Synthesis-guided Adversarial Scenario Generation for Gray-box Feedback Control Systems with Sensing Imperfections

In this paper, we study feedback dynamical systems with memoryless controllers under imperfect information. We develop an algorithm that searches for “adversarial scenarios”, which can be thought of as the strategy for the adversary representing the noise and disturbances, that lead to safety violations. The main challenge is to analyze the closed-loop system's vulnerabilities with a potentially complex or even unknown controller in the loop. As opposed to commonly adopted approaches that treat the system under test as a black-box, we propose a synthesis-guided approach, which leverages the knowledge of a plant model at hand. This hence leads to a way to deal with gray-box systems (i.e., with known plant and unknown controller). Our approach reveals the role of the imperfect information in the violation. Examples show that our approach can find non-trivial scenarios that are difficult to expose by random simulations. This approach is further extended to incorporate model mismatch and to falsify vision-in-the-loop systems against finite-time reach-avoid specifications.

Design of Robust Fopi-Fopd Controller for Maglev System Using Particle Swarm Optimization

Magnetic Levitation System (MLS) is one of the benchmark laboratories models for designing and testing feedback control systems in the presence of the parametric uncertainties and disturbances effect. Therefore, the MLS can be regarded as a tool to study and verify a certain robust controller design. In this paper, two types of powerful control schemes are presented to control the MLS. The first controller is a robust PI-PD controller, while the other is a robust fractional order FOPI-FOPD controller which provides two extra degrees of freedom to the system. In both controller design procedures, the Particle Swarm Optimization (PSO) algorithm is used to find the best values of controller parameters subject to the time-domain objective function and H∞ constraints. All modeling processes including parameterization, optimization, and validation of the controllers are performed using MATLAB. The simulation results show that the MLS with robust FOPI-FOPD is faster and more stable than the MLS with robust classical PI-PD. Also, the proposed FOPI-FOPD controller gives far superior results than the PI-PD controller for disturbance rejection.

The WiFly: Flapping-Wing Small Unmanned Aerial Vehicle with Center-of-Gravity Shift Mechanism

This paper describes the development of a flapping-wing unmanned aerial vehicle (UAV) named WiFly, which is equipped with a center-of-gravity (COG) shift mechanism. This mechanism allows seamless changes in the flight attitude between hovering and level flight by controlling the pitch angle. We implemented two types of feedback control systems in WiFly: PID control and reinforcement learning (shallow Q-learning) to stabilize the flight attitude. The controllability of WiFly is drastically improved by employing a double-motor drive system to independently control the flipping frequencies of the left and right wings.

Achieving natural behavior in a robot using neurally inspired hierarchical control

Terrestrial locomotion presents tremendous computational challenges on account of the enormous degrees of freedom in legged animals, and the complex and unpredictable properties of the natural environment and the effectors. Yet the nervous system can achieve locomotion with ease. Here we introduce a quadrupedal robot capable of goal-directed posture control and locomotion over rough terrain. The underlying control architecture is a hierarchical network of simple negative feedback control systems inspired by the organization of the vertebrate nervous system. Without using an internal model or feedforward planning, and without any training, our robot shows robust posture control and locomotor behavior in novel environments with unpredictable disturbances.

Active Approaches to Vibration Absorption through Antiresonance Assignment: A Comparative Study

Vibration absorption is a core research topic in structural dynamics and the mechanics of machines, and antiresonance assignment is an effective solution to such a problem in the presence of harmonic excitation forces. Due to recent developments in the theory of feedback control systems, the use of active control approaches to antiresonance assignment has been recently gaining more attention in the literature. Therefore, several methods exploiting state feedback or output feedback have been proposed in recent years. These techniques that just rely on servo-controlled actuators are becoming an interesting alternative to active approaches that emulate the well-known Tuned Mass Damper in an active (or semi-active) framework. This paper reviews and compares the most important approaches, with a greater focus on the methods exploiting the concept of control theory without adding new degrees of freedom in the system. The theoretical results, with the underlying theory, are discussed to highlight the key features of each assignment techniques. Several numerical examples where different techniques are applied and compared, also providing some analysis usually neglected in the literature, enrich the paper and demonstrate the key concepts.