Related papers: Constraint Control of a Boom Crane System
The precise and safe control of heavy material handling machines presents numerous challenges due to the hard-to-model hydraulically actuated joints and the need for collision-free trajectory planning with a free-swinging end-effector tool.…
This paper presents a real time control strategy for energy storage systems integration in electric vehicles fast charging applications combined with generation from intermittent renewable energy sources. A two steps approach taking…
This article focuses on the problem of adaptive tracking control for a specific type of nonlinear system that is subject to full-state constraints via a hybrid event-triggered control (HETC) strategy. With the auxiliary system, we proposed…
Ensuring safe real-time control of ship-mounted cranes in unstructured transportation environments requires handling multiple safety constraints while maintaining effective payload transfer performance. Unlike traditional crane systems,…
The present study proposes a novel collaborative traffic engineering scheme for networks of autonomous systems. Backpressure routing principles are used for deriving priority routing rules that optimally stabilize a network, while…
Connected and autonomous vehicles have the potential to minimize energy consumption by optimizing the vehicle velocity and powertrain dynamics with Vehicle-to-Everything info en route. Existing deterministic and stochastic methods created…
In order to cope with the explosive growth of data traffic which is associated with a wide plethora of emerging applications and services that are expected to be used by both ordinary users and vertical industries, the congestion control…
Inverter-based resources (IBRs) are becoming increasingly prevalent in power systems. Due to the inherently low inertia of inverters, there is a heightened risk of disruptive voltage oscillations. A particular challenge in the operation of…
We present an improvement of a recent funnel controller design for uncertain nonlinear multi-input, multi-output systems modeled by higher order functional differential equations in the presence of input constraints. The objective is to…
We propose a general strategy for feedback control design of complex dynamical systems exploiting the nonlinear mechanisms in a systematic unsupervised manner. These dynamical systems can have a state space of arbitrary dimension with…
Deep Reinforcement Learning (DRL) has become a popular method for solving control problems in power systems. Conventional DRL encourages the agent to explore various policies encoded in a neural network (NN) with the goal of maximizing the…
Motivated by applications in queueing theory, we consider a stochastic control problem whose state space is the $d$-dimensional positive orthant. The controlled process $Z$ evolves as a reflected Brownian motion whose covariance matrix is…
This paper treats an optimal scheduling problem of control nodes in networked systems. We newly introduce both the L0 and l0 constraints on control inputs to extract a time-varying small number of effective control nodes. As the cost…
After over a century of internal combustion engines ruling the transport sector, electric vehicles appear to be on the verge of gaining traction due to a slew of advantages, including lower operating costs and lower CO2 emissions. By using…
Positive systems describing networks with inherently non-negative states and inputs arise naturally in routing, logistics, and compartmental modelling. We consider problems modelled as positive linear systems in incidence form with linear…
Model-free algorithms are brought into the control system's research with the emergence of reinforcement learning algorithms. However, there are two practical challenges of reinforcement learning-based methods. First, learning by…
This paper presents a computationally efficient solution for constraint management of multi-input and multi-output (MIMO) systems. The solution, referred to as the Decoupled Reference Governor (DRG), maintains the highly-attractive…
This paper proposes a novel control architecture for state and input constrained Euler-Lagrange (E-L) systems with parametric uncertainties. A simple saturated controller is strategically coupled with a Barrier Lyapunov Function (BLF) based…
A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interference channel. Towards this end, we first present PCNet, which…
In this paper, a novel recurrent adaptive backstepping optimal control strategy for a single inverted pendulum system is studied. By this method, an inverted pendulum is stabilized using projection recurrent neural network-based adaptive…