Research Papers

J. Dyn. Sys., Meas., Control. 2019;141(7):071001-071001-11. doi:10.1115/1.4043101.

In this paper, a new robotic fish propelled by a hybrid tail, which is actuated by two active joints, is developed. The first joint is driven by a servo motor, which generates flapping motions for main propulsion. The second joint is actuated by a soft actuator, an ionic polymer-metal composite (IPMC) artificial muscle, which directs the propelled fluid for steering. A state-space dynamic model is developed to capture the two-dimensional (2D) motion dynamics of the robotic fish. The model fully captures the actuation dynamics of the IPMC soft actuator, two-link tail motion dynamics, and body motion dynamics. Experimental results have shown that the robotic fish is capable of swimming forward (up to 0.45 body length/s) and turning left and right (up to 40 deg/s) with a small turning radius (less than half a body length). Finally, the dynamic model has been validated with experimental data, in terms of steady-state forward speed and turning speed at steady-state versus flapping frequency.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071002-071002-9. doi:10.1115/1.4043112.

Controlling underactuated systems is a challenging problem in control engineering. This paper presents a novel constraint-following approach for control design of an underactuated two-wheeled mobile robot (2 WMR), which has two degrees-of-freedom (DOF) to be controlled but only one actuator. The control goal is to drive the 2 WMR to follow a set of constraints, which may be holonomic or nonholonomic constraints. The constraint is considered in a more general form than the previous studies on constraint-following control (hence including a wider range of constraints). No auxiliary variables or pseudo variables are required for the control design. The proposed control only uses physical variables. We show that the proposed control is able to deal with both holonomic and nonholonomic constraints by forcing the constraint-following error to converge to zero, even if the system is not initially on the constraint manifold. Using this control design, we investigate two cases regarding different constraints on the 2 WMR motion, one for a holonomic constraint and the other for a nonholonomic constraint. Simulation results show that the proposed control is able to drive the 2 WMR to follow the constraints in both cases. Furthermore, the standard linear quadratic regulator (LQR) control is applied as a comparison in the simulations, which reflects the advantage of the proposed control.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071003-071003-10. doi:10.1115/1.4043114.

Unmanned aerial vehicles (UAVs) are making increasingly long flights today with significantly longer mission times. This requires the UAVs to have long endurance as well as have long range capabilities. Motivated by locomotory patterns in birds and marine animals which demonstrate a powered-coasting-powered periodic locomotory behavior, an optimal control problem is formulated to study UAV trajectory planning. The concept of differential flatness is used to reformulate the optimal control problem as a nonlinear programing problem where the flat outputs are parameterized using Fourier series. The Π test is also used to verify the existence of a periodic solution which outperforms the steady-state motion. An example of an Aerosonde UAV is used to illustrate the improvement in endurance and range costs of the periodic control solutions relative to the equilibrium flight.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071004-071004-10. doi:10.1115/1.4043117.

This paper presents a study of the energy-efficient operation of all-electric vehicles leveraging route information, such as road grade, to adjust the velocity trajectory. First, Pontryagin's maximum principle (PMP) is applied to derive necessary conditions and to determine the possible operating modes. The analysis shows that only five modes are required to achieve minimum energy consumption: full propulsion, cruising, coasting, full regeneration, and full regeneration with conventional braking. Then, the minimum energy consumption problem is reformulated and solved in the distance domain using dynamic programming to find the optimal speed profiles. Various simulation results are shown for a lightweight autonomous military vehicle. The sensitivity of energy consumption to regenerative-braking power limits and trip time is investigated. These studies provide important information that can be used in designing component size and scheduling operation to achieve the desired vehicle range.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071005-071005-8. doi:10.1115/1.4043119.

