid electric vehicles (PHEV), utilizing more battery power, has become a next-generation HEV with great promise of higher fuel economy. Global optimization charge-depletion power management would be desirable. This has so far been hampered due to the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). Combined with the Intelligent Transportation Systems (ITS), our previous work developed a two-scale dynamic programming approach as a nearly globally optimized charge-depletion strategy for PHEV power management. Trip model is obtained via GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling. The Gas-kinetic based model was used for the trip modeling in our previous study. The complicated partial deferential equation based model with several parameters needs to be calibrated had for implementation. In this paper, a neural network based trip model was developed for the highway portion, using the given data from WisTransPortal. The real test data was used for the training and validation of the network. The simulation results show that the obtained trip model using neural network can greatly improve the trip modeling accuracy, and thus improve the fuel economy. The potential of the advantages were indicated by the fuel economy comparison.

Control, estimation and optimization of energy efficient buildings

Abstract: Commercial buildings are responsible for a significant fraction of the energy consumption and greenhouse gas emissions in the US and worldwide Consequently, the design, optimization and control of energy efficient buildings can have a tremendous impact on energy cost and greenhouse gas emission Buildings are complex, multi-scale in time and space, multi-physics and highly uncertain dynamic systems with wide varieties of disturbances Recent results have shown that by considering the whole building as an integrated system and applying modern estimation and control techniques to this system, one can achieve greater efficiencies than obtained by optimizing individual building components such as lighting and HVAC We consider estimation and control for a distributed parameter model of a multi-room building In particular, we show that distributed parameter control theory, coupled with high performance computing, can provide insight and computational algorithms for the optimal placement of sensors and actuators to maximize observability and controllability Numerical examples are provided to illustrate the approach We also discuss the problems of design and optimization (for energy and CO2 reduction) and control (both local and supervisory) of whole buildings and demonstrate how sensitivities can be used to address these problems

Fractional-order [proportional derivative] controller for robust motion control: Tuning procedure and validation

Abstract: A fractional-order [proportional derivative] (FO-[PD]) controller is proposed for robust motion control systems. Focusing on a class of simplified models for motion control systems, a practical and systematic tuning procedure has been developed for the proposed FO-[PD] controller synthesis. The fairness issue in comparing with other controllers such as the traditional integer order PID (IO-PID) controller and the fractional order proportional derivative (FO-PD) controller has been for the first time addressed under the same number of design parameters and the same specifications. Side-to-side fair comparisons of the three controllers (i.e., IO-PID, FO-PD and FO-[PD]) via both simulation and experimental tests have revealed some interesting facts: 1) IO-PID controller designed may not always be stabilizing to achieve flat-phase specification while both FO-PD and FO-[PD] controllers designed are always stabilizing; 2) Both FO-PD and FO-[PD] controllers outperform IO-PID controller designed in this paper; 3) FO-[PD] controller outperforms FO-PD controller more when the time constant of the motion control system increases. Extensive validation tests on our real-time experimental test-bench illustrate the same.

Multiple UAV path planning using anytime algorithms

Abstract: We address the problem of generating feasible paths from a given start location to a goal configuration for multiple unmanned aerial vehicles (UAVs) operating in an obstacle rich environment that consist of static, pop-up and moving obstacles. The UAVs have limited sensor and communication ranges, when they detect a pop-up or a moving obstacle that is in the collision course with the UAV flight path, then it has to replan a new optimal path from its current location to the goal. Determining optimal paths with short time intervals is not feasible, hence we develop anytime algorithm using particle swarm optimization that yields paths whose quality increases with increase in available computation time. To track the given path by the anytime algorithm in 3D, we developed a new uav guidance law that is based on a combination of pursuit guidance law and line of sight guidance law from missile guidance literature. Simulations are carried out to show that the anytime algorithm produces good paths in a relatively short time interval and the guidance law allows the UAVs to track the generated path

Flexible control Lyapunov functions

Abstract: A central tool in systems theory for synthesizing control laws that achieve stability are control Lyapunov functions (CLFs). Classically, a CLF enforces that the resulting closed-loop state trajectory is contained within a cone with a fixed, predefined shape, and which is centered at and converges to a desired converging point. However, such a requirement often proves to be overconservative. In this paper we propose a novel idea that improves the design of CLFs in terms of flexibility, i.e. the CLF is permitted to be locally non-monotone along the closed-loop trajectory. The focus is on the design of optimization problems that allow certain parameters that define a cone associated with a standard CLF to be decision variables. In this way non-monotonicity of the CLF is explicitly linked with a decision variable that can be optimized on-line. Conservativeness is significantly reduced compared to classical CLFs, which makes flexible CLFs more suitable for stabilization of constrained discrete-time nonlinear systems and real-time control.

