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This paper aims to investigate an adaptive prescribed performance control problem for switched pure-feedback non-linear systems with input quantization.

By using the semi-bounded continuous condition of non-affine functions, the controllability of the system can be guaranteed. Then, a constraint variable method is introduced to ensure that the tracking error satisfies the prescribed performance requirements. Meanwhile, to avoid the design difficulties caused by the input quantization, a non-linear decomposition method is adopted. Finally, the feasibility of the proposed control scheme is verified by a numerical simulation example.

Based on neural networks and prescribed performance control method, an adaptive neural control strategy for switched pure-feedback non-linear systems is proposed.

The complex deduction and non-differentiable problems of traditional prescribed performance control methods can be solved by using the proposed error transformation approach. Besides, to obtain more general results, the restrictive differentiability assumption on non-affine functions is removed.

As more and more robots are used in industry, it is necessary for robots to interact with high dynamic environments. For this reason, the purpose of this research is to form an excellent force controller by considering the transient contact force response, overshoot and steady-state force-tracking accuracy.

Combining the active disturbance rejection control (ADRC) and the adaptive fuzzy PD controller, an enhanced admittance force-tracking controller framework and a well-designed control scheme are proposed. Tracking differentiator balances the contradiction between inertia and jump control signal of the control object. Kalman filter and extended state observer are introduced to obtain purer feedback force signal and uncertainty compensation. Adaptive fuzzy PD controller is introduced to account for transient and steady state performance of the system.

The proposed controller has achieved successful results through simulation and actual test of 6-axis robot with minimum error.

The controller is simple and practical in real industrial scenarios, where force control by robots is required.

In this research, a new practical force control algorithm is proposed to guarantee the performance of the force controller for robots interacting with high dynamic environments.

Argues that in increasingly turbulent and complex environments, the tools of “systems thinking” may provide a valuable resource in the drive to attain integration between strategic and operational objectives. Particular examples in the domain of production control and supply‐chain management are presented as illustrations of the dynamicist’s paradigm. Hence it is suggested that familiar concepts such as JIT/Kanban and supply chains are actually special cases of generic feedback control principles, while pure MRP is a classic example of feedforward. In practice, the complexities of real world operations require combinations of these two approaches and the paper therefore assesses the implications of these theoretical concepts, for the design of practical operations systems.

To provide an insight into the work of sound artists who use feedback to create music based on Heinz von Foerster's “order from noise” principle.

Selected examples of current and past working methods are given to demonstrate the research value of the artistic use of feedback music. It is attempted to find analogies between multi‐disciplinary uses of feedback by using the language of cybernetics to describe artistic practice. The text is augmented by graphic representations of audible feedback.

Audible feedback is suggested as a prime example to study the behaviour of circular systems. Feedback music is established as valid artistic practice. A set of questions is devised to push forward a pan‐disciplinary discussion about feedback mechanisms.

Very little research has been done on the subject, thereby the paper concentrates on subjective interpretations by the author.

Can only be considered a starting point for further research.

This paper is written for the curious reader who wants to discover the feedback strand of experimental music. It serves as another example for Heinz von Foersters boundary defying influence.

Reviews considerations of information theory and feedback control as applied to biological systems, and particularly their impact on the early development of cybernetics. Also reviews Lovelock’s Gaia hypothesis and its extension to account for the emergence of global homeostatis using the Daisyworld model. The latter provides a compelling view of the origin of biological regulation, but one that is difficult to reconcile with the neural and hormonal regulatory processes in modern plants and animals.

This paper aims to design an event-triggered adaptive prescribed performance controller for flexible manipulators, with the primary objectives of achieving output performance constraints and addressing communication resource limitations.

The authors propose a novel prescribed performance barrier Lyapunov function (PP-BLF) that considers both output and tracking performance constraints. The PP-BLF ensures that the system's output, transient behavior and steady-state performance, adhere to prescribed constraints. The boundary of the PP-BLF is established by an exponential function that decays over time. Notably, the PP-BLF can be applied seamlessly in unconstrained cases without necessitating controller redesign. Moreover, the controller design incorporates an event-triggered mechanism, effectively reducing the frequency of controller updates and optimizing the utilization of communication resources. Additionally, the authors employ adaptive techniques to estimate the system's unknown parameters and approximate unknown nonlinear functions using radial basis function neural networks (RBFNN). To address the challenge of “complexity explosion”, dynamic surface technology is employed.

Numerical simulations are conducted under five different cases to verify the effectiveness of the proposed controller. The results demonstrate that the controller successfully constrains the output tracking error within the prescribed performance boundary. Moreover, compared with the traditional time-triggered mechanism, the event-triggered mechanism significantly reduces the controller's update frequency, resolving the problem of limited communication resources.

