Tower Crane System Research Articles

Experimental Fuzzy Control for Tower Crane

Tower crane is a popular model in industry. It helps to move constructing material from lower place to higher place and vice versa. Due to its importance in real application, educational experimental models are necessary to be built in laboratory for researching and training students. In this paper, an experimental platform of tower crane system is presented for testing algorithms. For a real model with un-known system parameters and MIMO under-actuated structure, intelligent control is a suitable selection. Fuzzy control is a primary algorithm in training intelligent control for students. Thence, an 81-rule fuzzy controller is designed and used to control position and anti-sway for this model. This idea is inspired from fuzzy control for SIMO system, such as inverted pendulum. Our controller gives good results under kinds of external effects. The quality of controlling is proved to be better under fuzzy controller than under no-control. Therefore, survey in this paper can be used to train students in fuzzy control structure for MIMO under-actuated system.

Performance Improvement of Low-Cost Iterative Learning-Based Fuzzy Control Systems for Tower Crane Systems

This paper is dedicated to the memory of Prof. Ioan Dzitac, one of the fathers of this journal and its founding Editor-in-Chief till 2021. The paper addresses the performance improvement of three Single Input-Single Output (SISO) fuzzy control systems that control separately the positions of interest of tower crane systems, namely the cart position, the arm angular position and the payload position. Three separate low-cost SISO fuzzy controllers are employed in terms of first order discrete-time intelligent Proportional-Integral (PI) controllers with Takagi-Sugeno-Kang Proportional-Derivative (PD) fuzzy terms. Iterative Learning Control (ILC) system structures with PD learning functions are involved in the current iteration SISO ILC structures. Optimization problems are defined in order to tune the parameters of the learning functions. The objective functions are defined as the sums of squared control errors, and they are solved in the iteration domain using the recent metaheuristic Slime Mould Algorithm (SMA). The experimental results prove the performance improvement of the SISO control systems after ten iterations of SMA.

Iterative Feedback Tuning Algorithm for Tower Crane Systems

This paper proposes validates an Iterative Feedback Tuning (IFT) algorithm, which is a classical and also popular data-driven algorithm, on tower crane systems. The IFT algorithm is applied to improve the performances of three proportional-integral (PI) controllers by solving an optimization problem. The IFT algorithm is validated using experiments to control the cart position, the arm angular position and the payload position. The experiments are conducted on a tower crane system in authors’ laboratory, and the experimental setup is laboratory equipment that has strong nonlinearities. Ten iterations are performed to highlight the control system performance using IFT controllers.

Disturbance employment-based sliding mode control for 4-DOF tower crane systems

To improve the transient control performance and to guarantee the satisfactory robustness simultaneously, in this paper, a disturbance employment-based sliding mode control (DESMC) method is designed for 4-DOF tower crane systems. To this aim, a nonlinear disturbance observer is first designed, based on which, the pros and cons of disturbance effects on the tower crane system are defined by a well-designed disturbance effect indicator (DEI). Then, the DESMC method is systematically proposed and discussed, based on the estimated disturbance and the introduced DEI. The stability and state convergence analysis are implemented by Lyapunov techniques without any linearization operation. It is shown that, except bad effects, system disturbance has potential good effects on transient control performance improvement, and hence, it is essential to introduce these beneficial effects into controller design. As far as we know, the designed DESMC method yields the first observation-based control without any linearization operation to the original 4-DOF tower crane dynamic model and achieves the first control method making full use of beneficial disturbance effects on crane systems. Several simulation results are presented to validate the effectiveness and robustness of the designed DESMC method.

Piece‐wise ellipsoidal set‐based model predictive control of linear parameter varying systems with application to a tower crane

AbstractIn this paper, a model predictive control algorithm for polytopic linear parameter varying (LPV) systems is proposed, which is based on an offline calculated sequence of robust one‐step controllable sets. At each sampling instant, a finite‐time optimal control problem is solved subject to a stabilizing constraint on the first state to ensure stability and recursive feasibility guarantees independent of the length of the prediction horizon and the cost function. An algorithm is proposed to compute a sequence of piece‐wise ellipsoidal one‐step controllable sets. The systematic application of the developed algorithm is possible via tensor product model transformation to a class of nonlinear systems. The size and shape of each set depend on the vertices of the polytopic LPV system and influences the control performance. By using the tensor product model transformation, different vertex representations can be obtained automatically. Furthermore, a trade‐off between model complexity and accuracy can be chosen, and the computational effort of the set calculation can be influenced. The developed method is applied to a tower crane system. The tower crane is modeled in a cascaded form with three separate subsystems. The couplings between the subsystems are treated as varying parameters. Each subsystem of the crane is transformed into a polytopic LPV system using the tensor product model transformation. By discarding the nonzero singular values, the complexity of the polytopic LPV model is reduced in terms of the number of vertices.

