We address the problem of searching for an unknown number of stationary targets at unknown positions with a mobile agent. A probability hypothesis density filter is used to estimate the expected number of targets under measurement uncertainty. Existing planners, such as Active Search (AS) and its Intermittent variant (ASI), achieve accurate detection but require costly online optimization. To reduce online computation, we propose to use a convolutional neural network to approximate AS or ASI decisions through direct inference. The network is trained on AS/ASI data using a multi-channel grid that encodes target beliefs, the agent position, visitation history, and boundary information. Simulations with uniform and clustered target distributions show that the network achieves detection rates comparable to AS or ASI while reducing computation by orders of magnitude.

This paper presents an optimal control scheme for a class of nonlinear systems subject to input saturation and external disturbances, achieved through the integration of reinforcement learning (RL) and a self-regulated prescribed performance control (SRPPC) algorithm. The proposed architecture employs an Identifier-Critic-Actor RL structure, implemented via interval type-2 fuzzy logic systems (IT2FLSs), to accurately approximate unknown nonlinear dynamics while optimizing control performance. To circumvent the singularity issues inherent in conventional PPC, the SRPPC strategy is developed to dynamically initialize the prescribed performance bounds (PPBs) and autonomously relax them during severe disturbances or actuator saturation, thereby maintaining the tracking error within a feasible envelope. By synergizing RL-based optimization with the SRPPC safety mechanism, the resulting RL-SRPPC controller not only minimizes operational costs but also guarantees transient safety and strict adherence to performance specifications. Numerical simulations on a one-link manipulator demonstrate the robustness and superiority of the proposed scheme compared to existing methodologies.

We consider the classical emulation paradigm in which a controller is already designed for a linear time-invariant plant. Motivated by implementation constraints in real applications, we analyze the effects of ubiquitous low-cost quantizers on the closed-loop dynamics. Consequently, we address the robust control problem of an uncertain discrete-time linear process using a regulator affected by the effects of uniform quantization performed by the input-output converters and arithmetical unit. In this setup with fixed hardware resolutions, the regulator's state-space realization is balanced to minimize the process' state quantization error while simultaneously maintaining its desired transient response. To characterize the quantization error, we provide an ultimate bound for its worst-case scenario using the input-to-state stability framework. The minimization is performed using off-the-shelf tools, with a characterization of the resulting problem. Finally, a comparative numeric case study showing the tightness of the computed bound is discussed.

Conventional fault-tolerant control (FTC) schemes typically assume the exponent of the faulty input to be 1, overlooking its impact on actuator power. In this article, we propose a novel FTC strategy that extends the exponent to any positive odd integer, thus capturing higher-order fault effects. In addition, by integrating a Gaussian function to modify the constraint boundaries, a novel suction-cup-type prescribed performance function is proposed. Unlike existing prescribed performance functions, this design uses a suction cup module to regulate output overshoot without requiring asymmetric design. This design is globally effective, eliminating the initial feasibility conditions. Simulation results validate the effectiveness of the proposed scheme.

In this paper, we investigate potential singularity issues in prescribed performance control induced by actuator faults and propose a global nonmonotonic prescribed performance scheme for strict-feedback nonlinear systems. This scheme allows for parameterized relaxation of constraint boundaries in the presence of actuator faults. Unlike existing prescribed performance control schemes that rely on redesigning nonmonotonic rate functions to modify boundary relaxation properties, the proposed method introduces a novel adjustment module into the prescribed performance function, enabling the parametric design of nonmonotonic boundaries. Moreover, the proposed method simultaneously addresses the removal of the initial feasibility condition and the imposition of asymmetric constraints by introducing a global asymmetric design with an error correction module. This module enables flexible conversion from asymmetric to symmetric boundaries at a predetermined time instant, thereby reducing transient overshoot while maintaining steady-state tracking precision. Simulation results validate the effectiveness and superiority of the proposed scheme.

