Abstract: This brief describes the application of direct and indirect model reference adaptive control to a lightweight low-cost quadrotor unmanned aerial vehicle platform. A baseline trajectory tracking controller is augmented by an adaptive controller. The approach is validated using simulations and flight tested in an indoor test facility. The adaptive controller is found to offer increased robustness to parametric uncertainties. In particular, it is found to be effective in mitigating the effects of a loss-of-thrust anomaly, which may occur due to component failure or physical damage. The design of the adaptive controller is presented, followed by a comparison of flight test results using the existing linear and augmented adaptive controllers.

Abstract: In this brief, we consider the formation control problem of underactuated autonomous surface vehicles (ASVs) moving in a leader-follower formation, in the presence of uncertainties and ocean disturbances. A robust adaptive formation controller is developed by employing neural network and dynamic surface control technique. The stability of the design is proven via Lyapunov analysis where semiglobal uniform ultimate boundedness of the closed-loop signals is guaranteed. The advantages of the proposed formation controller are that: first, the proposed method only uses the measurements of line-of-sight range and angle by local sensors, no other information about the leader is required for control implementation; second, the developed neural formation controller is able to capture the vehicle dynamics without exact information of coriolis and centripetal force, hydrodynamic damping and disturbances from the environment. Comparative analysis with a model-based approach is given to demonstrate the effectiveness of the proposed method.

Abstract: Energy consumption of a vehicle is greatly influenced by its driving behavior in highly interacting urban traffic. Strategies for fuel efficient driving have been studied and experimented with in various conceptual frameworks. This paper presents a novel control system to drive a vehicle efficiently on roads containing varying traffic and signals at intersections for improved fuel economy. The system measures the relevant information of the current road and traffic, predicts the future states of the preceding vehicle, and computes the optimal vehicle control input using model predictive control (MPC). A typical control objective is chosen to maximize fuel economy by regulating a safe head-distance or cruising at the optimal velocity under bounded driving torque condition. The proposed vehicle control system is evaluated in urban traffic containing thousands of diverse vehicles using the microscopic traffic simulator AIMSUN. Simulation results show that the vehicles controlled by the proposed MPC method significantly improve their fuel economy.

Abstract: Recent developments in vehicle steering systems offer new opportunities to measure the steering torque and reliably estimate the vehicle sideslip and the tire-road friction coefficient. This paper presents an approach to vehicle stabilization that leverages these estimates to define state boundaries that exclude unstable vehicle dynamics and utilizes a model predictive envelope controller to bound the vehicle motion within this stable region of the state space. This approach provides a large operating region accessible by the driver and smooth interventions at the stability boundaries. Experimental results obtained with a steer-by-wire vehicle and a proof of envelope invariance demonstrate the efficacy of the envelope controller in controlling the vehicle at the limits of handling.

Abstract: This brief explores the applicability of recent results in game theory and cooperative control to the problem of optimizing energy production in wind farms. One such result is a model-free control strategy that is completely decentralized and leads to efficient system behavior in virtually any distributed system. We demonstrate that this learning rule can provably maximize energy production in wind farms without explicitly modeling the aerodynamic interaction amongst the turbines.

Abstract: This brief aims to perform security threat assessment of networked control systems with regulatory and supervisory control layers. We analyze the performance of a proportional-integral controller (regulatory layer) and a model-based diagnostic scheme (supervisory layer) under a class of deception attacks. We adopt a conservative approach by assuming that the attacker has knowledge of: 1) the system dynamics; 2) the parameters of the diagnostic scheme; and 3) the sensor-control signals. The deception attack presented here can enable remote water pilfering from automated canal systems. We also report a field-operational test attack on the Gignac canal system located in Southern France.

Abstract: Lithium ion batteries should always be prevented from overheating and, hence, thermal monitoring is indispensable. Since only the surface temperature of the battery can be measured, a thermal model is needed to estimate the core temperature of the battery, which can be higher and more critical. In this paper, an online parameter identification scheme is designed for a cylindrical lithium ion battery. An adaptive observer of the core temperature is then designed based on the online parameterization methodology and the surface temperature measurement. A battery thermal model with constant internal resistance is explored first. The identification algorithm and the adaptive observer is validated with experiments on a 2.3Ah 26650 lithium iron phosphate/graphite battery. The methodology is later extended to address temperature-dependent internal resistance with nonuniform forgetting factors. The ability of the methodology to track the long-term variation of the internal resistance is beneficial for battery health monitoring.

