Panagiotis S. Trakas

In this article, we introduce an adaptive cruise control (ACC) scheme that ensures collision avoidance by enforcing amplitude-and-rate constraints on the control input, while dynamically adapting output performance specifications. The proposed control strategy leverages the adaptive performance control (APC) methodology to handle asymmetrical input constraints, providing robust performance under diverse driving conditions. By incorporating multiple constraints into a unified funnel-based approach, the controller eliminates the need for complex optimization techniques, thereby maintaining low computational complexity. A rigorous theoretical analysis is provided, demonstrating the boundedness of all closed-loop signals and the overall feasibility of the control design. Comprehensive and comparative simulations highlight the ability of the proposed scheme to maintain a safe intervehicular distance, achieve accurate velocity tracking when state constraints (SCs) allow, and strict adherence to input limitations, resulting in improved passenger comfort. Finally, experimental validation using mobile robots confirms the effectiveness of the proposed ACC scheme in real-world conditions.

In this work, we propose an approximation-free adaptive performance control scheme for unknown high-relative degree, multi-input–multioutput (MIMO) nonlinear systems with saturation on the control input signal. We introduce a novel reconciling adaptive modification of the predefined performance specifications based on the input constraints of the controlled plant, providing the best-feasible output performance. The automatic gain tuning in combination with the simplicity of the proposed controller enhance its robustness and enable its easy deployment in practical scenarios. Notably, the introduced control methodology ensures the necessary compromise between input-output constraints on a semi-global sense for ISS systems. However, the stability attributes for general nonlinear systems are inevitably limited to compact domains due to the inherent conflict between performance demand and actuation capability. In this context, we provide a sufficient closed-loop stability criterion through Lyapunov analysis. Finally, illustrative simulation studies and experimental results clarify and verify the efficacy of the proposed controller.

In this paper, we address the trajectory-/target-tracking and obstacle-avoidance problem for nonholonomic mobile robots subjected to diamond-shaped velocity constraints and predefined output performance specifications. The proposed scheme leverages the adaptive performance control to dynamically adjust the user-defined output performance specifications, ensuring compliance with input and safety constraints. A key feature of this approach is the integration of multiple constraints into a single adaptive performance function, governed by a simple adaptive law. Additionally, we introduce a robust velocity estimator with a priori-determined performance attributes to reconstruct the unmeasured trajectory/target velocity. Finally, we validate the effectiveness and robustness of the proposed control scheme, through extensive simulations and a real-world experiment.

This paper presents a distributed approach to the motion control problem for a platoon of unicycle robots moving through an unknown environment filled with static obstacles under multiple hard and soft operational constraints. Each robot has an onboard camera to determine its relative position in relation to its predecessor and proximity sensors to detect and avoid nearby obstascles. Moreover, no robot apart from the leader can independently localize itself within the given workspace. To overcome this limitation, we propose a novel distributed control protocol for each robot of the fleet, utilizing the Adaptive Performance Control (APC) methodology. By utilizing the APC approach to address input constraints via the on-line modification of the error specifications, we ensure that each follower effectively tracks its predecessor without encountering collisions with obstacles, while simultaneously maintaining visual contact with its preceding robot, thus ensuring the inter-robot visual connectivity. Finally, extensive simulation results are presented to demonstrate the effectiveness of the presented control system along with a real-time experiment conducted on an actual robotic system to validate the feasibility of the proposed approach in real-world scenarios.

This study focuses on addressing the problem of motion planning within workspaces cluttered with obstacles while considering temporal and input constraints. These specifications can encapsulate intricate high-level objectives involving both temporal and spatial constraints. The existing literature lacks the ability to fulfill time specifications while simultaneously managing input-saturation constraints. The proposed approach introduces a hybrid three-component control algorithm designed to learn the safe execution of a high-level specification expressed as a timed temporal logic formula across predefined regions of interest in the workspace. The first component encompasses a motion controller enabling secure navigation within the minimum allowable time interval dictated by input constraints, facilitating the abstraction of the robot’s motion as a timed transition system between regions of interest. The second component utilizes formal verification and convex optimization techniques to derive an optimal high-level timed plan over the mentioned transition system, ensuring adherence to the agent’s specification. However, the necessary navigation times and associated costs among regions are initially unknown. Consequently, the algorithm’s third component iteratively adjusts the transition system and computes new plans as the agent navigates, acquiring updated information about required time intervals and associated navigation costs. The effectiveness of the proposed scheme is demonstrated through both simulation and experimental studies.

A decentralized robust control protocol addressing leader-follower formation control of unknown nonlinear input-constrained multi-agent systems with adaptive performance specifications is proposed in this paper. The performance characteristics predefined by the user are adaptively modified in order to comply with the actuation constraints of the agents regarding both the magnitude and the rate of the control signals, ensuring closed-loop stability. The proposed control protocol is characterized by easy gain tuning and low structural complexity which simplifies the integration to real systems. A thorough experiment involving a system of multiple quadrotors was conducted to clarify and verify the theoretical findings.

This paper presents a novel robust reference governor (RG) for trajectory tracking of quadrotors. The proposed scheme is characterized by low computational complexity and straightforward gain selection. Moreover, it considers safety constraints regarding speed limits and ensures the stability and the proper operation of the closed-loop system. The proposed scheme imposes user-specified performance attributes on the evolution of the tracking error when the safety constraints allow it. When these constraints are at risk of violation, the proposed RG provides a relaxation of the predefined performance specifications to ensure the stability of the plant. Lyapunov analysis proves the boundedness of the closed-loop signals, while its efficacy is further clarified and verified via extensive comparative experimental results against a well-established PI regulator.

A novel approximation-free adaptive prescribed performance control scheme for the longitudinal motion tracking of input and state constrained small fixed-wing UAVs is designed in this work. The proposed controller employs the adaptive prescribed performance technique to impose output performance specifications in accordance with actuation limitations regarding the amplitude and the rate of the control signal. Furthermore, state constraints are introduced to ensure the proper operation of the closed-loop system. The adoption of PPC methodology results in a low complexity control algorithm with easy gain selection which facilitates its practical implementation. Finally, a comprehensive simulation study as well as comparative simulation paradigms on an Aerosonde model clarifies and verifies the superiority and the effectiveness of the proposed controller to control the longitudinal motion of UAVs in the presence of wind gusts.

A universal approximation-free adaptive prescribed performance control scheme is designed for unknown SISO nonlinear systems with input saturation. The proposed control method introduces a compromising relaxation of output performance specifications depending on the input limitations. Given the conflicting nature between input and output constraints, the stability properties are inevitably local. In this respect, a sufficient stability condition for the closed-loop system is provided through theoretical analysis. Owing to the adopted prescribed performance control technique, the satisfaction of the aforementioned stability properties guarantees the desired trade-off between input and output constraints. Moreover, no hard calculations are needed, neither for the controller nor for the adaptive law, maintaining the complexity of the control algorithm relatively low. Finally, the proposed approach is clarified and verified by various simulation studies.