Boundary tracking and source seeking of oceanic features using autonomous vehicles

The thesis concerns the study and the development of boundary tracking and source seeking approaches for autonomous vehicles, specifically for marine autonomous systems. The underlying idea is that the characterization of most environmental features can be posed from either a boundary tracking or a source seeking perspective. The suboptimal sliding mode boundary tracking approach is considered and, as a first contribution, it is extended to the study of three dimensional features. The approach is aimed at controlling the movement of an underwater glider tracking a three-dimensional underwater feature and it is validated in a simulated environment. Subsequently, a source seeking approach based on sliding mode extremum seeking ideas is proposed. This approach is developed for the application to a single surface autonomous vehicle, seeking the source of a static or dynamic two dimensional spatial field. A sufficient condition which guarantees the finite time convergence to a neighbourhood of the source is introduced. Furthermore, a probabilistic learning boundary tracking approach is proposed, aimed at exploiting the available preliminary information relating to the spatial phenomenon of interest in the control strategy. As an additional contribution, the sliding mode boundary tracking approach is experimentally validated in a set of sea-trials with the deployment of a surface autonomous vehicle. Finally, an embedded system implementing the proposed boundary tracking strategy is developed for future installation on board of the autonomous vehicle. This work demonstrates the possibility to perform boundary tracking with a fully autonomous vehicle and to operate marine autonomous systems without remote control or pre-planning. Conclusions are drawn from the results of the research presented in this thesis and directions for future work are identified

Environmental feature exploration with a single autonomous vehicle

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In this paper, a sliding mode based guidance strategy is proposed for the control of an autonomous vehicle. The aim of the autonomous vehicle deployment is the study of unknown environmental spatial features. The proposed approach allows the solution of both boundary tracking and source seeking problems with a single autonomous vehicle capable of sensing the value of the spatial field at its position. The movement of the vehicle is controlled through the proposed guidance strategy, which is designed on the basis of the collected measurements without the necessity of pre-planning or human intervention. Moreover, no a priori knowledge about the field and its gradient is required. The proposed strategy is based on the so-called sub-optimal sliding mode controller. The guidance strategy is demonstrated by computer based simulations and a set of boundary tracking experimental sea trials. The efficacy of the algorithm to autonomously steer the C-Enduro surface vehicle to follow a fixed depth contour in a dynamic coastal region is demonstrated by the results from the trial described in this paper.Natural Environment Research Council (NERC)Defence Science and Technology Laboratory (DSTL)Innovate UKAutonomous Surface Vehicles (ASV) Ltd., Portcheste

Design of an Integral Suboptimal Second-Order Sliding Mode Controller for the Robust Motion Control of Robot Manipulators

The formulation of an integral suboptimal second-order sliding mode ((ISSOSM) control algorithm, oriented to solve motion control problems for robot manipulators, is presented in this paper. The proposed algorithm is designed so that the so-called reaching phase, normally present in the evolution of a system controlled via the sliding mode approach, is reduced to a minimum. This fact makes the algorithm more suitable to be applied to a real industrial robot, since it enhances its robustness, by extending it also to time intervals during which the classical sliding mode is not enforced. Moreover, since the algorithm generates second-order sliding modes, while the model of the controlled electromechanical system has a relative degree equal to one, the control action actually fed into the plant is continuous, which provides a positive chattering alleviation effect. The assessment of the proposal has been carried out by experimentally testing it on a COMAU SMART3-S2 anthropomorphic industrial robot manipulator. The satisfactory experimental results, also compared with those obtained with a standard proportional-derivative controller and with the original suboptimal algorithm, confirm that the new algorithm can actually be used in an industrial context

Dynamic Tube MPC for Nonlinear Systems

Modeling error or external disturbances can severely degrade the performance of Model Predictive Control (MPC) in real-world scenarios. Robust MPC (RMPC) addresses this limitation by optimizing over feedback policies but at the expense of increased computational complexity. Tube MPC is an approximate solution strategy in which a robust controller, designed offline, keeps the system in an invariant tube around a desired nominal trajectory, generated online. Naturally, this decomposition is suboptimal, especially for systems with changing objectives or operating conditions. In addition, many tube MPC approaches are unable to capture state-dependent uncertainty due to the complexity of calculating invariant tubes, resulting in overly-conservative approximations. This work presents the Dynamic Tube MPC (DTMPC) framework for nonlinear systems where both the tube geometry and open-loop trajectory are optimized simultaneously. By using boundary layer sliding control, the tube geometry can be expressed as a simple relation between control parameters and uncertainty bound; enabling the tube geometry dynamics to be added to the nominal MPC optimization with minimal increase in computational complexity. In addition, DTMPC is able to leverage state-dependent uncertainty to reduce conservativeness and improve optimization feasibility. DTMPC is demonstrated to robustly perform obstacle avoidance and modify the tube geometry in response to obstacle proximity