Adaptive dynamic surface control for a class of strict-feedback nonlinear systems with unknown backlash-like hysteresis

Abstract: In this paper, we investigate the control design for a class of strict-feedback nonlinear systems preceded by unknown backlash-like hysteresis. Using the characteristics of backlash-like hysteresis, adaptive dynamic surface control (DSC) is developed without constructing a hysteresis inverse. The explosion of complexity in traditional backstepping design is avoided by utilizing DSC. Function uncertainties are compensated for using neural networks due to their universal approximation capabilities. Through Lyapunov synthesis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.

Automatic path planning and control design for autonomous landing of UAVs using dynamic inversion

Abstract: In this paper a nonlinear control has been designed using the dynamic inversion approach for automatic landing of unmanned aerial vehicles (UAVs), along with associated path planning. This is a difficult problem because of light weight of UAVs and strong coupling between longitudinal and lateral modes. The landing maneuver of the UAV is divided into approach, glideslope and flare. In the approach UAV aligns with the centerline of the runway by heading angle correction. In glideslope and flare the UAV follows straight line and exponential curves respectively in the pitch plane with no lateral deviations. The glideslope and flare path are scheduled as a function of approach distance from runway. The trajectory parameters are calculated such that the sink rate at touchdown remains within specified bounds. It is also ensured that the transition from the glideslope to flare path is smooth by ensuring C1 continuity at the transition. In the outer loop, the roll rate command is generated by assuring a coordinated turn in the alignment segment and by assuring zero bank angle in the glideslope and flare segments. The pitch rate command is generated from the error in altitude to control the deviations from the landing trajectory. The yaw rate command is generated from the required heading correction. In the inner loop, the aileron, elevator and rudder deflections are computed together to track the required body rate commands. Moreover, it is also ensured that the forward velocity of the UAV at the touch down remains close to a desired value by manipulating the thrust of the vehicle. A nonlinear six-DOF model, which has been developed from extensive wind-tunnel testing, is used both for control design as well as to validate it.

Path following for marine surface vessels with rudder and roll constraints: An MPC approach

Abstract: The problem of path following for marine surface vessels using the rudder control is addressed in this paper. The need to enforce the roll constraints and the fact that the rudder actuation is limited in both amplitude and rate make the model predictive control (MPC) approach a natural choice. The MPC design is based on a linearized model for computational and implementation considerations, while the evaluation of the performance of MPC controller is performed on a nonlinear 4 degree of freedom surface vessel model. The simulation results are presented to verify the effectiveness of the resulting controller and a simulation based tuning process for the controller is also presented. Furthermore, the performance of the path following MPC control in wave fields is evaluated using an integrated maneuvering and seakeeping model, and the simulation confirms its robustness.

Experimental validation of cooperative environmental boundary tracking with on-board sensors

Abstract: This paper describes a cooperative testbed implementation of a new algorithm (Jin and Bertozzi, CDC 2007) for environmental boundary tracking and estimation using only localized noisy sensors. The tracking algorithm is based on Page's cumulative sum algorithm (CUSUM) a method for change-point detection. A geometric, biologically inspired, motion control algorithm allows individual vehicles to track and follow the environmental boundary without external positioning information. Relative positioning between vehicles allows several to maintain a convoy while tracking the boundary. The algorithm performs well in the presence of moderate sensor noise.

Optimally controlling Hybrid Electric Vehicles using path forecasting

Abstract: The paper examines path-dependent control of Hybrid Electric Vehicles (HEVs). In this approach we seek to improve HEV fuel economy by optimizing charging and discharging of the vehicle battery depending on the forecasted vehicle route. The route is decomposed into a series connection of route segments with (partially) known properties. The dynamic programming is used as a tool to quantify the benefits offered by route information availability.

Regularized Fitted Q-Iteration for planning in continuous-space Markovian decision problems

Abstract: Reinforcement learning with linear and non-linear function approximation has been studied extensively in the last decade. However, as opposed to other fields of machine learning such as supervised learning, the effect of finite sample has not been thoroughly addressed within the reinforcement learning framework. In this paper we propose to use L2 regularization to control the complexity of the value function in reinforcement learning and planning problems. We consider the Regularized Fitted Q-Iteration algorithm and provide generalization bounds that account for small sample sizes. Finally, a realistic visual-servoing problem is used to illustrate the benefits of using the regularization procedure.