Raúl Ordóñez

Adaptive fuzzy control: experiments and comparative analyses

Advances in nonlinear control theory have provided the mathematical foundations necessary to establish conditions for stability of several types of adaptive fuzzy controllers. However, very few, if any, of these techniques have been compared to conventional adaptive or nonadaptive nonlinear controllers or tested beyond simulation; therefore, many of them remain as purely theoretical developments whose practical value is difficult to ascertain. In this paper we develop three case studies where we perform a comparative analysis between the adaptive fuzzy techniques in Spooner and Passino (1995,1996) and some conventional adaptive and nonadaptive nonlinear control techniques. In each case, the analysis is performed both in simulation and in implementation, in order to show practical examples of how the performance of these controllers compares to conventional controllers in real systems.

Numerical Optimization-Based Extremum Seeking Control With Application to ABS Design

Extremum seeking control (ESC) schemes based on numerical optimization are proposed in this paper. The extremum seeking problem is treated as an optimization with dynamic system constraints. The numerical optimization-based extremum seeking control scheme is first applied to linear time-invariant (LTI) systems, then it is extended to a class of feedback linearizable systems. The convergence of the ESC scheme is guaranteed by the numerical optimization algorithm and state regulation. The robustness of line search methods and trust region methods is studied, which provides further flexibility for the design of robust extremum seeking controller. Simulation study of antilock braking systems (ABS) design via extremum seeking control is addressed

Robust and adaptive design of numerical optimization-based extremum seeking control

We consider the employment of numerical optimization and state regulation to solve the extremum seeking control (ESC) problem, which does not assume the time scale separation between the plant dynamics and the extremum seeking loop. Extremum seeking is realized via a state regulator that drives the state traveling along a convergent set point sequence generated by a numerical optimization algorithm. In this paper, we propose a novel design of an asymptotic state regulator via output tracking for state feedback linearizable systems, where we trade off finite time state regulation to obtain flexibility in designing a robust extremum seeking controller. Existing techniques such as nonlinear damping and nonlinear adaptive control are then used to deal with input disturbance and unmodeled plant dynamics. Simulation examples illustrate the effectiveness of the basic and robust extremum seeking schemes, and some design guidelines are provided for engineering applications.

Swarm Tracking Using Artificial Potentials and Sliding Mode Control

In this paper, we present a stable and decentralized control strategy for multiagent systems (swarms) to capture a moving target in a specific formation. The coordination framework uses artificial potentials to take care of both tracking and formation tasks. First, a basic controller is designed based on a kinematic model. After that, sliding mode control technique is used to force the agents with general vehicle dynamics to obey the required motion. Finally, specific potential functions are discussed and corresponding simulation results are given.

Direct adaptive fuzzy control for a class of discrete-time systems

A direct adaptive control scheme is presented for a class of discrete-time nonlinear systems. The control scheme is based on an online functional approximation approach which modifies a standard or Takai-Sugeno (1985) fuzzy control system. A modified version of the standard key technical lemma which allows for adaptive systems with dead zones is used to guarantee asymptotic convergence of the tracking error to an /spl epsiv/-neighborhood of zero.

Indirect adaptive fuzzy control for a class of discrete-time systems

An indirect adaptive control scheme is presented for a class of discrete-time nonlinear systems. The control scheme is based on an online functional approximation approach which modifies a standard or Takagi-Sugeno fuzzy control system. A modified version of the standard key technical lemma which allows for adaptive systems with dead zones is used to guarantee asymptotic convergence of the tracking error to an /spl epsiv/-neighborhood of zero.

Dynamic programming applied to UAV way point path planning in wind

Path planning for small unmanned air vehicles (UAVs) becomes a difficult problem when accounting for wind. Wind can affect the path quality in a nonlinear manner requiring extended segment lengths for accurate measurements. Dynamic programming offers an efficient solution to create way point paths justified when settling lengths are linearly interpolated. A database of good turn progressions supports this method and can be composed off-line. The heuristic inherent in dynamic programming provides near optimal paths in very fast computational time because it restricts the search to a small number of candidates. A solution can be guaranteed by including turns over 180deg.

Stable Direct Adaptive Control of a Class of Discrete Time Nonlinear Systems

Abstract A direct adaptive control scheme is presented for a class of discrete-time nonlinear systems. We show that if the plant has exponentially stable zero-dynamics and appropriate basis functions can be specified, then the internal signals of the controller are bounded and the error between the plant output and a reference model output will converge to a neighborhood of zero. We do not impose sector restrictions upon the nonlinear functions that describe the plant dynamics or any kind of initialization restrictions. Finally, we show how fuzzy systems fit the proper form to be used as the tunable controller and illustrate the results in simulation for a magnetic levitation system.

Fast super-resolution with affine motion using an adaptive Wiener filter and its application to airborne imaging

Fast nonuniform interpolation based super-resolution (SR) has traditionally been limited to applications with translational interframe motion. This is in part because such methods are based on an underlying assumption that the warping and blurring components in the observation model commute. For translational motion this is the case, but it is not true in general. This presents a problem for applications such as airborne imaging where translation may be insufficient. Here we present a new Fourier domain analysis to show that, for many image systems, an affine warping model with limited zoom and shear approximately commutes with the point spread function when diffraction effects are modeled. Based on this important result, we present a new fast adaptive Wiener filter (AWF) SR algorithm for non-translational motion and study its performance with affine motion. The fast AWF SR method employs a new smart observation window that allows us to precompute all the needed filter weights for any type of motion without sacrificing much of the full performance of the AWF. We evaluate the proposed algorithm using simulated data and real infrared airborne imagery that contains a thermal resolution target allowing for objective resolution analysis.

Trajectory Generation on Approach and Landing for RLVs Using Motion Primitives and Neighboring Optimal Control

A major objective of next generation reusable launch vehicle (RLV) programs includes significant improvements in vehicle safety, reliability, and operational costs. In this paper, trajectory generation on approach/ landing (A&L) for RLVs using motion primitives (MPs) and neighboring optimal control (NOC) is discussed. The proposed trajectory generation approach at A&L phase is based on an MP scheme which consists of trims and maneuvers. From an initial point to a given touchdown point, all feasible trajectories that satisfy certain constraints are generated and saved into a trajectory database. An optimal trajectory can then be found off-line by using Dijkstras algorithm. After perturbations are imposed on the initial states of the off-line optimal trajectory, it is reshaped into a neighboring feasible trajectory on-line by using NOC approach. At this point, a neighboring feasible trajectory existence theorem (NFTET) is investigated and its proof is provided as well. The results show that the vehicle with stuck effectors can be recovered from failures in real time. Finally, robustness issues on NOC approach are briefly discussed.