Tensor product‐based model transformation approach to tower crane systems modeling

AbstractThis paper presents the application of the tensor product (TP)‐based model transformation approach to produce Tower CRrane (TCR) systems models. The modeling approach starts with a nonlinear model of TCR systems as representative multi‐input–multi‐output controlled processes. A linear parameter‐varying model is next derived, and the modeling steps specific to TP–based model transformation are proceeded to obtain the TP model. The TP model is tested on TCR laboratory equipment in two open‐loop scenarios considering chirp signals and pseudorandom binary step signals applied to the three model inputs (control inputs). The nonlinear and TP model outputs in the two scenarios are the payload position, the cart position, and the arm angular position. The nonlinear and TP model outputs are collected, measured, and compared. The simulation results prove that the derived TP model approximately mimics the behavior of the nonlinear model; both system responses and numerical approximation errors are illustrated.

Adaptive Neural Network Tracking Control for Double-Pendulum Tower Crane Systems With Nonideal Inputs

A novel adaptive neural network tracking control method is systematically investigated for a unique double-pendulum tower crane system model in this article. Several critical and practical application-oriented control issues, including robustness, tracking error limitation, double-pendulum effects, and input dead zone nonlinearity, are considered simultaneously, which have never been well addressed in the existing literature. Technically, neural networks are employed to approximate the functions with uncertain/unknown dynamics and nonideal inputs. Several barrier Lyapunov functions are proposed that can circumvent the violation of tracking error limitations in the proposed control method. Importantly, based on the designed adaptive neural network tracking control method, the jib and trolley can track their desired trajectories very fast, and the hook and payload sway can be completely eliminated. The Lyapunov stability theory and Babalat’s lemma are utilized to theoretically prove the convergence and stability of the proposed control system. Finally, well-designed simulation studies are carried out to verify the excellent performance and strong robustness of the control method. This article should be the first work considering a double-pendulum tower crane system with guaranteed convergence and performance without any linearization for the original nonlinear dynamic model.

Hybrid data-driven fuzzy active disturbance rejection control for tower crane systems

This paper proposes the Virtual Reference Feedback Tuning (VRFT) of a combination of two control algorithms, Active Disturbance Rejection Control (ADRC) as a representative data-driven (or model-free) control algorithm and fuzzy control, in order to exploit the advantages of data-driven control and fuzzy control. The combination of Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control (PDTSFC) tuned by Virtual Reference Feedback Tuning results in two novel data-driven algorithms referred to as hybrid data-driven fuzzy ADRC algorithms. The main benefit of this combination is the automatic optimal tuning in a model-free manner of the parameters of the combination of Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control called ADRC-PDTSFC. The second benefit is that the suggested combination is time saving in finding the optimal parameters of the controllers. However, since Virtual Reference Feedback Tuning generally works with linear controllers to solve a certain optimization problem and the fuzzy controllers are essentially nonlinear, this paper replaces the least-squares algorithm specific to Virtual Reference Feedback Tuning with a metaheuristic optimization algorithm, i.e. Grey Wolf Optimizer. The fuzzy control system stability is guaranteed by including a limit cycle-based stability analysis approach in Grey Wolf Optimizer algorithm to validate the next solution candidates. The hybrid data-driven fuzzy ADRC algorithms are validated as controllers in terms of real-time experiments conducted on three-degree-of-freedom tower crane system laboratory equipment. To determine the efficiency of the new hybrid data-driven fuzzy ADRC algorithms, their performance is compared experimentally with that of two control algorithms, namely Active Disturbance Rejection Control with Proportional-Derivative Takagi-Sugeno Fuzzy Control, whose parameters are optimally tuned by Grey Wolf Optimizer in a model-based manner using the nonlinear process model.