Abstract: Vehicle active safety receives ever increasing attention in the attempt to achieve zero accidents on the road. In this paper, we investigate a control architecture that has the potential of improving yaw stability control by achieving faster convergence and reduced impact on the longitudinal dynamics. We consider a system where active front steering and differential braking are available and propose a model predictive control (MPC) strategy to coordinate the actuators. We formulate the vehicle dynamics with respect to the tire slip angles and use a piecewise affine (PWA) approximation of the tire force characteristics. The resulting PWA system is used as prediction model in a hybrid MPC strategy. After assessing the benefits of the proposed approach, we synthesize the controller by using a switched MPC strategy, where the tire conditions (linear/saturated) are assumed not to change during the prediction horizon. The assessment of the controller computational load and memory requirements indicates that it is capable of real-time execution in automotive-grade electronic control units. Experimental tests in different maneuvers executed on low-friction surfaces demonstrate the high performance of the controller.

Abstract: This paper examines the problem of demand-side energy management in smart power grids through the setpoint control of aggregate thermostatic loads. This paper models these loads using a novel partial differential equation framework that builds on existing diffusion- and transport-based load modeling ideas in the literature. Both this partial differential equation (PDE) model and its finite-difference approximations are bilinear in the state and control variables. This key insight creates a unique opportunity for designing nonlinear load control algorithms with theoretically guaranteed Lyapunov stability properties. This paper's main contribution to the literature is the development of the bilinear PDE model and a sliding mode controller for the real-time management of thermostatic air conditioning loads. The proposed control scheme shows promising performance in adapting aggregate air conditioning loads to intermittent wind power.

Abstract: Batteries are the key technology for enabling further mobile electrification and energy storage. Accurate prediction of the state of the battery is needed not only for safety reasons, but also for better utilization of the battery. In this work we present a state estimation strategy for a detailed electrochemical model of a lithium-ion battery. The benefit of using a detailed model is the additional information obtained about the battery, such as accurate estimates of the internal temperature, the state of charge within the individual electrodes, overpotential, concentration and current distribution across the electrodes, which can be utilized for safety and optimal operation. Based on physical insight, we propose an output error injection observer based on a reduced set of partial differential-algebraic equations. This reduced model has a less complex structure, while it still captures the main dynamics. The observer is extensively studied in simulations and validated in experiments for actual electric-vehicle drive cycles. Experimental results show the observer to be robust with respect to unmodeled dynamics as well as to noisy and biased voltage and current measurements. The available state estimates can be used for monitoring purposes or incorporated into a model based controller to improve the performance of the battery while guaranteeing safe operation.

Abstract: In this paper, we investigate the formation control of multiple unmanned aerial vehicles (UAVs), specifically unmanned aircraft, in an obstacle-laden environment. The main contribution of this paper is to integrate the formation control, trajectory tracking, and obstacle/collision avoidance into one unified optimal control framework. A nonquadratic avoidance cost is innovatively constructed via an inverse optimal control approach, which leads to an analytical, distributed, and optimal formation control law. The stability and optimality of the closed-loop system are proven. In addition, the proposed optimal control law is dependent only on the information from the local neighbors, rather than all UAVs' information. Simulation of multiple UAVs' formation flying demonstrates the effectiveness of the integrated optimal control design with desired behaviors including formation flying, trajectory tracking, and obstacle/collision avoidance.

Abstract: This brief describes robust adaptive tracking control systems for the attitude dynamics of a rigid body. Both the attitude dynamics and the proposed control system are globally expressed on the special orthogonal group, to avoid complexities and ambiguities associated with other attitude representations, such as Euler angles or quaternions. By designing an adaptive law for the inertia matrix of a rigid body, the proposed control system can asymptotically follow an attitude command without the knowledge of the inertia matrix, and it is extended to guarantee boundedness of tracking errors in the presence of unstructured disturbances. These are illustrated by the experimental results of the attitude dynamics of a quadrotor unmanned aerial vehicle.

