Shahab Heshmati-alamdari

Bio: Shahab Heshmati-alamdari is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Model predictive control & Workspace. The author has an hindex of 12, co-authored 36 publications receiving 497 citations. Previous affiliations of Shahab Heshmati-alamdari include National and Kapodistrian University of Athens & National Technical University of Athens.

Trajectory Tracking With Prescribed Performance for Underactuated Underwater Vehicles Under Model Uncertainties and External Disturbances

Abstract: This paper addresses the tracking control problem of 3-D trajectories for underactuated underwater robotic vehicles. Our recent theoretical results on the prescribed performance control of fully actuated nonlinear systems are innovatively extended on the control of the most common types of underactuated underwater vehicles, namely, the torpedo-like (i.e., vehicles actuated only in surge, pitch, and yaw) and the unicycle-like (i.e., vehicles actuated only in surge, heave, and yaw). The main contributions of this paper concentrate on: 1) the reduced design complexity; 2) the increased robustness against system uncertainties; 3) the prescribed transient and steady-state performance; and 4) the minimal tracking information requirements. A comparative simulation study points out the intriguing performance properties of the proposed method, while its applicability is experimentally verified using a small unicycle-like underactuated underwater vehicle in a test tank.

Robust Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles in Uncertain Environments

Abstract: This article addresses the tracking control problem of 3-D trajectories for underactuated underwater robotic vehicles operating in a constrained workspace including obstacles More specifically, a robust nonlinear model predictive control (NMPC) scheme is presented for the case of underactuated autonomous underwater vehicles (AUVs) (ie, unicycle-like vehicles actuated only in the surge, heave, and yaw) The purpose of the controller is to steer the unicycle-like AUV to the desired trajectory with guaranteed input and state constraints (eg, obstacles, predefined vehicle velocity bounds, and thruster saturations) inside a partially known and dynamic environment where the knowledge of the operating workspace is constantly updated via the vehicle’s onboard sensors In particular, considering the sensing range of the vehicle, obstacle avoidance with any of the detected obstacles is guaranteed by the online generation of a collision-free trajectory tracking path, despite the model dynamic uncertainties and the presence of external disturbances representing ocean currents and waves Finally, realistic simulation studies verify the performance and efficiency of the proposed framework Note to Practitioners —This article was motivated by the problem of robust trajectory tracking for an autonomous underwater vehicle (AUV) operating in an uncertain environment where the knowledge of the operating workspace (eg, obstacle positions) is constantly updated online via the vehicle’s onboard sensors (eg, multibeam imaging sonars and laser-based vision systems) In addition, there may be other system limitations (eg, thruster saturation limits) and other operational constraints, induced by the need of various common underwater tasks (eg, a predefined vehicle speed limit for inspecting the seabed, and mosaicking), where it should also be considered into the control strategy However, based on the existing trajectory tracking control approaches for underwater robotics, there is a lack of an autonomous control scheme that provides a complete and credible control strategy that takes the aforementioned issues into consideration Based on this, we present a reliable control strategy that takes into account the aforementioned issues, along with dynamic uncertainties of the model and the presence of ocean currents In future research, we will extend the proposed methodology for multiple AUV performing collaborative inspection tasks in an uncertain environment

Self-triggered Model Predictive Control for nonholonomic systems

Abstract: This paper proposes a Model Predictive Control (MPC) framework combined with a self-triggering mechanism for constrained uncertain systems. Under the proposed scheme, the control input as well as the next control update time are provided at each triggering instant. Between two consecutive triggering instants, the control trajectory given by the MPC is applied to the plant in an open-loop fashion. This results to less frequent computations while preserving stability and convergence of the closed-loop system. A scenario for the stabilization of a nonholonomic robot subject to constraints and disturbances is considered, with the aim of reaching a specific triggering mechanism. The robot under the proposed control framework is driven to a compact set where it is ultimately bounded. The efficiency of the proposed approach is illustrated through a simulated example.