Adaptive Output Feedback Control for 5-DOF Varying-Cable-Length Tower Cranes With Cargo Mass Estimation

Tower crane systems exhibit high nonlinearity and underactuation, making the control issue challenging. Most reported methods for tower crane systems utilize linearized models, and accurate plant parameters (e.g., cargo mass, jib moment inertia) and full state feedback are usually required; moreover, most existing works only consider a part of the crane motions, and the control issue of five degree-of-freedom (5-DOF) tower cranes (i.e., 2-DOF cargo swing, slew, hoisting, and translation) is still open. However, tower cranes are practically influenced by uncertainties and disturbances, which may make linearized models ineffective; additionally, exact values of plant parameters may be difficult to obtain, and velocity signals are usually not directly measurable in practice. To address the aforementioned problems, this article proposes an adaptive output feedback control method for 5-DOF varying-cable-length tower cranes. As far as we know, this article provides the first adaptive output feedback controller, designed and analyzed without linearizing the dynamic equations, which can simultaneously achieve cargo hoisting/lowering, jib slew, trolley translation, and swing suppression, by avoiding using velocity-related feedback signals. Resorting to an elaborately constructed virtual spring-mass system, the control objectives are satisfactorily achieved even without involving any velocity signals with theoretical/experimental guarantee. Moreover, by elaborately designing a new adaptive law, the exact values of unknown cargo masses can be estimated through online identification. We provide rigorous stability/convergence analysis for the closed-loop system. Hardware experiment results are included for effectiveness/robustness verification.

Adaptive sway reduction for tower crane systems with varying cable lengths

For practical underactuated tower cranes, vertical payload motion is always involved to increase the working efficiency, making the control problem more complex. Most existing control methods are designed based on linearized crane dynamics and require exact plant parameters. To solve these problems, the dynamic model of tower cranes with varying cable lengths is established firstly, on whose basis, an adaptive sway reduction control method is proposed. To our knowledge, the designed control method yields the first closed-loop control solution, with guaranteed theoretical analysis, to successfully address the tower crane anti-swing and positioning problem in the presence of varying cable lengths. The model and control method are simulated by MATLAB/Simulink, and the simulation results show that the anti-swing performance is significantly enhanced by more than 63.22%, compared with PD method. Considering there are several control gains in the designed law, optimization of control gains based on optimization methods will be conducted.

Robust integral sliding mode control of tower cranes

In this study, integral sliding mode control is proposed for tower cranes to ensure precise tracking of the desired position while reducing the oscillations of the payload. The nonlinear robust controller is designed based on high fidelity nonlinear dynamical model, unlike the decoupled or linearized models used in the literature. The advantage of this approach is reducing the model uncertainties resulting in a lower control effort demand that would be required by the sliding mode controller. Moreover, the stability of the under-actuated tower crane system is analyzed using Lyapunov theory to guarantee the practical stability of error dynamics. Experimental results of the proposed control approach are compared with conventional sliding mode control to show its effectiveness and robustness against real system uncertainties.

Motion planning approach for payload swing reduction in tower cranes with double-pendulum effect

Tower crane system is usually considered as single-pendulum system to design controllers. However, in reality, tower crane often exhibits more complex double-pendulum characteristics, which makes it more difficult for positioning and swing angles elimination. To this end, a composite trajectory planning method is designed. The first part of the proposed trajectory guarantees the motion positioning of the jib and trolley, and the second part achieves the purpose of swing suppression by using a damping component. The proposed method can be implemented online that does not need to advance or offline planning. After a series of proof and simulations, it is shown that the proposed method can achieve the desired effect, and at the same time it is robust to uncertain system parameters and external disturbances.

Adaptive integral sliding mode control with payload sway reduction for 4-DOF tower crane systems

An adaptive integral sliding mode control (AISMC) method with payload sway reduction is presented for 4-DOF tower cranes in this paper. The designed controller consists of three parts: The integral sliding mode control is used to provide the robust behavior; the adaptive control is utilized to present the adaptive performance; the swing-damping term is added to suppress and eliminate the payload swing angles. Different from existing sliding mode control methods presenting with chattering phenomenon, the proposed AISMC method is essentially continuous and chattering free. Moreover, the accurate values of the system parameters including the payload mass, the trolley mass, the cable length, the moment of inertia of the jib, the friction-related coefficients are not required for the designed controller due to the adaptive control. Lyapunov-based analysis and LaSalle’s invariance principle are employed to support the theoretical derivations without linearizing the nonlinear dynamics. Experimental results are illustrated to show the superior control performance of the designed controller.