Abstract: Planetary soft landing is one of the benchmark problems of optimal control theory and is gaining renewed interest due to the increased focus on the exploration of planets in the solar system, such as Mars. The soft landing problem with all relevant constraints can be posed as a finite-horizon optimal control problem with state and control constraints. The real-time generation of fuel-optimal paths to a prescribed location on a planet's surface is a challenging problem due to the constraints on the fuel, the control inputs, and the states. The main difficulty in solving this constrained problem is the existence of nonconvex constraints on the control input, which are due to a nonzero lower bound on the control input magnitude and a nonconvex constraint on its direction. This paper introduces a convexification of the control constraints that is proven to be lossless; i.e., an optimal solution of the soft landing problem can be obtained via solution of the proposed convex relaxation of the problem. The lossless convexification enables the use of interior point methods of convex optimization to obtain optimal solutions of the original nonconvex optimal control problem.

Abstract: In this paper, robust adaptive control with dynamic control allocation is proposed for the positioning of marine vessels equipped with a thruster assisted mooring system, in the presence of parametric uncertainties, unknown disturbances and input nonlinearities. Using neural network approximation and variable structure based techniques in combination with backstepping and Lyapunov synthesis, the positioning control is developed to handle the uncertainties, input saturation and dead-zone characteristics of the mooring lines and thrusters. Full state feedback with all states measurable and output feedback using high gain observer to estimate unmeasurable states are considered. Dynamic control allocation is presented for actuation of the position mooring system. Under the proposed robust adaptive control, semi-global uniform boundedness of the closed-loop signals are guaranteed. Numerical simulations are carried out to show the effectiveness of the proposed control.

Abstract: In this paper, performance analysis of generalized extended state observer (ESO) in handling fast-varying sinusoidal disturbances is presented. It is shown that the higher order ESO offers improvement in the tracking of fast-varying sinusoidal disturbances, if the ESO bandwidth is chosen significantly larger than the frequency of the disturbance and ensuring that it is sufficiently smaller than unmodeled high frequency dynamics. The frequency and time-domain analysis results are presented, and the findings are verified through numerical simulations and experimentation on Quanser's motion control module.

Abstract: This paper investigates the problem of vibration suppression in vehicular active suspension systems, whose aim is to stabilize the attitude of the vehicle and improve the riding comfort. A full-car model is adopted, and electrohydraulic actuators with highly nonlinear characteristics are considered to form the basis of accurate control. In this paper, the H∞ performance is introduced to realize the disturbance suppression by selecting the actuator forces as virtual inputs, and an adaptive robust control technology is further used to design controllers which help real force inputs track virtual ones. The resulting controllers are robust against both actuator parametric uncertainties and uncertain actuator nonlinearities. The stability analysis for the closed-loop system is given within the Lyapunov framework. Finally, a numerical example is given to illustrate the effectiveness of the proposed control law, where different road conditions are considered in order to reveal the closed-loop system performance in detail.

Abstract: We apply an operator splitting technique to a generic linear-convex optimal control problem, which results in an algorithm that alternates between solving a quadratic control problem, for which there are efficient methods, and solving a set of single-period optimization problems, which can be done in parallel, and often have analytical solutions. In many cases, the resulting algorithm is division-free (after some off-line pre-computations) and can be implemented in fixed-point arithmetic, for example on a field-programmable gate array (FPGA). We demonstrate the method on several examples from different application areas.

Abstract: The detection of direct causality, as opposed to indirect causality, is an important and challenging problem in root cause and hazard propagation analysis. Several methods provide effective solutions to this problem when linear relationships between variables are involved. For nonlinear relationships, currently only overall causality analysis can be conducted, but direct causality cannot be identified for such processes. In this paper, we describe a direct causality detection approach suitable for both linear and nonlinear connections. Based on an extension of the transfer entropy approach, a direct transfer entropy (DTE) concept is proposed to detect whether there is a direct information flow pathway from one variable to another. Especially, a differential direct transfer entropy concept is defined for continuous random variables, and a normalization method for the differential direct transfer entropy is presented to determine the connectivity strength of direct causality. The effectiveness of the proposed method is illustrated by several examples, including one experimental case study and one industrial case study.

