Yannick Morel

Direct Adaptive Tracking Control of Quadrotor Aerial Vehicles

This paper presents a novel adaptive control algorithm solving the trajectory tracking problem for quadrotor aerial vehicles. A model reference approach is used, such that the vehicle tracks the trajectory of a reference system, which itself tracks a specified desired trajectory. The control law is derived using a backstepping procedure. A technique derived from dynamic surface control is used to simplify the expression of the obtained control algorithm, with no significant loss in terms of performance. Proof of stability is obtained using Lyapunov theory. Results from numerical simulations illustrate the performance of the obtained controller.Copyright

Neural Network Model Reference Adaptive Control of Marine Vehicles

A neural network model reference adaptive controller for trajectory tracking of nonlinear systems is developed. The proposed control algorithm uses a single layer neural network that bypasses the need for information about the system’s dynamic structure and characteristics and provides portability. Numerical simulations are performed using nonlinear dynamic models of marine vehicles. Results are presented for two separate vehicle models, an autonomous surface vehicle and an autonomous underwater vehicle, to demonstrate the controller performance in terms of tuning, robustness, and tracking.

Estimation of relative position and coordination of mobile underwater robotic platforms through electric sensing

In the context of underwater robotics, positioning and coordination of mobile agents can prove a challenging problem. To address this issue, we propose the use of electric sensing, with a technique inspired by weakly electric fishes. In particular, the approach relies on one or several of the agents applying an electric field to their environment. Using electric measures, others agents are able to reconstruct their relative position with respect to the emitter, over a range that is function of the geometry of the emitting agent and of the power applied to the environment. Efficacy of the technique is illustrated using a number of numerical examples. The approach is shown to allow coordination of unmanned underwater vehicles, including that of bio-inspired swimming robotic platforms.

Nonlinear motion control of CPG-based movement with applications to a class of swimming robots

In bio-inspired robotics, use of a Central Pattern Generator (CPG) to coordinate actuation is fairly common. The gait achieved depends on a number of CPG parameters, which can be adjusted to control the robots motion. This paper presents an output feedback motion control framework, addressing issues encountered when dealing with this type of control problem, including partial state measurements and system uncertainty. Efficacy of the presented approach is illustrated by results of numerical simulations in the case of a swimming robot.

Nonlinear Predictor-Based Output Feedback Control for a Class of Uncertain Nonlinear Systems

The presented work addresses the output feedback control problem for a large class of uncertain nonlinear systems. The control algorithm relies on an output predictor, designed to predict the system’s measured output with arbitrary accuracy, for any admissible control signal. This output predictor is constructed using a derivative estimator, which allows the algorithm to only require limited knowledge of the system’s dynamics in general, and of the input matrix in particular. The output predictor, which is designed to be controllable, is then controlled using a backstepping control algorithm. The output feedback control problem is thus solved by controlling the predictor’s output, as opposed to controlling the actual system’s output, as is more commonly the case in the literature. Ultimately, it is shown that the predictor’s output is made to simultaneously converge to the actual system’s output and to a given desired output trajectory. It follows that the system’s output itself converges to the desired trajectory. Numerical simulation results are provided to illustrate the algorithm performance.Copyright

Action-Perception Trade-Offs for Anguilliform Swimming Robotic Platforms with an Electric Sense

The work presented addresses the combination of anguilliform swimming-based propulsion with the use of an electric sensing modality for a class of unmanned underwater vehicles, and in particular investigates the relative influence of adjustments to the swimming gait on the platforms displacement speed and on sensing performance. This influence is quantified, for a relevant range of swimming gaits, using experimental data recordings of displacement speeds, and a boundary element method-based numerical simulation tool allowing to reconstruct electric measures. Results show that swimming gaits providing greater movement speeds tend to degrade sensing performance. Conversely, gaits yielding accurate sensing tend to prove slower. To reconcile opposing tendencies, a simple action-perception cost function is designed, with the purpose of adjusting an anguilliform swimmers gait shape, in accordance with respective importance afforded to action (i.e. movement speed) and perception.

Prediction-based observation of nonlinear systems non-affine in the unmeasured states

The presented work addresses the observation problem for a large class of nonlinear systems, including systems which are nonlinear in the unmeasured states. Assuming partial state measurements, the unmeasured states are reconstructed so that a prediction of the measured states converges to a neighborhood of the actual measurements. This prediction-based observer algorithm relies on carefully selected prediction-observation errors, designed using a backstepping technique. Lyapunovs direct method is used to show Lyapunov stability and convergence of these errors to an arbitrarily small neighborhood of the origin. The technique is applied to two different nonlinear systems. Results of numerical simulations are presented for both cases and illustrate the efficacy of the algorithm. Experimental results are also provided for one of the examples.

OBSERVER-BASED OUTPUT FEEDBACK CONTROL OF NONLINEAR SYSTEMS NON-AFFINE IN THE UNMEASURED STATES

The presented work addresses the output feedback control problem for a large class of nonlinear systems. The control strategy shares similarities to separation-based algorithms commonly found in the literature, in the sense that the control problem is solved using an observer-predictor, in conjunction with a state feedback control law. The approach however distinguishes itself in significant ways. In particular, the control algorithm is not designed to control the actual system’s output, as is usually the case in separation-based approaches, but it is rather designed to control the output of the observer-predictor. The latter is designed to ensure that, for any admissible control signal, its output converges to a neighborhood of the corresponding output of the real system. The observer-predictor is thus used to indirectly control the real system. Results of numerical simulations are provided to illustrate performance of the obtained control algorithm.Copyright

Indirect, Non-Adaptive Control of a Class of Nonlinear Uncertain Systems With Applications to Motion Control of Swimming Robots

Motion control of bio-inspired mobile robotic platforms can prove a challenging problem. In particular, models for the considered type of systems may prove nonlinear, uncertain, and fairly complicated. To address these issues, use of an output predictor-based control algorithm was proposed. In particular, the approach relies on the design of a virtual system, constructed to emulate the actual system’s input/output behavior. Then, a control law is designed to leverage the dynamic information contained within this predictor. The resulting control scheme proves reasonably concise, and effectively circumvents issues related to partial state measurements and system uncertainty. Simulation results for an anguilliform swimming robot illustrate the control scheme’s efficacy.Copyright

Indirect Adaptive Control of a Class of Marine Vehicles

A nonlinear adaptive framework for bounded-error tracking control of a class of non-minimum phase marine vehicles is presented. The control algorithm relies on a special set of tracking errors guaranteeing satisfactory tracking performance and stable internal dynamics. First, the design of a model-based nonlinear control law, providing asymptotic stability of the error dynamics, is presented. This control algorithm solves the tracking problem for the considered class of marine vehicles, assuming full knowledge of the system model. Then, the analysis of the zero dynamics is carried out, which illustrates the efficacy of the chosen set of tracking errors in stabilizing the internal dynamics. Finally, an indirect adaptive technique is used to address parametric uncertainties in the model. The resulting adaptive control algorithm guarantees Lyapunov stability of the tracking errors and of the parameter estimates, and convergence of the tracking errors to zero. Numerical simulations illustrate the performance of the adaptive algorithm.Copyright