This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

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Hydraulic Control Systems

This paper proposes a tracking control method for differential-drive wheeled mobile robots with nonholonomic constraints by using a backstepping-like feedback linearization. Unlike previous backstepping controllers for wheeled mobile robots, a backstepping-like feedback control structure is proposed in the form of a cascaded kinematic and dynamic linearization to have a simpler and modular control structure. First, the pseudo commands for the forward linear velocity and the heading direction angle are designed based on kinematics. Then, the actual torque control inputs are designed to make the actual forward linear velocity and heading direction angle follow their corresponding pseudo commands. A stability analysis shows that the tracking errors of the posture (the position and heading direction angle) are globally ultimately bounded and its ultimate bound can be adjusted by the proper choice of control parameters. In addition, numerical simulations for various reference trajectories (e.g., a straight line, a circle, a sinusoidal curve, a spinning trajectory with no forward velocity and a nonzero rotational velocity, a cross-shaped trajectory changing the forward and backward directions, etc.) show the validity of the proposed scheme.

Tracking Control of Differential-Drive Wheeled Mobile Robots Using a Backstepping-Like Feedback Linearization

Stochastic model predictive control with active uncertainty learning: A Survey on dual control

This paper presents the design and implementation of a complete control system for the swing-up and stabilizing control of an inverted pendulum. In particular, this work outlines the effectiveness of a particular swing-up method, based on feedback linearization and energy considerations. The power of modern state-space techniques for the analysis and control of Multiple Input Multiple Output (MIMO) systems is also investigated and a state-feedback controller is employed for stabilizing the pendulum. Cascade control is then utilized to reduce the complexity of the complete controller by splitting it into two separate control loops operating at well distinct bandwidths.

/pdf/non-linear-swing-up-and-stabilizing-control-of-an-inverted-389prbluw0.pdf

Non-linear swing-up and stabilizing control of an inverted pendulum system

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.

/pdf/dual-adaptive-dynamic-control-of-mobile-robots-using-neural-4zack2fpg2.pdf

Dual Adaptive Dynamic Control of Mobile Robots Using Neural Networks

Literature reviews on Multi-Robot Systems (MRS) typically focus on fundamental technical aspects, like coordination and communication, that need to be considered in order to coordinate a team of robots to perform a given task effectively and efficiently. Other reviews only consider works that aim to address a specific problem or one particular application of MRS. In contrast, this paper presents a survey of recent research works on MRS and categorises them according to their application domain. Furthermore, this paper compiles a number of seminal review works that have proposed specific taxonomies in classifying fundamental concepts, such as coordination, architecture and communication, in the field of MRS.

/pdf/a-review-on-multi-robot-systems-categorised-by-application-3fj3j9ar6l.pdf

A review on multi-robot systems categorised by application domain

This paper proposes and investigates the application of sliding mode control to the ball and plate problem. The nonlinear properties of the ball and plate control system are first presented. Then the experimental setup designed and built specifically for the purpose of this research is discussed. The paper then focuses on the implementation and thorough evaluation of the experimental results obtained with two different control schemes: the linear full-state feedback controller and the sliding mode controller. The latter control strategy was selected for its robust and order reduction properties. Finally the control performance of the two controllers is analysed. The sliding controller manages to obtain a faster and more accurate operation for continuously changing reference inputs. The robustness of the proposed control scheme is also verified, since the system's performance is shown to be insensitive to parameter variations.

Application of sliding mode control to the ball and plate problem

This paper presents a novel dual adaptive dynamic controller for trajectory tracking of nonholonomic wheeled mobile robots. The controller is developed entirely in discrete-time and the and the robot's nonlinear dynamic functions are assumed to be unknown. A Gaussian radial basis function neural network is employed for function approximation, and its weights are estimated stochastically in real-time. In contrast to adaptive certainty equivalence controllers hitherto published for mobile robots, the proposed control law takes into consideration the estimates' uncertainty, thereby leading to improved tracking performance. The proposed method is verified by realistic simulations and Monte Carlo analysis.

/pdf/dual-adaptive-control-for-trajectory-tracking-of-mobile-27cuuiyabx.pdf

Marvin K. Bugeja

Papers

Non-linear swing-up and stabilizing control of an inverted pendulum system

Dual Adaptive Dynamic Control of Mobile Robots Using Neural Networks

A review on multi-robot systems categorised by application domain

Application of sliding mode control to the ball and plate problem

Dual Adaptive Control for Trajectory Tracking of Mobile Robots