This paper considers aerial cargo transportation tasks in the presence of double-pendulum swing effects. Due to the higher degree of freedom and the more intricate dynamic coupling of the system, an effective nonlinear trajectory tracking control method is proposed to effectively track a given trajectory and simultaneously suppress the swing motion during tracking. The controller is designed to achieve improved transient tracking performance and generate amplitude-saturated inputs. Specifically, a tracking performance improvement term is elaborately designed through a dynamic generator, achieving more accurate trajectory tracking with bounded overshoots. Also, saturated functions are induced to provide bounded inputs, being able to deal with input saturation problems. Using the Lyapunov stability analysis and LaSalle’s invariance theorem, the stability of the system is rigorously proven. The author’s search of relevant literature shows that the proposed method is the first method of a nonlinear anti-swing trajectory tracking controller for double-pendulum aerial transportation systems in three-dimensional space. The tracking performance, swing suppression, and robustness against external disturbances are verified through comparative simulation.

Recently, the pneumatic artificial muscle (PAM) that can reproduce natural muscle functionalities has become one of the core actuator mechanisms of intelligent interactive soft robots. Unfortunately, some inherent defects (e.g., unignorable nonlinearities, hysteresis, low shrinkage frequencies, etc.) have limited the application progress of humanoid PAM arm robots. Additionally, the input constraints (e.g., saturations, dead zones, unidirectional inputs, etc.), unexpected external disturbances, unidentifiable system parameters, and inevitable unmodeled dynamics are usually complicated, which cannot be easily eliminated through existing adaptive control methods. This article proposes an adaptive fuzzy-sliding mode control method for humanoid PAM arm robotswithoutany information of precise model structures and system parameters, which can suppress the unexpected effects of complicated unknown functions and achieve high performance tracking control,simultaneously. To the best of our knowledge, the proposed controller is thefirstmethod for the humanoid PAM arm robots that considers the nonlinear input constraints including unidirectional conditions, saturations, and dead zones,simultaneously. Next, all the input constraints, system parameter uncertainties, unmodeled dynamics, and external disturbances can be estimated adaptively by utilizing the proposed fuzzy update law. Particularly, a sliding mode control law is designed to compensate possible fuzzy approximation errors, and rigorous Lyapunov-based stability analysis is provided to ensure that the state errors can converge to zero withinfinitetime. Hardware experiments are carried out later tovalidate the effectiveness and robustness of the proposed method.

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.

Pneumatic artificial muscle (PAM) systems are a kind of tube-like actuators, which can act roughly like human muscles by performing contractile or extensional motions actuated by pressurized air. At present, it is still an open and challenging issue to tackle positioning and tracking control problems of PAM systems, due to inherent characteristics, e.g., unidirectional inputs, high nonlinearities, hysteresis, time-varying characteristics, etc. In this paper, a new adaptive control method is proposed for PAM systems, which achieves satisfactory tracking performance. To this end, an update law is designed to estimate unknown system parameters online. Also, some control input transforming operations are applied to address unidirectional constraints (i.e., control inputs of PAM systems should always be positive). As far as we know, compared with most of the existing control methods, this paper gives the first continuous control solution for PAM systems that can simultaneously compensate parametric uncertainties, reject external disturbances, and meet unidirectional constraints. Without linearizing the nonlinear dynamics, the closed-loop system is theoretically proven to be asymptotically stable at the equilibrium point with the stability analysis. In addition, a series of hardware experiments are implemented on a self-built hardware platform, indicating that the proposed method achieves satisfactory tracking control and exhibits robustness against parametric uncertainties and disturbances.

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.

In real-world applications, industrial cranes commonly suffer from effects caused by the so-called double-pendulum phenomenon in many situations. However, at present, the double-pendulum phenomenon is usually directly roughly neglected when designing control methods. For double-pendulum cranes, most currently available approaches are open loop control; the existing feedback methods are mostly developed based on linearized dynamic models (around the equilibrium point) or designed without adding integral terms in the control laws, which may cause positioning errors in the presence of unmodeled dynamics. To address these problems, this paper proposes a new quasi-proportional integral derivative control method to effectively control underactuated double-pendulum crane systems. Then, we provide rigorous theoretical analysis for the equilibrium point of the closed-loop system based on the original nonlinear dynamic equations. To our knowledge, this paper gives the first plant-parameter-free controller that incorporates both integral action and actuating constraints without any linearizing operations during controller design or closed-loop analysis, which theoretically ensures that the controller can work well in the presence of unmodeled dynamics (e.g., insufficient friction compensation), actuating constraints, and large swing angles (i.e., not satisfying linearization conditions). Finally, hardware experimental results are provided to examine the effectiveness of the suggested control method.

In mechanical engineering, multi-translational oscillator with rotational actuator (multi-TORA) systems have been introduced to study the self-synchronized phenomenon as well as to investigate the vibration damping problem associated with many vibrational mechatronic systems, e.g., hand-held drills. Due to the lack of available actuators, multi-TORA systems are typically underactuated. Multi-TORA systems consist of a series of single TORA subsystems connected to and coupled with each other by elastic springs. For practical multi-TORA systems, the plant parameters are usually unknown or difficult to measure. Moreover, they exhibit strong nonlinear coupling behaviors. These factors bring much difficulty for both controller design and analysis. The control problem for underactuated multi-TORA systems with parametric uncertainties is challenging and still open. To address the above issues, this paper proposes a nonlinear increased motion control scheme for multi-TORA systems with parametric uncertainties. Specifically, a novel energy function is constructed and some extra coupling terms are introduced into the proposed controller for improving the transient performance. Then, based on Lyapunov techniques, we rigorously prove the asymptotic stability for the equilibrium point of the closed-loop system. As far as we know, this paper gives the first smooth control law to yield global asymptotic control results for underactuated multi-TORA systems suffering from unknown/uncertain plant parameters. To verify the effectiveness of the proposed controller, a series of hardware experiments are carried out on a self-built double-TORA hardware testbed, which indicate that the controller achieves effective control results in various conditions.

In practical applications, industrial cranes may exhibit double-pendulum swing effects, due to many factors, such as large payload scales and non-negligible hook masses. Currently, for double-pendulum cranes, most available methods are open-loop controllers designed based on linearized crane dynamics; even for existing closed-loop approaches, they are also mostly developed using linearized dynamics and require the exact knowledge of system parameters, which makes them sensitive to parametric uncertainties. To handle these issues, we present an adaptive antiswing control strategy for crane systems with double-pendulum swing effects and uncertain/unknown parameters, which can make the trolley accurately reach the target position with reduced overshoots and effectively eliminate the double-pendulum swing angles at the same time. A complete stability analysis, based upon the full nonlinear dynamics (i.e., without linearizing the dynamics), is included to support the theoretical derivations. We present hardware experimental results to demonstrate that the proposed controller achieves better performance than existing ones and exhibits good robustness.