LMI based antiswing adaptive controller for uncertain overhead cranes

This paper proposes an adaptive anti-sway controller for uncertain overhead cranes. The state-space model of the 2D overhead crane with the system parameter uncertainties is shown firstly. Next, the adaptive controller which can adapt with the system uncertainties and input disturbances is established. The proposed controller has ability to move the trolley to the destination in short time and with small oscillation of the load despite the effect of the uncertainties and disturbances. Moreover, the controller has simple structure so it is easy to execute. Also, the stability of the closed-loop system is analytically proven. The proposed algorithm is verified by using Matlab/Simulink simulation tool. The simulation results show that the presented controller gives better performances (i.e., fast transient response, position tracking, and low swing angle) than the state feedback controller when there exist system parameter variations as well as input disturbances

Advanced Discrete-Time Control Methods for Industrial Applications

This thesis focuses on developing advanced control methods for two industrial systems in discrete-time aiming to enhance their performance in delivering the control objectives as well as considering the practical aspects. The first part addresses wind power dispatch into the electricity network using a battery energy storage system (BESS). To manage the amount of energy sold to the electricity market, a novel control scheme is developed based on discrete-time model predictive control (MPC) to ensure the optimal operation of the BESS in the presence of practical constraints. The control scheme follows a decision policy to sell more energy at peak demand times and store it at off-peaks in compliance with the Australian National Electricity Market rules. The performance of the control system is assessed under different scenarios using actual wind farm and electricity price data in simulation environment. The second part considers the control of overhead crane systems for automatic operation. To achieve high-speed load transportation with high-precision and minimum load swings, a new modeling approach is developed based on independent joint control strategy which considers actuators as the main plant. The nonlinearities of overhead crane dynamics are treated as disturbances acting on each actuator. The resulting model enables us to estimate the unknown parameters of the system including coulomb friction constants. A novel load swing control is also designed based on passivity-based control to suppress load swings. Two discrete-time controllers are then developed based on MPC and state feedback control to track reference trajectories along with a feedforward control to compensate for disturbances using computed torque control and a novel disturbance observer. The practical results on an experimental overhead crane setup demonstrate the high performance of the designed control systems.Comment: PhD Thesis, 230 page

Modeling and design of an observer-based robust controller for a low-cost inverted pendulum based on the H8 approach

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe inverted pendulum is a mechanical system with a simple configuration that carries a non-linear, unstable nature widely used in control theory as a benchmark for research. The current research presents the modeling of a low-cost commercially available inverted pendulum and the design of a robust state-feedback controller and a robust observer, following the H 8 principle and using a low-cost commercially available inverted pendulum system as a reference. Simulated results compare the performance of the designed controller and observer with a conventional LQR controller and observer.Peer ReviewedPostprint (author's final draft

Human adaptive mechatronics methods for mobile working machines

Despite the trend of increasing automation degree in control systems, human operators are still needed in applications such as aviation and surgery, or machines used in remote mining, forestry, construction, and agriculture, just to name a few. Although there are research results showing that the performance between the operators of working machines differ significantly, there are currently no means to improve the performance of the human-machine system automatically based on the skill and working differences of the operators. Traditionally the human-machine systems are designed so that the machine is "constant" for every operator. On the contrary, the Human Adaptive Mechatronics (HAM) approach focuses on individual design, taking into account the skill differences and preferences of the operators. This thesis proposes a new type of a HAM system for mobile working machines called Human Adaptive Mechatronics and Coaching (HAMC) system that is designed to account for the challenges regarding to the measurement capability and the work complexity in the real-life machines. Moreover, the related subproblems including intent recognition, skill evaluation, human operator modeling, intelligent coaching and skill adaptivity are described. The intent recognition is solved using a Hidden Markov model (HMM) based work cycle modeling method, which is a basis for the skill evaluation. The methods are implemented in three industrial applications. The human operator modeling problem is studied from the structural models' perspective. The structural models can be used to describe a continuum of human operator models with respect to the operating points of the controlled machine. Several extensions and new approaches which enable more efficient parameter estimation using the experimental data are described for the conventional Modified Optimal Control Model (MOCM) of human operator. The human operator modeling methods are implemented to model a human operator controlling a trolley crane simulator. Finally, the concept of human adaptive Human-Machine Interface (HMI) is described. The analytic and knowledge-based approaches for realizing the HMI adaptation are presented and implemented for trolley crane simulator control