Modeling and energy-based sway reduction control for tower crane systems with double-pendulum and spherical-pendulum effects

As typical underactuated systems, tower crane systems present complicated nonlinear dynamics. For simplicity, the payload swing is traditionally modeled as a single-pendulum in existing works. Actually, when the hook mass is close to the payload mass, or the size of the payload is large, a tower crane may exhibit double-pendulum effects. In addition, existing control methods assume that the hook and the payload only swing in a plane. To tackle the aforementioned practical problems, we establish the dynamical model of the tower cranes with double-pendulum and spherical-pendulum effects. Then, on this basis, an energy-based controller is designed and analyzed using the established dynamic model. To further obtain rapid hook and payload swing suppression and elimination, the swing part is introduced to the energy-based controller. Lyapunov techniques and LaSalle’s invariance theorem are provided to demonstrate the asymptotic stability of the closed-loop system and the convergence of the system states. Simulation results are illustrated to verify the correctness and effectiveness of the designed controller.

Nonlinear Control of Underactuated Systems Subject to Both Actuated and Unactuated State Constraints With Experimental Verification

In practice, underactuated systems are widely used, and their control problems have attracted much attention in recent years. For safety concerns or transient performance requirements, some state constraints should be guaranteed both theoretically and practically, particularly those exerted on unactuated states. To the best of our knowledge, there are few closed-loop control methods that can treat unactuated state constraints with theoretical guarantees (i.e., that can theoretically guarantee unactuated state variables to be always within the preset ranges). For this purpose, in this article, we propose a new control strategy for a class of underactuated systems that can treat the various constraints including actuated and unactuated state constraints and the constraints on some specific composite variables. Specifically, we elaborately design some new auxiliary terms that are composed of constrained variable signals and actuated velocity signals. These terms can enhance the couplings between unactuated and actuated states that are further utilized to tackle the unactuated state and composite variable constraints. Then, the performance of the designed method is proven by rigorous analysis. Finally, the proposed method is applied to a double pendulum crane system and a tower crane system to verify its superior performance by experiments.

A rotary tower crane system: an appealing problem in nonholonomic mechanics

ABSTRACT Cranes system is widely used to transport heavy loads and hazardous materials in shipyards, factories, nuclear installations and high-building construction and plays an important role in the production process and serves to transfer loads from one place to another. Cranes are the best way of providing a heavy lifting facility covering virtually the whole area of the industry. The equations of motion of a rotary tower crane are highly non-linear, and rolling without slipping condition of the trolley on the jib plane can only be expressed by nonholonomic constraint equations. In this paper, a geometrical theory of general nonholonomic constrained systems on fibred manifolds and their jet prolongations, based on so-called Chetaev-type constraint forces, was applied to the above-mentioned mechanical problem. The dynamical model is preserved in simulations in its original non-linear form without any simplification. The results of numerical solutions of constrained equations of motion, derived within the theory, open the possibility of direct application of the theory to practical situations.

Switching Logic-Based Nonlinear Feedback Control of Offshore Ship-Mounted Tower Cranes: A Disturbance Observer-Based Approach

We propose, in this paper, an observer-based nonlinear feedback controller for four degrees of freedom (4-DOF) offshore ship-mounted tower cranes, which includes a robust-type term to address the observation uncertainty, and a switching logic tuning mechanism to update the involved unknown parameter. As a typical nonlinear underactuated mechatronic system, unlike the land-fixed tower cranes, an offshore ship-mounted tower crane also suffers from persistent disturbances caused by sea waves or currents, which leads to many difficulties and challenges in the controller design of such systems. Existing offshore crane control methods require either linearizations or approximations when performing analysis; moreover, most of them are only applicable for simplified 2-DOF crane models, and the ranges of system state errors cannot be guaranteed during the overall process. Motivated by these facts, to achieve simultaneous accurate jib/trolley positioning and fast payload swings suppression against complex unknown external disturbances, in particular, a disturbance observer is first designed in this paper, based on which a novel nonlinear switching logic-based control scheme for offshore cranes with jib rotation and horizontal transportation is proposed. As far as we know, the proposed method yields the first observer-based feedback closed-loop control without simplification operations to the original 4-DOF offshore tower crane dynamics and achieves the first asymptotic stability of the closed-loop system’s equilibrium point for offshore cranes. To support the theoretical derivation, the corresponding stability analysis of the closed-loop system’s equilibrium point is implemented by Lyapunov techniques. Numerous simulation and hardware experimental results are presented to demonstrate the superior performance of the proposed method. Note to Practitioners —This paper is motivated by the issue of controlling a four- degree-of-freedom (4-DOF) offshore ship-mounted tower crane system under the effects of unknown external disturbances. In practical applications, the persistent disturbances caused by sea waves or currents can always happen and make the offshore crane operation very challenging. Moreover, it brings more difficulties in the controller design for such a system due to its nonlinear underactuated property. Existing control approaches for offshore cranes are developed based upon linearized or oversimplified crane models or require exact model knowledge, and they may not work well in the presence of harsh sea conditions. Toward this end, this paper suggests a new control approach for 4-DOF offshore tower cranes suffering from sea wave effects to achieve a satisfactory performance. The asymptotic stability of the closed-loop system’s equilibrium point is derived, implying that the accurate jib/trolley positioning and fast payload swings suppression against complex unknown external disturbances are achieved simultaneously. Preliminary physical experiments implemented on a self-built offshore tower crane hardware test bed verify the effectiveness and the robustness of the proposed observer-based control method. In the future research, we will apply the suggested control approach to industrial offshore crane systems to improve their working efficiency.