This paper introduces a robust visual tracking of objects in complex environments with blocking obstacles and light reflection noises. This visual tracking method utilizes a transfer matrix to project image pixels back to real-world coordinates. During the image process, a color and shape test is used to recognize the object and a vector is used to represent the object, which contains the information of orientation and body length of the object. If the object is partially blocked by the obstacles or the reflection from the water surface, the vector predicts the position of the object. During the real-time tracking, a Kalman filter is used to optimize the result. To validate the method, the visual tracking algorithm was tested by tracking a submarine and a fish on the water surface of a water tank, above which three pieces of blur glass were blocking obstacles between the camera and the object. By using this method, the interference from the reflection of the side glass and the fluctuation of the water surface can be also avoided.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071006-071006-11. doi:10.1115/1.4043113.

Knowledge of the tire-road information is not only very crucial in many active safety applications but also significant for self-driving cars. The tire-road information mainly consists of tire-road friction coefficient and road-tire friction forces. However, precise measurement of tire-road friction coefficient and tire forces requires expensive equipment. Therefore, the monitoring of tire-road information utilizing either accurate models or improved estimation algorithms is essential. Considering easy availability and good economy, this paper proposes a novel adaptive unified monitoring system (AUMS) to simultaneously observe the tire-road friction coefficient and tire forces, i.e., vertical, longitudinal, and lateral tire forces. First, the vertical tire forces can be calculated considering vehicle body roll and load transfer. The longitudinal and lateral tire forces are estimated by an adaptive unified sliding mode observer (AUSMO). Then, the road-tire friction coefficient is observed through the designed mode-switch observer (MSO). The designed MSO contains two modes: when the vehicle is under driving or brake, a slip slope method (SSM) is used, and a recursive least-squares (RLS) identification method is utilized in the SSM; when the vehicle is under steering, a comprehensive friction estimation method is adopted. The performance of the proposed AUMS is verified by both the matlab/simulinkCarSim co-simulation and the real car experiment. The results demonstrate the effectiveness of the proposed AUMS to provide accurate monitoring of tire-road information.

Topics: Roads , Tires , Wheels , Friction , Vehicles
Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071007-071007-12. doi:10.1115/1.4043115.

This paper describes a stochastic predictive control algorithm for partially observable Markov decision processes (POMDPs) with time-joint chance constraints. We first present the algorithm as a general tool to treat finite space POMDP problems with time-joint chance constraints together with its theoretical properties. We then discuss its application to autonomous vehicle control on highways. In particular, we model decision-making/behavior-planning for an autonomous vehicle accounting for safety in a dynamic and uncertain environment as a constrained POMDP problem and solve it using the proposed algorithm. After behavior is planned, we use nonlinear model predictive control (MPC) to execute the behavior commands generated from the planner. This two-layer control framework is shown to be effective by simulations.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071008-071008-13. doi:10.1115/1.4043107.

In this paper, using the theory of input saturation, a novel path following guidance law for fixed-wing unmanned aerial vehicles (UAVs) is developed. The proposed guidance law is adapted from a pursuit plus line-of-sight guidance law. Furthermore, it employs inertial speed for computing the acceleration commands which adds an adaptive capability of accommodating vehicle speed changes due to external disturbances such as wind. The guidance law is initially developed for two-dimensional (2D) environments which enables vehicles to follow straight lines, circles, and ellipses in planar spaces. Lyapunov theory is used to establish its stability properties, followed by a comparative study with existing algorithms, proposed for 2D environments, to establish its efficacy. The guidance law is then extended for the case of three-dimensional (3D) environments, and appropriate simulation studies are performed. Finally, real-world flight tests for 2D as well as 3D cases are presented, establishing the applicability of the proposed law on UAVs.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071009-071009-11. doi:10.1115/1.4043116.