A Robust Predictive Control Approach for Underwater Robotic Vehicles

Abstract: This article presents a robust nonlinear model predictive control (NMPC) scheme for autonomous navigation of underwater robotic vehicles operating in a constrained workspace including the static obstacles. In particular, the purpose of the controller is to guide the vehicle toward specific way points with guaranteed input and state constraints. Various constraints, such as obstacles, workspace boundaries, predefined upper bounds for the velocity of the robotic vehicle, and thruster saturations, are considered during the control design. Moreover, the proposed control scheme is designed at dynamic level, and it incorporates the full dynamics of the vehicle in which the ocean currents are also involved. Hence, taking the thrusts as the control inputs of the robotic system and formulating them accordingly, the vehicle exploits the ocean current dynamics when these are in favor of the way-point tracking mission, resulting in reduced energy consumption by the thrusters. The robustness of the closed-loop system against parameter uncertainties has been analytically guaranteed with convergence properties. The performance of the proposed control strategy is experimentally verified using a 4 degrees of freedom (DoF) underwater robotic vehicle inside a constrained test tank with sparse static obstacles.

Guidance And Control Of Ocean Vehicles

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Modelling and Control of Robot Manipulators

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DeepMPC: Learning Deep Latent Features for Model Predictive Control

Abstract: Designing controllers for tasks with complex nonlinear dynamics is extremely challenging, time-consuming, and in many cases, infeasible. This difficulty is exacerbated in tasks such as robotic food-cutting, in which dynamics might vary both with environmental properties, such as material and tool class, and with time while acting. In this work, we present DeepMPC, an online real-time model-predictive control approach designed to handle such difficult tasks. Rather than hand-design a dynamics model for the task, our approach uses a novel deep architecture and learning algorithm, learning controllers for complex tasks directly from data. We validate our method in experiments on a large-scale dataset of 1488 material cuts for 20 diverse classes, and in 450 real-world robotic experiments, demonstrating significant improvement over several other approaches.

Platoon Formation Control With Prescribed Performance Guarantees for USVs

Abstract: This paper studies the platoon formation control problem for unmanned surface vehicles, in the presence of modeling uncertainties and time-varying external disturbances. The control objective is to make the vehicular platoons proceed along a given trajectory while maintaining a desired line-of-sight (LOS) range between each vehicle and its predecessor. To provide transient performance specifications on formation errors, including LOS range and angle errors, we enforce prescribed performance guarantees in the control design. The prescribed performance guarantees mean that formation errors evolve always within the predefined regions that are bounded by exponentially decaying functions of time. Using prescribed performance control methodology, neural network approximation, disturbance observers, dynamic surface control technique, and Lyapunov synthesis, we propose an adaptive formation control that ensures internal stability of closed-loop systems with guaranteed prescribed performance. Meanwhile, both collision avoidance and connectivity maintenance between two consecutive vehicles are guaranteed during the whole operation. The proposed formation control is decentralized in the sense that the control action on each vehicle depends only on information from its immediate predecessor. Simulation results demonstrate the performance of the proposed control.

Path Following of a Surface Vessel With Prescribed Performance in the Presence of Input Saturation and External Disturbances

Abstract: This paper presents a path following controller of a surface vessel with a prescribed performance in the presence of input saturation and external disturbances. Based on the three degrees-of-freedom model of the surface vessel, the designed backstepping control scheme features three functional parts, namely, guidance, attitude control, and velocity control. To guarantee that the position errors are confined within the prescribed convergence rates and maximum overshoot, a performance constrained guidance law is formulated with an error transformed function. Command filters are incorporated in the control subsections to limit the magnitude of the virtual controls and simultaneously avoid arduous computations involving their time derivatives. Subsequently, auxiliary systems that are governed by smooth switching functions are developed in an unprecedented manner to compensate for the saturation constraints on actuators. Nonlinear disturbance observers are concurrently introduced to estimate the unknown external disturbances for increasing system's robustness. It is demonstrated that under the proposed control, the prescribed transient and steady tracking performance bounds are never violated, and all closed-loop signals remain uniformly ultimately bounded, despite the presence of input saturation and disturbances. Results from a comparative simulation study illustrate the effectiveness and advantages of the proposed method.