Antiswing Cargo Transportation of Underactuated Tower Crane Systems by a Nonlinear Controller Embedded With an Integral Term

A tower crane is a nonlinear mechatronic system with complicated underactuated characteristics, which is widely used in modern construction sites. At present, most existing methods for tower cranes are proposed by linearizing the original nonlinear dynamics near equilibrium points, which are, thus, prone to suffering from unexpected steady errors due to such factors as unmodeled dynamics, imperfect friction compensation, etc., since they have not included integral terms in either controller design or stability analysis. Therefore, in this paper, an improved feedback controller with an elaborately constructed integral term is proposed for 3-D tower cranes without linearization, which can achieve both antiswing and positioning control while being able to effectively reduce steady errors in the presence of, e.g., inaccurate friction compensation. Furthermore, asymptotic stability results are proven through rigorous theoretical analysis. Owing to no linearization, the proposed controller is applicable when state variables (e.g., cargo swing angles) are not close enough to the equilibrium points, which makes it suitable for complicated working conditions. Hardware experimental results are included to verify the effectiveness of the proposed controller. Note to Practitioners —This paper is motivated by the requirement of effective control methods for tower cranes. Tower cranes are widely applied in modern construction sites to fulfill cargo transportation tasks. For such systems, the jib slew motion not only enlarges the workspace but also brings more difficulties to suppress unexpected cargo swing during the transportation process. Up until now, most existing methods use simplified system models or need exact model knowledge, which are difficult to reflect real dynamics in many practical situations. To handle these existing problems, in this paper, a novel feedback control approach embedded with an elaborately constructed integral term is presented without model linearization. By introducing the integral term, even when frictions are inaccurately compensated, steady errors can be reduced effectively and, hence, the positioning accuracy can be improved. Also, the proposed controller can handle parametric uncertainties. By applying the proposed controller, the closed-loop system achieves asymptotic results, which is rigorously proven theoretically. Finally, the effectiveness of the proposed controller is verified by implementing several groups of hardware experiments. In the future studies, we will apply the proposed method in practical applications.

Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems

In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption.

An adaptive tracking control method with swing suppression for 4-DOF tower crane systems

Abstract As an efficient transportation tool, a tower crane is widely used in construction sites. To improve the working efficiency, some automatic control methods have been proposed for tower crane systems, including some open loop methods, such as input shaping methods, trajectory planning methods, and so on. However, generally, tower cranes usually work in outdoor environment, which are sensitive to unavoidable external disturbances. Additionally, accurate system parameters are hard to obtain, which increases the control difficulty of the tower crane system. Considering these factors, in this paper, we propose an adaptive tracking control method, which achieves satisfactory tracking performance w.r.t. parameter uncertainties and external disturbances. Specifically, utilizing the passivity property, a shaped energy-like function is designed as a Lyapunov candidate, based on which, an adaptive tracking controller is proposed to deal with parameter uncertainties. Using Lyapunov stability analysis, together with LaSalle’s invariance principle, the closed-loop system is proven to be asymptotically stable. At last, a series of experimental tests are implemented to illustrate the satisfactory performance of the proposed method.