This paper investigates and compares two control strategies for a DC motor actuating a joint of a rehabilitation robot intended for upper-limb recovery. A classical PD controller and a model-based LQG approach, are designed based on identified dynamics. Both controllers are first evaluated in simulation and are then tested on the physical motor using identical reference trajectories. Performance is assessed in terms of tracking accuracy, steady-state error, and sensitivity to measurement noise and modelling errors. The results highlight the practical trade-offs between the simplicity and tuning effort of the PD controller and the improved estimation and optimal feedback properties of the LQG scheme.

This paper presents the online identification, based on finite impulse response filter coefficients, of a DC motor. The coefficients are obtained based on least mean-square identification using experimental data. They are used to construct the Hankel matrix based on which the mathematical model is determined. The results are subsequently compared with a standard method in Matlab. The method is applied in real time directly on a low-cost development board, where it successfully replicates the identification process previously tested in simulation. The results obtained online confirm the accuracy and reliability of the approach in a real-time setting. This work bridges the gap between theoretical system identification and practical real-time implementation, enabling motor identification with accessible hardware. The proposed approach supports rapid prototyping, educational use, and cost-effective industrial applications, particularly in scenarios that require real-time system monitoring.

Cybersecurity is becoming a pressing issue in networked control systems and cyber-physical systems. The current paper proposes a resilient networked control methodology for small drones, in the case of man-in-the-middle cyberattacks. An adaptive sliding mode controller is used as a low level mitigation approach, together with redundant encrypted data for high level mitigation. This combination ensures both robustness and resilience for the networked control of a drone with limited computational resources. The whole approach is validated through experiments on a Parrot Mambo drone.

This research proposes a neural network-based super-twisting controller for robot joints. A modified fast nonsingular terminal sliding surface is introduced, which not only avoids singularity but also increases the convergence rate of the sliding mode control. To address the challenge of system uncertainty modeling, a type-2 fuzzy single hidden layer recurrent neural network (T2FSHLRNN) is proposed. The T2FSHLRNN, configured as a weighted combination of a type-2 fuzzy neural network and a single hidden layer network, demonstrates strong global learning ability. Leveraging its internal and external double-layer feedback mechanism, the network can incorporate both current and previous error information during the approximation process, effectively improving the approximation accuracy and reducing system chattering. Furthermore, an adaptive gain function is proposed and an adaptive terminal super-twisting controller based on T2FSHLRNN (ATSC-T2FSHLRNN) is developed. The system's stability under unknown disturbance is ensured using Lyapunov synthesis. Based on this, the online parameter learning algorithm for T2FSHLRNN and the variable gains of ATSC are derived. Simulation confirms the effectiveness of the proposed ATSC-T2FSHLRNN.

In order to meet the performance requirements of permanent magnet synchronous motor (PMSM) systems with time-varying model parameters and input constraints under step load, this paper proposes a dynamic prescribed performance fuzzy-neural backstepping control approach. Firstly, a novel finite-time asymmetric dynamic prescribed performance function (FADPPF) is proposed to tackle the issues of exceeding predefined error, control singularity, and system instability that arise in the traditional prescribed performance function under load changes. To address model accuracy degradation and control quality deterioration caused by nonlinear time-varying parameters and input constraints in the PMSM system, a backstepping controller is designed by combining the speed function (SF), fuzzy neural network (FNN), and the proposed FADPPF. The FNN approximates nonlinear uncertain functions in the system model; the SF, as an error amplification mechanism, works together with FADPPF to ensure the transient and steady-state performance of the system. The stability of the devised control strategy is proved using Lyapunov analysis. Finally, simulation results demonstrate the dynamic self-adjusting ability and effectiveness of FADPPF under step load. In addition, the feasibility and superiority of the proposed control scheme are validated.

Passivity of a large-scale interconnected system is often broken down to the passivity of the individual subsystems that compose it. Nevertheless, there are cases in which the individual elements are not all passive, yet the overall large-scale system is. In such scenarios, we need to directly solve very large problems to conclude on the passivity. This letter proposes a methodology to analyze passivity based on the topology of the multi-agent system. In many cases, large multi-agent systems are formed by interconnected clusters, which are groups of agents densely interconnected. The clusters are sparsely interconnected with each other and this leads to a time scale-separation with a fast dynamics inside the clusters and a slow one between them. The purpose of this letter is twofold. First, we exploit the time-scale separation property inherent to such a system to provide a computationally efficient alternative to analyze its passivity. Second, we provide insight into how robust its passivity is with respect to the inter-and intra-cluster agent interactions. To achieve this, we consider the singular perturbation framework with respect to the ratio of the strength of the controls between and within the clusters, and rely on the connection between positive realness, passivity, and multi-input multi-output system phase. We consider agents with identical linear time-invariant dynamics. The method is illustrated on a numerical example.

This paper investigates the practical application and evaluation of four adaptive algorithms: Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Recursive Least Square (RLS) and Affine Projection (AP) for the identification of a DC motor. The work focuses on dynamic system identification, where the adaptability and efficiency of numerical filters play an important role. To identify the parameters of a DC motor, these adaptive algorithms are used on measured data. With the aim of having a shorter processing time, the data was downsampled. Satisfactory results were obtained with all algorithms. Finally, the difficulty of implementing the algorithms, the errors obtained and the processing time were analyzed.

Consider a drone that aims to find an unknown number of static targets at unknown positions as quickly as possible. A multi-target particle filter uses imperfect measurements of the target positions to update an intensity function that represents the expected number of targets. We propose a novel receding-horizon planner that selects the next position of the drone by maximizing an objective that combines exploration and target refinement. Confidently localized targets are saved and removed from consideration along with their future measurements. A controller with an obstacle-avoidance component is used to reach the desired waypoints. We demonstrate the performance of our approach through a series of simulations as well as via a real-robot experiment in which a Parrot Mambo drone searches from a constant altitude for targets located on the floor. Target measurements are obtained on-board the drone using segmentation in the camera image, while planning is done off-board. The sensor model is adapted to the application. Both in the simulations and in the experiments, the novel framework works better than the lawnmower and active-search baselines.

Consider a multi-agent system that must find an unknown number of static targets at unknown locations as quickly as possible. To estimate the number and positions of targets from noisy and sometimes missing measurements, we use a customized particle-based probability hypothesis density filter. Novel methods are introduced that select waypoints for the agents in a decoupled manner from taking measurements, which allows optimizing over waypoints arbitrarily far in the environment while taking as many measurements as necessary along the way. Optimization involves control cost, target refinement, and exploration of the environment. Measurements are taken either periodically, or only when they are expected to improve target detection, in an event-triggered manner. All this is done in 2D and 3D environments, for a single agent as well as for multiple homogeneous or heterogeneous agents, leading to a comprehensive framework for (Multi-Agent) Active target Search with Intermittent measurements - (MA)ASI. In simulations and real-life experiments involving a Parrot Mambo drone and a TurtleBot3 ground robot, the novel framework works better than baselines including lawnmowers, mutual-information-based methods, active search methods, and our earlier exploration-based techniques.

We consider two nonlinear state estimation problems in a setting where an extended Kalman filter receives measurements from two sets of sensors via two channels (2C). In the stochastic-2C problem, the channels drop measurements stochastically, whereas in 2C scheduling, the estimator chooses when to read each channel. In the first problem, we generalize linear-case 2C analysis to obtain -- for a given pair of channel arrival rates -- boundedness conditions for the trace of the error covariance, as well as a worst-case upper bound. For scheduling, an optimization problem is solved to find arrival rates that balance low channel usage with low trace bounds, and channels are read deterministically with the expected periods corresponding to these arrival rates. We validate both solutions in simulations for linear and nonlinear dynamics; as well as in a real experiment with an underwater robot whose position is being intermittently found in a UAV camera image.