Abstract: This brief presents a data-driven constrained norm-optimal iterative learning control framework for linear time-invariant systems that applies to both tracking and point-to-point motion problems. The key contribution of this brief is the estimation of the system's impulse response using input/output measurements from previous iterations, hereby eliminating time-consuming identification experiments. The estimated impulse response is used in a norm-optimal iterative learning controller, where actuator limitations can be formulated as linear inequality constraints. Experimental validation on a linear motor positioning system shows the ability of the proposed data-driven framework to: 1) achieve tracking accuracy up to the repeatability of the test setup; 2) minimize the rms value of the tracking error while respecting the actuator input constraints; 3) learn energy-optimal system inputs for point-to-point motions.

Abstract: Hysteresis of a piezoelectric actuator is rate-dependent, but most hysteresis models are based on elementary rate-independent models, which are not suitable for modeling actuator behavior across a wide range of frequencies. This paper presents a novel modified inverse Preisach model to compensate the hysteresis of a piezoelectric actuator at varying frequency ranges. The classical Preisach model for hysteresis is introduced first, the identification of μ-function through least square method is conducted afterwards. The linearity property of the Preisach model is analyzed and verified by experiment. A novel modified inverse Preisach model featured with weighed sum of μ-density functions is proposed, which is based on the linearity property. The fast Fourier transform method is adopted to select the proper μ-density functions and weights to form a real-time online rate-dependent compensator for piezoceramic (PZTs) hysteresis. During experiments with tracking multifrequency composed signals, we have observed that the hysteresis features of the PZT can be consistently compensated. The experimental results show that the proposed open-loop hysteresis adjust method greatly improves the tracking control accuracy of the PZT.

Abstract: Many optimal control problems include a continuous nonlinear dynamic system, state, and control constraints, and final state constraints. When using dynamic programming to solve such a problem, the solution space typically needs to be discretized and interpolation is used to evaluate the cost-to-go function between the grid points. When implementing such an algorithm, it is important to treat numerical issues appropriately. Otherwise, the accuracy of the found solution will deteriorate and global optimality can be restored only by increasing the level of discretization. Unfortunately, this will also increase the computational effort needed to calculate the solution. A known problem is the treatment of states in the time-state space from which the final state constraint cannot be met within the given final time. In this brief, a novel method to handle this problem is presented. The new method guarantees global optimality of the found solution, while it is not restricted to a specific class of problems. Opposed to that, previously proposed methods either sacrifice global optimality or are applicable to a specific class of problems only. Compared to the basic implementation, the proposed method allows the use of a substantially lower level of discretization while achieving the same accuracy. As an example, an academic optimal control problem is analyzed. With the new method, the evaluation time was reduced by a factor of about 300, while the accuracy of the solution was maintained.

Abstract: Iterative learning control (ILC) is concerned with tracking a reference trajectory defined over a finite time duration, and is applied to systems which perform this action repeatedly. However, in many application domains the output is not critical at all points over the task duration. In this paper the facility to track an arbitrary subset of points is therefore introduced, and the additional flexibility this brings is used to address other control objectives in the framework of iterative learning. These comprise hard and soft constraints involving the system input, output and states. Experimental results using a robotic arm confirm that embedding constraints in the ILC framework leads to superior performance than can be obtained using standard ILC and an a priori specified reference.

Abstract: Offline robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of many prototypes and industrial robots. However, this method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. The proposed new method called DIDIM requires only the joint force/torque measurement, which avoids the calculation of the velocity and acceleration by bandpass filtering of the measured position. It is a closed-loop output error method where the usual joint position output is replaced by the joint force/torque. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. This is a nonlinear least-squares problem which is dramatically simplified using the inverse dynamic model to obtain an analytical expression of the simulated force/torque, linear in the parameters. A validation experiment on a two degree-of-freedom direct drive rigid robot shows that the new method is efficient.