Automation is becoming more and more important to achieve high efficiency and productivities in manufacturing facilities, and there has been a large increase in the use of autonomous mobile robots (AMRs) for factory automation. With the number of AMRs increasing, how to optimally schedule them in a timely manner such that a large school of AMRs can finish all the assigned tasks within the shortest time presents a significant challenge for control engineers. Exhaustive search can provide an optimal solution. However, its associated computational time is too long to render it feasible for real-time control. This paper introduces a novel two-step algorithm for fast scheduling of AMRs that perform prioritized tasks involving transportation of tools/materials from a pick-up location to a drop-off point on the factory floor. The proposed two-step algorithm first clusters these tasks such that one cluster of tasks is assigned to one single AMR, followed by scheduling of the tasks within a cluster using a model-based learning technique. For the purpose of clustering and scheduling, a task space is defined. The results from the clustering and scheduling algorithms are compared with other widely used heuristic techniques. Both the clustering and the scheduling algorithms are shown to perform better on task sets of relevant sizes and generate real-time solutions for the scheduling of multiple AMRs under task space constraints with priorities.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071010-071010-10. doi:10.1115/1.4043118.

Flow estimation plays an important role in the control and navigation of autonomous underwater robots. This paper presents a novel flow estimation approach that assimilates distributed pressure measurements through coalescing recursive Bayesian estimation and flow model reduction using proper orthogonal decomposition (POD). The proposed flow estimation approach does not rely on any analytical flow model and is thus applicable to many and various complicated flow fields for arbitrarily shaped underwater robots, while most of the existing flow estimation methods apply only to those well-structured flow fields with simple robot geometry. This paper also analyzes and discusses the flow estimation design in terms of reduced-order model accuracy, relationship with conventional flow parameters, and distributed senor placement. To demonstrate the effectiveness of the proposed distributed flow estimation approach, two simulation studies, one with a circular-shaped robot and one with a Joukowski-foil-shaped robot, are presented. The application of flow estimation in closed-loop angle-of-attack regulation is also investigated through simulation.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071011-071011-11. doi:10.1115/1.4043189.

With the advent of self-driving autonomous vehicles, vehicle controllers are free to drive their own velocities. This feature can be exploited to drive an optimal velocity trajectory that minimizes fuel consumption. Two typical approaches to drive cycle optimization are velocity smoothing and tractive energy minimization. The former reduces accelerations and decelerations, and hence, it does not require information of vehicle parameters and resistance forces. On the other hand, the latter reduces tractive energy demand at the wheels of a vehicle. In this work, utilizing an experimentally validated full vehicle simulation software, we show that for conventional gasoline vehicles the lower energy velocity trajectory can consume as much fuel as the velocity smoothing case. This implies that the easily implementable, vehicle agnostic velocity smoothing optimization can be used for velocity optimization rather than the nonlinear tractive energy minimization, which results in a pulse-and-glide trajectory.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2019;141(7):071012-071012-11. doi:10.1115/1.4043152.

There has been an increasing interest in the use of autonomous underwater robots to monitor freshwater and marine environments. In particular, robots that propel and maneuver themselves like fish, often known as robotic fish, have emerged as mobile sensing platforms for aquatic environments. Highly nonlinear and often under-actuated dynamics of robotic fish present significant challenges in control of these robots. In this work, we propose a nonlinear model predictive control (NMPC) approach to path-following of a tail-actuated robotic fish that accommodates the nonlinear dynamics and actuation constraints while minimizing the control effort. Considering the cyclic nature of tail actuation, the control design is based on an averaged dynamic model, where the hydrodynamic force generated by tail beating is captured using Lighthill's large-amplitude elongated-body theory. A computationally efficient approach is developed to identify the model parameters based on the measured swimming and turning data for the robot. With the tail beat frequency fixed, the bias and amplitude of the tail oscillation are treated as physical variables to be manipulated, which are related to the control inputs via a nonlinear map. A control projection method is introduced to accommodate the sector-shaped constraints of the control inputs while minimizing the optimization complexity in solving the NMPC problem. Both simulation and experimental results support the efficacy of the proposed approach. In particular, the advantages of the control projection method are shown via comparison with alternative approaches.

Commentary by Dr. Valentin Fuster

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