This article presents an adaptive fuzzy control scheme capable of guaranteeing prescribed performance for stochastic nonlinear systems with unknown control directions. Unlike the majority of existing prescribed performance control schemes, the proposed scheme ensures the independence from initial errors and guarantees controllable overshoot. Moreover, the proposed prescribed function exhibits nonmonotonicity, which can be beneficial in control applications with input constraints. To address the challenge posed by unknown control directions, a novel class of multiple Nussbaum functions is introduced. Compared to the existing single Nussbaum function, the multiple Nussbaum functions can mitigate instability arising from the cancelation of multiple unknown signs. Additionally, to tackle unknown nonlinearities, a single-parameter fuzzy approximator is introduced, aiming to concurrently reduce computational complexity. Furthermore, a novel class of switching threshold event-triggered mechanisms is designed to address issues encountered in existing designs where parameter inequalities impose conservative constraints. The control scheme ensures that the tracking error converges to prescribed asymmetric boundaries with arbitrarily small residuals in a prescribed time, while also guaranteeing that all closed-loop signals are bounded in probability. The effectiveness and superiority of the control scheme are verified by simulation results.

The microelectromechanical system (MEMS) gyroscope is a complex nonlinear system with multiple variables, strong coupling, and susceptibility to stochastic disturbances. This article presents an adaptive fuzzy control scheme for stochastic MEMS gyroscopes, with the primary objectives of reducing control vibration and achieving high precision prescribed performance tracking with low communication resources within a fixed-time backstepping framework. To address the stochastic disturbances and unknown nonlinear system dynamics, the interval type-3 fuzzy logic system is introduced. In addition, a novel quadratic prescribed performance function is proposed to ensure satisfactory transient and steady-state performance of the system while mitigating initial control vibrations during fast error convergence. Furthermore, an event-triggered mechanism is developed using a switching threshold strategy to minimize the communication load without compromising control accuracy. By utilizing the fixed-time command-filtered backstepping design method and newly introduced error-compensating signals, the issue of "explosion of complexity" is effectively resolved, and filtering errors are adequately compensated. The proposed control scheme guarantees that the tracking errors converge to a predefined set of arbitrarily small residuals in probability. In addition, all the closed-loop signals are within a fixed time bounded in probability. The simulation results validate the effectiveness and superiority of the proposed scheme.

We propose a controller design method for time-delay nonlinear systems with delays affecting both the states and the inputs, represented by Takagi-Sugeno fuzzy models with nonlinear consequents. To handle the nonlinearities in the consequents we assume that they are slope-bounded. Linear matrix inequality conditions are formulated to design the controller. The obtained results are compared to state-of-the-art approaches and illustrated on two examples.

The current study proposes a network control structure for small low-cost drones like the Parrot Mambo mini-drone. The structure is composed of an inner loop running on the drone, and an outer loop running on a remote computer. The inner loop controls the attitude and altitude of the drone based on Kalman filter estimations from the onboard sensors. The outer loop ensures position tracking based on measurements from OptiTrack cameras. A time delay compensator is added to address the constraints imposed by wireless network communications between the drone and the remote computer. Experimental results using Parrot Mambo drones show good stability and tracking performances, despite model uncertainty and time delay.

The passivity of singularly perturbed systems (SPSs) is generally studied without taking advantage of the time-scale separation present in this class of systems. To fill this gap, the objective of this letter is to provide easy-to-verify well-posed conditions characterizing the passivity of a perturbation variable-dependent SPS starting from the passivity of its associated reduced-order system. To achieve this goal, we rely on the connection between positive realness and passivity, as well as the notion of phase for multi-input multi-output (MIMO) systems. We use a benchmark DC motor to illustrate that classical reasoning used for stability analysis of SPSs, which is based on the stability of the reduced-order (slow) and boundary layer (fast) subsystems, cannot be applied to guarantee the passivity of an SPS. On top of that, our methodology explains how the time-scale separation can be used to analyze the passivity of general linear time-invariant (LTI) systems. The approach is illustrated on a numerical example.