The paper reduces the update frequency of control signals and improves resource utilization through an event-triggered mechanism in the form of relative thresholds. The authors recognize that the event-triggered mechanism may impact the output performance of the system. To address this challenge, the authors propose a prescribed performance Barrier Lyapunov Function (PP-BLF). The PP-BLF is designed to effectively constrain the output performance of the system, ensuring satisfactory control even when the control signal updates are reduced.

The purpose of this paper is to explore how business-to-business (B2B), intercultural, interpersonal salesperson–customer relationships develop using the lens of identity management theory (IMT; Imahori and Cupach, 2005).

The research uses qualitative semi-structured interviews on 18 targeted relationships with customers from another culture conducted with business-to-business salespeople.

The findings indicate that our respondents' relationships moved from trial toward enmeshment and on occasion toward the renegotiation phase, as described in IMT. In the case of low cultural diversity between salesperson and customer, the relationships reached the trial and enmeshment phase. In the case of high cultural diversity between salesperson and customer, the relationships on occasion evolved toward the renegotiation phase. Salespeople's cultural intelligence (CQ) facilitates the development of interpersonal, intercultural salesperson–customer relationships.

The authors transfer IMT from the personal relationship development arena to B2B intercultural, interpersonal relationships, address a gap in the literature in the understanding of salesperson–customer interpersonal relationships in different contexts and develop a theoretical model to understand intercultural, interpersonal salesperson–customer relationship development across different levels of cultural diversity.

Submitting negative feedback is a stressful experience which both superiors and subordinates try to avoid. Do principals adapt to the mum effect and hide their (negative) thoughts and feelings, or do they confirm Weening et al. hypothesis about the existence of facilitating conditions.

About 40 elementary school principals described in semi‐structured interviews cases of poor performing teachers whom they had to inform about their shortcomings.

Four consecutive stages, which escalated in directedness and criticism were found. Each stage depended on the outcomes of the previous one. Initially, half of the principals preferred first to ignore, but later 88 percent held a soft negative feedback and problem‐solving oriented discussions. When these discussions did not bring the expected results, about 80 percent criticized the worker orally. Only 30 percent criticized in writing. That process was accompanied with mixed feelings, mainly those of anger and compassion.

The results emphasize the need for individual guidance as how to overcome their hesitattions and give effective feedback to their poor‐performing teachers. As an exploraty study, its major weakness was the reliance on one side of the equation – the principals.

Most earlier studies on mum effect were conducted in experimental setting. This study provides a uniquely realistic evidence of the interpersonal processes within the workplace. The worker appraisal and the transmission of (negative) feedback are explored through emotional lens.

The purpose of this paper is to propose a more efficient strategy, which is easier to implement, i.e. the engineer can directly operate the target object without the robot to do a demonstration, and the manipulator is regulated to track the trajectory using vision feedback repetitively. Generally, the applications of industrial robotic manipulators are based on teaching playback strategy, i.e. the engineer should directly operate the manipulator to perform a demonstration and then the manipulator uses the recorded driving signals to perform repetitive tasks.

In the teaching process, the engineer grasps the object with a camera on it to do a demonstration, during which a series of images are recorded. The desired trajectory is defined by the homography between the images captured at current and final poses. Tracking error is directly defined by the homography matrix, without 3D reconstruction. Model-free feedback-assisted iterative learning control strategy is used for repetitive tracking, where feed-forward control signal is generated by iterative learning control strategy and feedback control signal is generated by direct feedback control.

The proposed framework is able to perform precise trajectory tracking by iterative learning, and is model-free so that the singularity problem is avoided which often occurs in conventional Jacobean-based visual servo systems. Besides, the framework is robust to image noise, which is shown in simulations and experiments.

The proposed framework is model-free, so that it is more flexible for industrial use and easier to implement. Satisfactory tracking performance can be achieved in the presence of image noise. System convergence is analyzed and experiments are provided for evaluation.

This paper aims to study the issues of adaptive fuzzy control for a category of switched under-actuated systems with input nonlinearities and external disturbances.

A control scheme based on sliding mode surface with a hierarchical structure is introduced to enhance the responsiveness and robustness of the studied systems. An equivalent control and switching control rules are co-designed in a hierarchical sliding mode control (HSMC) framework to ensure that the system state reaches a given sliding surface and remains sliding on the surface, finally stabilizing at the equilibrium point. Besides, the input nonlinearities consist of non-symmetric saturation and dead-zone, which are estimated by an unknown bounded function and a known affine function.

Based on fuzzy logic systems and the hierarchical sliding mode control method, an adaptive fuzzy control method for uncertain switched under-actuated systems is put forward.

The “cause and effect” problems often existing in conventional backstepping designs can be prevented. Furthermore, the presented adaptive laws can eliminate the influence of external disturbances and approximation errors. Besides, in contrast to arbitrary switching strategies, the authors consider a switching rule with average dwell time, which resolves control problems that cannot be resolved with arbitrary switching signals and reduces conservatism.