Abstract: This paper investigates the problem of detection and isolation of attacks on a water distribution network comprised of cascaded canal pools. The proposed approach employs a bank of delay-differential observer systems. The observers are based on an analytically approximate model of canal hydrodynamics. Each observer is insensitive to one fault/attack mode and sensitive to other modes. The design of the observers is achieved by using a delay-dependent linear matrix inequality method. The performance of our model-based diagnostic scheme is tested on a class of adversarial scenarios based on a generalized fault/attack model. This model represents both classical sensor-actuator faults and communication network-induced deception attacks. Our particular focus is on stealthy deception attacks in which the attacker's goal is to pilfer water through canal offtakes. Our analysis reveals the benefits of accurate hydrodynamic models in detecting physical faults and cyber attacks to automated canal systems. We also comment on the criticality of sensor measurements for the purpose of detection. Finally, we discuss the knowledge and effort required for a successful deception attack.

Abstract: This paper addresses cooperative search for multiple stationary ground targets by a group of unmanned aerial vehicles with limited sensing and communication capabilities. The whole surveillance region is partitioned into cells where each cell is associated with a probability of target existence within the cell, which constitutes a probability map for the whole region. Each agent keeps an individual probability map and updates the map individually with measurements according to Bayesian rule. A nonlinear transformation of the probability map is introduced to simplify the computation by linearizing the Bayesian update. A consensus-like distributed fusion scheme is proposed for multiagent map fusion. We prove that all the individual probability maps converge to the same one that reflects the true existence or nonexistence of targets within each cell. Coverage and topology control algorithms are designed for the path planning of mobile agents. Moreover, the performance of the fusion scheme for asynchronous implementations of sampling and communication is analyzed. Finally, the effectiveness of the proposed algorithms is illustrated via simulations.

Abstract: A nonlinear position tracking controller with a disturbance observer (DOB) is proposed to track the desired position in the presence of the disturbance for electrohydraulic actuators (EHAs). The DOB is designed in the form of a second-order high-pass filter in order to estimate the disturbance. The nonlinear controller is designed for position tracking as a near input-output linearizing inner-loop load pressure controller and a backstepping outer-loop position controller. Variable structure control is implemented in order to compensate for the error in disturbance estimation. The desired load pressure is designed to generate the pressure using the differential flatness property of the EHA's mechanical subsystem. The disturbance within the bandwidth of the DOB can be cancelled by the proposed method. The performance of the proposed method is validated via simulations and experiments.

Abstract: This brief proposes an iterative sliding mode observer (ISMO) for the robust sensorless control of a permanent magnet synchronous motor with variable parameters. In the conventional SMO, a low-pass filter and an additional position compensator for the rotor are used to reduce the chattering coming from the switching by means of a signum function. It is shown that the chattering can be further reduced by using a sigmoid function as the switching function in the observer. This observer is faster in estimating the velocity and position of the rotor than the traditional adaptive SMO, since it does not include the integral operations for the low-pass filter. The proposed ISMO also improves the performance in estimating the motor speed and angle by reducing the estimation error in the back electromotive force by iteratively applying the observer in the sensorless operation. The stability of the proposed SMO is verified by the Lyapunov function in determining the observer gain, and the validity of the observer is demonstrated by simulations and experiments.

Abstract: Knowledge of tire force potential, i.e., tire-road frictional coefficient, is important for vehicle active safety systems because tire-road friction is an effective measure of the safety margin of vehicle dynamics. For vehicle handling dynamics, the frictional coefficient is highly coupled with tire slip angle, therefore, they need to be estimated simultaneously when the latter is not measured. This paper presents an estimation algorithm based on a robust adaptive observer methodology. Stability and robustness of this observer are analyzed numerically. The performance is analyzed using computer simulations and experiments under various road and steering conditions.

Abstract: In this paper, we present the basic features of the flight control of the SkySails towing kite system. After introducing the coordinate definitions and the basic system dynamics, we introduce a novel model used for controller design and justify its main dynamics with results from system identification based on numerous sea trials. We then present the controller design, which we successfully use for operational flights for several years. Finally, we explain the generation of dynamical flight patterns.