With the continuous development of industries such as wind power and construction, the weight and complexity of lifted equipment have significantly increased. These lifting tasks often rely on crawler cranes, and the demand for cooperative lifting using dual crawler cranes has grown. However, current operations remain largely manual, leading to safety risks and low efficiency. While extensive research has been conducted on the coordination of overhead cranes and other lifting systems, studies on cooperative control of crawler cranes are still in their early stages and have received insufficient attention. To address this issue, this paper establishes an accurate model of a dual crawler crane system using Lagrange equations. Motion constraints are incorporated to reduce the original fifth-order dynamics to a third-order system, simplifying control implementation. Given the strong coupling characteristics of the underactuated system, a PID-based coupled error compensation control method is proposed to regulate the load’s position and attitude precisely. By coordinating cable length adjustments, the proposed method ensures stable and accurate positioning while guaranteeing finite time convergence of cable length errors. Finally, a wind turbine installation scenario is simulated to validate the effectiveness of the proposed control approach.

This paper presents a multi-objective optimization framework to reduce energy costs while maintaining thermal comfort in a school classroom. The room is thermally controlled with an HVAC system. A first-order resistance-capacitance (RC) model is used to characterize the thermal behavior of the room. The multi-objective problem is solved using a scalarization method, which leads to a constrained quadratic programming (QP) problem. Simulations are carried out over a four-day period, considering dynamic electricity pricing and thermal inertia effects. The results demonstrate how increasing the thermal parameters of the room affects the thermal response and cost minimization. The study identifies optimal HVAC operation strategies that use the building’s thermal storage. The proposed framework can be used for real-time control and multi-agent coordination in large-scale building networks.

This paper deals with the problem of voltage and frequency restoration in droop-controlled inverter-based islanded microgrids (MGs). The problem is solved using a novel tracking consensus secondary control protocol based on the sliding-mode approach, which not only guarantees the exact finite-time restoration among voltages and frequencies of an inverter-based islanded microgrid, but also preserves the active power sharing among distributed generations (DGs). For each distributed generator with no direct access to reference values, a finite-time distributed estimator is locally designed and implemented in each DG unit to provide the reference value of frequency and voltage in a finite time. Lyapunov analysis is employed to verify the associated stability and fast convergence time of the proposed controller. Finally, simulation results are are presented and analyzed to confirm the effectiveness of the proposed approach.

Runtime frequency and voltage adaptation has become very attractive for current and next generation embedded multicore platforms because it allows handling the workload variabilities arising in complex and dynamic utilization scenarios. The main challenge of dynamic frequency adaptation is to adjust the processing speed of each element to match the quality-of-service requirements in the presence of workload variations. In this paper, we present a control theoretic approach to dynamic voltage/frequency scaling for data-flow models of computations mapped to multiprocessor systems-on-chip architectures. We discuss, in particular, nonlinear control approaches to deal with general streaming applications containing both pipeline and parallel stages. Theoretical analysis and experiments, carried out by means of a cycle-accurate energy-aware multiprocessor simulation platform, are provided. We have applied the proposed control approach to realistic streaming applications such as Data Encryption Standard and software-based FM radio.

In this work, we describe a control theoretic approach to dynamic voltage/frequency scaling (DVFS) in a pipelined MPSoC architecture with soft real-time constraints, aimed at minimizing energy consumption with throughput guarantees. Theoretical analysis and experiments carried out on a cycle-accurate, energy-aware, and multiprocessor simulation platform are provided. We give a dynamic model of the system behavior which allows to synthesize linear and nonlinear feedback control schemes for the run-time adjustment of the core frequencies. We study the characteristics of the proposed techniques in both transient and steady-state conditions. Finally, we compare the proposed feedback approaches and local DVFS policies from an energy consumption viewpoint.

In this work we take a control-theoretic approach to feedback-based dynamic voltage scaling (DVS) in multi processor system on chip (MPSoC) pipelined architectures. We present and discuss a novel feedback approach based on both linear and non-linear techniques aimed at controlling interprocessor queue occupancy. Theoretical analysis and experiments, carried out on a cycle-accurate multiprocessor simulation platform, show that feedback-based control reduces energy consumption with respect to standard local DVS policies and highlight that non-linear strategies allows a more flexible and robust implementation in presence of variable workload conditions