Robust motion control of a robot manipulator via Integral Suboptimal Second Order Sliding modes

This paper deals with the formulation of an Integral Suboptimal Second Order Sliding Mode control algorithm oriented to solve motion control problems for robot manipulators, taking into account the presence of unavoidable modelling uncertainties and external disturbances affecting the systems. The proposed algorithm is designed so that the so-called reaching phase, normally present in the evolution of a system controlled via a Sliding Mode controller, is reduced to a minimum. Moreover, since the relative degree of the relevant system output is suitably augmented through the use of an integrator, the control action affecting the robotic system is continuous, with a significant benefit, in terms of chattering alleviation, for the overall controlled electromechanical system. The verification and validation of our proposal have been performed by simulating the motion control scheme relying on a model of the considered robot, i.e. a COMAU SMART3-S2 anthropomorphic industrial robot manipulator, identified on the basis of real data. © 2013 IEEE

Robust motion control of a robot manipulator via Integral Suboptimal Second Order Sliding modes

This paper deals with the formulation of an Integral Suboptimal Second Order Sliding Mode control algorithm oriented to solve motion control problems for robot manipulators, taking into account the presence of unavoidable modelling uncertainties and external disturbances affecting the systems. The proposed algorithm is designed so that the so-called reaching phase, normally present in the evolution of a system controlled via a Sliding Mode controller, is reduced to a minimum. Moreover, since the relative degree of the relevant system output is suitably augmented through the use of an integrator, the control action affecting the robotic system is continuous, with a significant benefit, in terms of chattering alleviation, for the overall controlled electromechanical system. The verification and validation of our proposal have been performed by simulating the motion control scheme relying on a model of the considered robot, i.e. a COMAU SMART3-S2 anthropomorphic industrial robot manipulator, identified on the basis of real data. © 2013 IEEE

Design of robust Higher Order Sliding Mode control for microgrids

This paper deals with the design of advanced control strategies of sliding mode type for microgrids. Each distributed generation unit (DGu), constituting the considered microgrid, can work in both grid-connected operation mode (GCOM) and islanded operation mode (IOM). The DGu is affected by load variations, nonlinearities and unavoidable modelling uncertainties. This makes sliding mode control particularly suitable as a solution methodology for the considered problem. In particular, a second order sliding mode (SOSM) control algorithm, belonging to the class of Suboptimal SOSM control, is proposed for both GCOM and IOM, while a third-order sliding mode (3-SM) algorithm is designed only for IOM, in order to achieve, also in this case, satisfactory chattering alleviation. The microgrid system controlled via the proposed sliding mode control laws exhibits appreciable stability properties, which are formally analyzed in the paper. Simulation results also confirm that the obtained closed-loop performances comply with the IEEE recommendations for power systems

A Study of Advanced Modern Control Techniques Applied to a Twin Rotor MIMO System

The twin rotor MIMO system (TRMS) is a helicopter-like system that is restricted to two degrees of freedom, pitch and yaw. It is a complicated nonlinear, coupled, MIMO system used for the verification of control methods and observers. There have been many methods successfully applied to the system ranging from simple proportional integral derivative (PID) controllers, to machine learning algorithms, nonlinear control methods and other less explored methods like deadbeat control and various optimal methodologies. This thesis details the design procedure for two different control methods. The first is a suboptimal tracking controller using a linear quadratic regulator (LQR) with integral action. The second is the design of several adaptive sliding mode controller to provide robust tracking control of the TRMS. Once the design is complete the controllers are tested in simulation and their performance is compared against a PID controller experimentally. The performance of the controllers are also compared against other controllers in the literature. The ability of the sliding mode controllers (SMC) to suppress chattering is also be explored

The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators

Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft