In practical applications, tower cranes often operate in outdoor environments and are subject to a variety of disturbances. Additionally, the control performance is significantly affected by uncertain dynamics, such as frictional forces and payload mass. To address these challenges, this paper presents a novel adaptive fuzzy control mechanism for nonlinear 5-degree of freedom (5-DOF) tower cranes with variable cable length. The proposed fuzzy adaptive mechanism leverages fuzzy logic to compensate for dynamic uncertainties and external disturbances thereby enhancing the overall robustness of the controlled system. Moreover, to ensure safe operation and avoid collisions, auxiliary constraint terms are introduced to restrict the actuated state variables within predefined bounds throughout the transportation process. In particular, an adaptive law is developed to accurately estimate the payload mass， which is critical for precise cable positioning. To the best of our knowledge, this work proposes a novel adaptive fuzzy closed-loop control framework for 5-DOF tower crane systems with varying cable lengths. The framework systematically integrates online payload mass estimation, state variable constraints, and fuzzy adaptive compensation to improve system stability and robustness. Based on the Lyapunov technique and Barbalat’s lemma, the system stability is theoretically analyzed and proven. Finally, the effectiveness of the proposed controller is validated by hardware experiments.

Tower cranes play an important role in construction, whose performance can determine the efficiency and safety of building construction. Payloads (end-effectors) must be transported along desired paths to ensure work efficiency and obstacle avoidance, rather than point to point. In this paper, an end-effector tracking controller is designed for 5-DOF underactuated tower cranes. Based on the proposed system transformation, tower cranes can be described as cascade systems including actuated and unactuated subsystems. A novel nonlinear coupling variable is defined between actuated (positions and speeds of jibs, trolleys and rope lengths) and unactuated (positions and speeds of payload swing angles) states according to position of end-effectors. The developed control strategy is constructed by block backstepping and the stability analysis is given based on approximate linearization to ensure its mathematical rigor. Finally, the developed end-effector tracking controller is applied to an actual laboratorial tower crane to demonstrate its superiority by comparing to the existing control strategies. The experimental results show that the tracking accuracy of payloads is 35.3% and the maximum swing angle of payloads is 62.65% of the existing controllers.

Flexible job shop scheduling problem (FJSP) has attracted research interests as it can significantly improve the energy, cost, and time efficiency of production. As one type of reinforcement learning, deep Q-network (DQN) has been applied to solve numerous realistic optimization problems. In this study, a DQN model is proposed to solve a multiobjective FJSP with crane transportation and setup times (FJSP-CS). Two objectives, i.e., makespan and total energy consumption, are optimized simultaneously based on weighting approach. To better reflect the problem realities, eight different crane transportation stages and three typical machine states including processing, setup, and standby are investigated. Considering the complexity of FJSP-CS, an identification rule is designed to organize the crane transportation in solution decoding. As for the DQN model, 12 state features and seven actions are designed to describe the features in the scheduling process. A novel structure is applied in the DQN topology, saving the calculation resources and improving the performance. In DQN training, double deep Q-network technique and soft target weight update strategy are used. In addition, three reported improvement strategies are adopted to enhance the solution qualities by adjusting scheduling assignments. Extensive computational tests and comparisons demonstrate the effectiveness and advantages of the proposed method in solving FJSP-CS, where the DQN can choose appropriate dispatching rules at various scheduling situations.

Busy-wait synchronization is often used for latency-critical applications to ensure low latency. Unfortunately, its performance bugs due to thread contention may lead to request failures or even system crashes. Localizing the performance bugs of busy-wait synchronization is not trivial because we have to pinpoint the exact moment of occurrence from a relatively long measurement period and simultaneously identify candidate busy-wait threads from numerous concurrent threads. Existing methods often rely on hotspot-driven analysis of lock-related functions, but they still need extensive manual work to localize busy-wait threads. This paper proposes timing call stack analysis (TCSA), an efficient approach to localizing busy-wait synchronization bugs. The key idea is to time-serialize the function call stacks of applications and identify consecutive identical call stacks to catch busy-wait threads. TCSA can handle any application regardless of its programming language and identify various busy-wait patterns, including spinlocks, chaining spinlocks, futexes, and safepoint checks within the Java Virtual Machine. Compared to the state-of-the-art, TCSA can effectively diminish the quantity of examined records (e.g., threads and functions) by 1 to 3 orders of magnitude. TCSA has been deployed to a large cloud service provider, demonstrating its effectiveness, efficiency, and practicality in four real latency-critical applications.

Hand gesture and movement analysis is a crucial learning task in Human-computer interaction (HCI) applications. Sensor-based HCI systems simultaneously capture the information with multiple locations to track the coordination of different regions of muscles. Based on the fact that there exists a temporal correlation between the regions, the connectivity analysis of sensor signals builds a network. The graph-based approach for analyzing the sensor network has provided novel insight into the learning in HCI, which has not been broadly investigated in hand gesture recognition tasks. This work proposes a topological representation learning scheme as a graph-based approach for sensor network analysis. Through investigation of the topological properties with persistent homology, the spatial-temporal characteristics are well described to build recognition models. Experiments on the NinaPro DB-2, DB-4, DB-5, and DB-7 datasets with sensor networks built with sEMG signal and IMU signal demonstrate exceptional performance of the proposed topological approach. The topological features are effective in graph representation learning with sensor networks used in hand gesture recognition. The proposed work provides a novel learning scheme in HCI systems and human-in-the-loop studies.

In this article, an adaptive nonlinear controller is developed for a class of stochastic systems, whose inputs are uncertainly nonlinear and virtual control gains (simplified as VCGs) include unknown and known items. A novel auxiliary function of boundedness and smoothness is constructed for handling the unknown items of VCGs. Aiming at the challenges of control laws without enough differentiability and uncertainties caused by deadzone and saturation of inputs, the novel control signals are proposed, which are tested on a nonsmooth system, SISO robot and MIMO quadrotor. The superiority and effectiveness of the designed control strategy are proved via the strict stability analysis and simulation comparisons.

In practical applications, particularly in flexible manufacturing systems, there is a high level of uncertainty. A type-2 fuzzy logic system (T2FS) has several parameters and an enhanced ability to handle high levels of uncertainty. This article proposes an improved artificial immune system (IAIS) algorithm to solve a special case of the flexible job shop scheduling problem (FJSP), where the processing time of each job is a nonsymmetric triangular interval T2FS (IT2FS) value. First, a novel affinity calculation method considering the IT2FS values is developed. Then, four problem-specific initialization heuristics are designed to enhance both quality and diversity. To enhance the exploitation abilities, six local search approaches are conducted for the routing and scheduling vectors, respectively. Next, a simulated annealing method is embedded to accept antibodies with low affinity, which can enhance the exploration abilities of the algorithm. Moreover, a novel population diversity heuristic is presented to eliminate antibodies with high crowding values. Five efficient algorithms are selected for a detailed comparison, and the simulation results demonstrate that the proposed IAIS algorithm is effective for IT2FS FJSPs.

The single-input-rule-modules (SIRMs) connected fuzzy inference method can efficiently solve the fuzzy rule explosion phenomenon, which usually occurs in the multivariable modeling and/or control applications. However, the performance of the SIRMs connected fuzzy inference system (SIRM-FIS) is limited due to its simple input-output mapping. In this paper, to further enhance the performance of SIRM-FIS, a functionally weighted SIRM-FIS (FWSIRM-FIS), which adopts multivariable functional weights to measure the important degrees of the SIRMs, is presented. Then, in order to show the fundamental differences of the SIRMs methods, properties of the traditional SIRM-FIS, the type-2 SIRM-FIS (T2SIRM-FIS), the functional SIRM-FIS (FSIRM-FIS), the SIRMs model with single-variable functional weights (SIRM-FW), and FWSIRM-FIS are explored. These properties demonstrate that the proposed FWSIRM-FIS has more general and complex input-output mapping than the existing SIRMs methods. Such properties theoretically guarantee that better performance can be achieved by FWSIRM-FIS. Furthermore, based on the least-squares method, a novel data-driven optimization method is presented for the parameter learning of FWSIRM-FIS. It can also be used to optimize the parameters of SIRM-FIS, T2SIRM-FIS, FSIRM-FIS, and SIRM-FW. Due to the properties of the least-squares method, the proposed parameter learning algorithm can overcome the drawbacks of the gradients-based parameter learning methods and obtain both smallest training errors and smallest parameters. Finally, to show the effectiveness and superiority of FWSIRM-FIS and the proposed optimization method, six examples and detailed comparisons are given. Simulation results show that FWSIRM-FIS can obtain better performance than the other SIRMs methods, and, compared with some well-known methods, FWSIRM-FIS can achieve similar or better performance but has much less parameters and faster training speed.

This paper presents a novel interval type-2 TSK nominal-fuzzy-model-based sliding mode controller (IT2-TSK-NFMSMC) for flexible air-breathing hypersonic vehicle (FAHV) in order to stress robustness of the control system in dealing with data-driven based fuzzy modelling deviations, system uncertainty and disturbances. We adopt backstepping structure decomposing FAHV model into 5 control subsystems and design controllers, respectively. More specifically, two subsystems are designed with integral sliding mode model controllers. Another three subsystems which directly coupling with flexible mode disturbances are designed with 1T2-TSK-NFMSMCs by the following steps: 1) interval type-2 TSK nominal-fuzzy-models (IT2-TSK-NFM) are generated automatically by using type-2 fuzzy self-organizing methods from experiment datasets; 2) nominal model sliding mode controllers are designed based on the IT2-TSK-NFM, respectively; 3) notch filters are adopted in order to decrease the disturbance effects from the flexible modes; 4) sliding mode compensation controllers are designed through Lyapunov synthesis in order to compensate differences between IT2-TSK-NFM and real models of the FAHV. Several scenarios are studied and the simulation results validate the robustness of the proposed controllers when there exist internal flexible vibration and external system disturbances.

This paper presents a novel approach to automatically creating an interval type-2 fuzzy neural network (IT2-FNN) from a type-1 fuzzy TSK system (T1-TSK). The IT2-FNN is constructed in such a way that it takes advantage of the well-behaving T1-TSK. Our approach makes designing the IT2-FNN more efficient and the resulting system is expected to perform better than the T1-TSK due to the footprint of uncertainty of the IT2 fuzzy sets, especially when the system is subject to heavy external or internal uncertainties. There are two automated procedures in the IT2-FNN formation: (1) antecedent structure construction, and (2) learning of the parameters in both the antecedent and consequent. The structure construction is based on antecedent structure of the T1-TSK and consists of three steps - IT2 fuzzy set creation, similarity categorization, and mergence. The IT2 fuzzy sets are directly initialized from the fuzzy sets of the T1-TSK. Then, the IT2 fuzzy sets are classified into different groups based on their similarities. Finally, the IT2 fuzzy sets in each group are merged to create a representative IT2 fuzzy set for each group. The parameter learning procedure uses a hybrid learning algorithm to attain the optimal values for all the parameters. The learning algorithm adopts a new adaptive steepest descent algorithm and a linear least-squares method to adjust the antecedent parameters and consequent parameters, respectively. One benchmark modelling problem is utilized to compare our approach with the T1-TSK systems in the literature under various scenarios. The comparison results show our IT2-FNN performs better than the T1-TSK systems, especially when there are strong uncertainties. In summary, the IT2-FNN can not only achieve better performance but its structure is simpler than that of the similar type-2 fuzzy neural networks in the literature.

In this paper an adaptive interval type-2 fuzzy sliding mode controller, which is applied to flexible air-breathing hypersonic vehicle (FAHV) longitudinal model, is designed based on interval type-2 fuzzy logic systems (IT2-FLS) and sliding mode control (SMC) theory. In order to get FAHV longitudinal model stably controlled, we decouple the model into velocity and altitude channels through output feedback linearization. Moreover, due to the severe uncertainties which mainly come from unpredictable varying aerodynamic interferences and mutual couplings in airframe flexible modes and those difficulties of computing nonlinear functions with high-order derivatives under practical conditions, we design a sliding mode controller to achieve system convergence and adopt IT2-FLS to estimate the nonlinear functions with bounded parameter uncertainties online for counteracting the tracking errors and suppressing flexible vibrations. The adaptive law of interval type-2 fuzzy sliding mode controller is derived through Lyapunov synthesis approach. Furthermore, we adopt tracking differentiator (TD) and nonlinear state observer (NSO) algorithms to generate the real-time derivatives and high-order approximate commands in velocity and altitude channels, respectively. Several comparisons have been done in this paper and the simulation results validate the robustness and effectiveness of the proposed controller.

In modeling, prediction and control applications, the single-input-rule-modules (SIRMs) connected fuzzy inference method can efficiently tackle the rule explosion problem that conventional fuzzy systems always face. In this paper, to improve the learning performance of the SIRMs method, a neural structure is presented. Then, based on the least square method, a novel parameter learning algorithm is proposed for the optimization of the SIRMs connected neural-fuzzy system. Further, the proposed neural-fuzzy system is applied to the cooling and heating loads prediction which is a popular multi-variable problem in the research domain of intelligent buildings. Simulation and comparison results are also given to demonstrate the effectiveness and superiority of the proposed method.

Computing with words (CWW) proposed by Zadeh is an useful paradigm to mimic the human decision-making ability in a wide variety of physical and mental tasks. To realize CWW, Mendel proposed a specific architecture called perceptual computer, in which interval type-2 (IT2) fuzzy sets (FSs) and perceptual reasoning (PR) method are adopted. The PR method has been proved to have good properties (e.g. it can output intuitive IT2 FSs) and has found several applications in decision making. In this study, we focus on simplifying this method by avoiding its a-cuts based inference process. We first present a novel property for the inference of the PR method. We observe from the property that, if the IT2 FSs in the consequents of the IF-THEN rules are trapezoidal and have consistent slopes, then the output IT2 FS will be strictly trapezoidal and can be determined easily. In this case, the computation of the PR method can be simplified. To achieve such simplification, the trapezoidal IT2 FSs without consistent slopes should be approximated by the slope-consistent trapezoidal IT2 FSs. This issue is also studied in this paper by solving the constrained linear-quadratic optimization problem. At last, examples are given. The simplified PR method will be useful when the CWW models are utilized in the modeling and/or control problems of complex systems or multivariable dynamic systems.

Qualitative knowledge is very useful for system modeling and control problems, especially when specific physical structure knowledge is unavailable and the number of training data points is small. This paper studies the incorporation of one common qualitative knowledge-monotonicity into interval type-2 (IT2) fuzzy logic systems (FLSs). Sufficient conditions on the antecedent and consequent parts of fuzzy rules are derived to guarantee the monotonicity between inputs and outputs. We take into account five type-reduction and defuzzification methods (the Karnik-Mendel method, the Du-Ying method, the Begian-Melek-Mendel method, the Wu-Tan method, and the Nie-Tan method). We show that IT2 FLSs are monotonic if the antecedent and consequents parts of their fuzzy rules are arranged according to the proposed monotonicity conditions. The derived monotonicity conditions are valid for the IT2 FLSs using any kind of IT2 fuzzy sets (FSs) (e.g., Trapezoidal IT2 FSs and Gaussian IT2 FSs) and stand for type-1 FLSs as well. Guidelines for applying the proposed conditions to modeling and control problems are also given. Our results will be useful in the design of monotonic IT2 FLSs for engineering applications when the monotonicity property is desired.

This paper tries to show some important proper ties of the single input rule modules (SIRMs) connected fuzzy inference systems (FIS), including both type-1 (Tl) and inter val type-2 (IT2) FISs. Three kinds of properties continuity, monotonicity and robustness are explored. First, conditions on the parameters are derived to ensure that the SIRMs connected FISs are continuous and monotonic. Then, a methodology for the robustness analysis of the SIRMs connected FISs are presented. At last, an example is given to show the correctness of the theorems on the continuity and monotonicity and to demonstrate the effectiveness of the proposed methodology for robustness analysis. These results can not only deepen our understanding of the SIRMs connected FISs, but also provide us guidelines for the design of the SIRMs connected FISs.

In this paper we propose an effective method to design a Single-Input Single-Output (SISO) Unnormalized Interval Type-2 Takagi-Sugeno-Kang (TSK) Fuzzy Logic System (UIT2FLS) for noisy regression problems based on multi-source knowledge which includes here the information from sample data and the prior knowledge of bounded range, symmetry and monotonicity. The sufficient conditions are given which ensure that the prior knowledge can be embedded into the UIT2FLS, and then the UIT2FLS is designed so that the target function can be approached as accurately as possible via constrained least squares algorithm. The performance of the UIT2FLS is verified through comparisons with unnormalized type-1 Fuzzy Logic Systems (FLSs) and normalized interval type-2 FLSs under three different noisy circumstances. Simulation results verify the correctness of the sufficient conditions, and demonstrate that the UIT2FLS has the best overall performance.

This paper tries to provide a stability analysis approach for the single input rule modules (SIRMs) based type-2 fuzzy logic control systems. First, in the neighbor of the equilibrium point, the closed-form input-output mappings of type-2 SIRMs (T2SIRMs) are explored, and the derivatives of T2SIRMs at the equilibrium point are computed. Then, how to compute the Jacobian matrix of the SIRMs based type-2 fuzzy logic control systems, which is a fundamental step for local stability analysis, is presented. At last, two examples on stabilization control of the TORA system and the inverted pendulum system are given. The results in both examples demonstrate that the stability analysis results agree completely with the control results.

This paper applies prior knowledge - monotonicity and convexity - to a Single-Input-Single-Output (SISO) un-normalized interval type-2 Takagi-Sugeno-Kang (TSK) Fuzzy Logic System (FLS). Sufficient conditions are provided to guarantee its monotonicity and convexity with respect to its input, respectively. The derived monotonic conditions focus on a zeroth-order TSK fuzzy model. Also, the corresponding proofs for the convex conditions of both the zeroth-order and first-order TSK fuzzy models are given, respectively. For the zeroth-order fuzzy systems, simulation examples demonstrate the validity of the theorems.

The translational oscillations with a rotational proof-mass actuator (TORA) is a well-known benchmark for examining the advantages and limitations of different nonlinear control design techniques. In this paper, a single-input-rule-modules (SIRMs) based type-2 fuzzy logic control scheme is proposed for this nonlinear multivariable system. And, genetic algorithms (GAs) are adopted to determine the parameters and to improve the performance of the SIRMs based type-2 fuzzy logic controller (SIRM-T2FLC). At last, simulations and comparisons are given to demonstrate the effectiveness, robustness and superiority of the proposed controller under three circumstances: normal case, the disturbance existing case, and the parameter varying case. From the design process and comparisons, it can be seen that: 1) this SIRMs based type-2 fuzzy control scheme can alleviate the difficulty to design conventional type-2 fuzzy logic controllers (T2FLCs) for this multivariable TORA system, 2) the SIRM-T2FLC is much easier to design and understand compared with conventional nonlinear control strategies for the TORA system, 3) better performance can be achieved.

The prior knowledge-monotonicity property-is helpful for system analysis, modeling and design, especially when no specific physical structure knowledge about systems is available. This paper presents how to use interval type-2 fuzzy logic systems (IT2FLSs) to incorporate the monotonicity property into system design. First, we present sufficient conditions on the parameters of IT2FLSs to ensure the monotonicity between the inputs and outputs of IT2FLSs. Then, we transform the design of monotonic IT2FLSs to the least squares problem with linear-inequality constraints. At last, simulations are given to show the usefulness of the monotonicity property and the advantages of monotonic IT2FLSs under noisy circumstances.

To solve the level-adjusting and force-tuning problems of high accurate and costly payloads when loading and unloading, a cable-driven auto-leveling parallel robot is developed. A hierarchical fuzzy controller, which has the ability to deal with the rule explosion problem, is proposed in this paper. After a brief introduction of the architecture of the closed-loop control system for the cable-driven auto-leveling parallel robot, the construction of the hierarchical fuzzy controller is set up, in which the force offsets of the four cables and the angle deviations of the two diagonal inclinations are chosen as input variables, and the output variables are the position changes of the four linear motion units. The hierarchical fuzzy controller contains two layers - the low level layer which generates two outputs for leveling adjustment and force tuning, and the high level layer which is used to coordinate the two outputs from the low level layer. Experimental results have demonstrated that the hierarchical fuzzy controller can achieve the control objectives with high regulation accuracy and short adjusting time, and can be easily applied to practical systems.