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This study aims to investigate the relationship between university and industry as two major infrastructures of national innovation system in all leading scientific and industrial settings.

Large complex organizations with high technology that follow non-linear dynamic rules need to define concepts and adopt new approaches to achieve organizational efficiency and effectiveness. Among various models, a dynamic model of innovation was developed based on a joint investment between industry and university. Hence, the concepts of systems thinking and system dynamics were used.

The results reveal three levels of industry and university communication from the lower levels to the higher levels.

The value of this paper lies in adding two axes of “type of relationship” and “form of relationship” to the axis of “strength of relationship”, and a static three-dimensional space as a spatial capacity of the relationship between the industry and the university has been organized. Further, this is the first study that investigates the dynamic relationship between industry and university based on the self-organization theory and system thinking.

Applying discrete linear optimal control to robot manipulators faces two challenging problems, namely nonlinearity and uncertainty. This paper aims to overcome nonlinearity and uncertainty to design the discrete optimal control for electrically driven robot manipulators.

Two novel discrete optimal control approaches are presented. In the first approach, a control-oriented model is applied for the discrete linear quadratic control while modeling error is estimated and compensated by a robust time-delay controller. Instead of the torque control strategy, the voltage control strategy is used for obtaining an optimal control that is free from the manipulator dynamics. In the second approach, a discrete optimal controller is designed by using a particle swarm optimization algorithm.

The first controller can overcome uncertainties, guarantee stability and provide a good tracking performance by using an online optimal algorithm whereas the second controller is an off-line optimal algorithm. The first control approach is verified by stability analysis. A comparison through simulations on a three-link electrically driven robot manipulator shows superiority of the first approach over the second approach. Another comparison shows that the first approach is superior to a bounded torque control approach in the presence of uncertainties.

The originality of this paper is to present two novel optimal control approaches for tracking control of electrically driven robot manipulators with considering the actuator dynamics. The novelty is that the proposed control approaches are free from the robot's model by using the voltage control strategy. The first approach is a novel discrete linear quadratic control design supported by a time-delay uncertainty compensator. The second approach is an off-line optimal design by using the particle swarm optimization.

The purpose of this paper is to design a discrete indirect adaptive fuzzy controller for a robotic manipulator. This paper addresses how to overcome the approximation error of the fuzzy system and uncertainties for asymptotic tracking control of robotic manipulators. The uncertainties include parametric uncertainty, un-modeled dynamics, discretization error and external disturbances.

The proposed controller is model-free and voltage-based in the form of discrete-time Mamdani fuzzy controller. The parameters of fuzzy controller are adaptively tuned for asymptotic tracking of a desired trajectory. A robust control term is used to compensate the approximation error of the fuzzy system. An adaptive mechanism is derived based on the stability analysis.

The proposed model-free discrete control is robust against all uncertainties associated with the robot manipulator and actuators. The approximation error of the fuzzy system is well compensated to achieve asymptotic tracking of the desired trajectories. Stability analysis and simulation results show its efficiency in the tracking control.

A novel discrete indirect adaptive fuzzy controller is designed for electrically driven robot manipulators using the voltage control strategy. The novelty of this paper is compensating the approximation error of the fuzzy system and discretizing error for asymptotic tracking of the desired trajectory.

Discrete control of robot manipulators with uncertain model is the purpose of this paper.

The proposed control design is model-free by employing an adaptive fuzzy estimator in the controller for the estimation of uncertainty as unknown function. An adaptive mechanism is proposed in order to overcome uncertainties. Parameters of the fuzzy estimator are adapted to minimize the estimation error using a gradient descent algorithm.

The proposed model-free discrete control is robust against all uncertainties associated with the model of robotic system including the robot manipulator and actuators, and external disturbances. Stability analysis verifies the proposed control approach. Simulation results show its efficiency in the tracking control.

A novel model-free discrete control approach for electrically driven robot manipulators is proposed. An adaptive fuzzy estimator is used in the controller to overcome uncertainties. The parameters of the estimator are regulated by a gradient descent algorithm. The most gradient descent algorithms have used a known cost function based on the tracking error for adaptation whereas the proposed gradient descent algorithm uses a cost function based on the uncertainty estimation error. Then, the uncertainty estimation error is calculated from the joint position error and its derivative using the closed-loop system.

The uncertainty and nonlinearity are the challenging problems for the control of a nonholonomic wheeled mobile robot. To overcome these problems, many valuable methods have been proposed by using two control loops namely the kinematic control and the torque control so far. In majority of the proposed approaches the dynamics of actuators is omitted for simplicity in the control design. This drawback degrades the control performance in high-velocity tracking control. On the other hand, to guarantee stability and overcome uncertainties, the control methods become computationally extensive and may be impractical due to using all states. The purpose of this paper is to design a simple controller with guaranteed stability for overcoming the nonlinearity, uncertainty and actuator dynamics.

The control design includes two control loops, the kinematic control loop and the novel dynamic control loop. The dynamic control loop uses the voltage control strategy instead of the torque control strategy. Feedbacks of the robot orientation, robot position, robot linear and angular velocity, and motor currents are given to the control system.

To improve the precision, the dynamics of motors are taken into account. The most important advantages of the proposed control law is that it is free from the robot dynamics, thereby the controller is simple, fast response and robust with ignorable tracking error. The control approach is verified by stability analysis. Simulation results show the effectiveness of the proposed control applied on an uncertain nonholonomic wheeled mobile robot driven by permanent magnet dc motors. A comparison with an adaptive sliding-mode dynamic control approach confirms the superiority of the proposed approach in terms of precision, simplicity of design and computations.

The originality of the paper is to present a new control design for an uncertain nonholonomic wheeled mobile robot by using voltage control strategy in replace of the torque control strategy. In addition, a novel state-space model of electrically driven nonholonomic wheeled mobile robot in the workspace is presented.

The innovation process demands an interaction between environment agents, knowledge generators and policies of incentive for innovation and not only development by companies. Universities have gradually become the core of the knowledge production system and, therefore, their role regarding innovation has become more important and diversified. This study is aimed at identifying the mechanisms of university–industry (U–I) collaboration, as well as the operationalization steps of the U–I collaboration process.

This study is aimed at identifying, based on a systematic literature review, the mechanisms of university–industry (U–I) collaboration, as well as the operationalization steps of the U–I collaboration process.

The analysis of the 72 selected articles enabled identifying 15 mechanisms of U–I collaboration, proposing a new classification for such mechanisms and developing a framework presenting the operationalization steps of the interaction process.

In this paper, the authors screened nearly 1,500 papers and analyzed in detail 86 papers addressing U–I collaboration, mechanisms of U–I collaboration and operationalization steps of the U–I collaboration process. This paper provides a new classification for such mechanisms and developing a framework presenting the operationalization steps of the interaction process. This research contributes to both theory and practice by highlighting managerial aspects and stimulating academic research on such timely topic.