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

Conventionally, researchers have favored the model-based control scheme for controlling gantry crane systems. However, this method necessitates a substantial investment of time and resources in order to develop an accurate mathematical model of the complex crane system. Recognizing this challenge, the current paper introduces a novel data-driven control scheme that relies exclusively on input and output data. Undertaking a couple of modifications to the conventional marine predators algorithm (MPA), random average marine predators algorithm (RAMPA) with tunable adaptive coefficient to control the step size ( CF) has been proposed in this paper as an enhanced alternative towards fine-tuning data-driven multiple-node hormone regulation neuroendocrine-PID (MnHR-NEPID) controller parameters for the multi-input–multi-output (MIMO) gantry crane system. First modification involved a random average location calculation within the algorithm’s updating mechanism to solve the local optima issue. The second modification then introduced tunable CF that enhanced search capacity by enabling users’ resilience towards attaining an offsetting level of exploration and exploitation phases. Effectiveness of the proposed method is evaluated based on the convergence curve and statistical analysis of the fitness function, the total norms of error and input, Wilcoxon’s rank test, time response analysis, and robustness analysis under the influence of external disturbance. Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods

Multi-objective Anti-swing Trajectory Planning of Double-pendulum Tower Crane Operations using Opposition-based Evolutionary Algorithm

Underactuated tower crane lifting requires time-energy optimal trajectories for the trolley/slew operations and reduction of the unactuated swings resulting from the trolley/jib motion. In scenarios involving non-negligible hook mass or long rig-cable, the hook-payload unit exhibits double-pendulum behaviour, making the problem highly challenging. This article introduces an offline multi-objective anti-swing trajectory planning module for a Computer-Aided Lift Planning (CALP) system of autonomous double-pendulum tower cranes, addressing all the transient state constraints. A set of auxiliary outputs are selected by methodically analyzing the payload swing dynamics and are used to prove the differential flatness property of the crane operations. The flat outputs are parameterized via suitable B\'{e}zier curves to formulate the multi-objective trajectory optimization problems in the flat output space. A novel multi-objective evolutionary algorithm called Collective Oppositional Generalized Differential Evolution 3 (CO-GDE3) is employed as the optimizer. To obtain faster convergence and better consistency in getting a wide range of good solutions, a new population initialization strategy is integrated into the conventional GDE3. The computationally efficient initialization method incorporates various concepts of computational opposition. Statistical comparisons based on trolley and slew operations verify the superiority of convergence and reliability of CO-GDE3 over the standard GDE3. Trolley and slew operations of a collision-free lifting path computed via the path planner of the CALP system are selected for a simulation study. The simulated trajectories demonstrate that the proposed planner can produce time-energy optimal solutions, keeping all the state variables within their respective limits and restricting the hook and payload swings.Comment: 14 pages, 14 figures, 6 table

A Robust Offline Precomputed Optimal Feedforward Control Action for the Real Time Feedback/Feedforward Control of Double Pendulum Gantry Cranes

none1openvalentina orsiniOrsini, Valentin

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

Conventionally, researchers have favored the model-based control scheme for controlling gantry crane systems. However, this method necessitates a substantial investment of time and resources in order to develop an accurate mathematical model of the complex crane system. Recognizing this challenge, the current paper introduces a novel data-driven control scheme that relies exclusively on input and output data. Undertaking a couple of modifications to the conventional marine predators algorithm (MPA), random average marine predators algorithm (RAMPA) with tunable adaptive coefficient to control the step size (CF) has been proposed in this paper as an enhanced alternative towards fine-tuning data-driven multiple-node hormone regulation neuroendocrine-PID (MnHR-NEPID) controller parameters for the multi-input–multi-output (MIMO) gantry crane system. First modification involved a random average location calculation within the algorithm’s updating mechanism to solve the local optima issue. The second modification then introduced tunable CF that enhanced search capacity by enabling users’ resilience towards attaining an offsetting level of exploration and exploitation phases. Effectiveness of the proposed method is evaluated based on the convergence curve and statistical analysis of the fitness function, the total norms of error and input, Wilcoxon’s rank test, time response analysis, and robustness analysis under the influence of external disturbance. Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods