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The precise control and dynamic analysis of the electrical Stewart platform have not been so well treated in the literature. This paper aims to design a novel model‐based controller to improve the tracing performance of the electrical Stewart platform. Moreover, the simulations under uncertain environments are used to verify the robustness of the controller.

In the electrical Stewart platform, there exist two special movements of the motor systems: motor systems' movement with the actuators and meanwhile the rotors and snails' rotation around their axis. The Kane equation is used to compute the driven torque of the movements of motor systems, actuators and movable platform. The improved dynamic models of the electrical Stewart platform which consider the motor systems and actuators' influences are used to design the novel controller. The PID controller and the simple model‐based controller are also developed to compare with the novel one. Moreover, the robustness of the controller is verified by the platform friction and the parameters uncertainty.

Simulation results show that the novel model‐based controller can gain a better tracing performance than the PID controller and even the simple model‐based controller. Under the environments of the platform with friction and 5% parameters variety, the tracing performance of the novel controller is also satisfactory, which verifies the robustness of the controller. Most importantly, the novel model‐based controller can be used in a higher precision control demand and a more complicated environment.

The main contribution of this paper is to derive a novel model‐based controller considering the motor systems' influence, which enhances the robustness of the controller. To the authors' best knowledge, such a framework for the improved model based controller has not been well treated in the past literature. The conventional PID controller and a simple model‐based controller are also built to verify the advantages of the improved model‐based controller.

Recently, deep reinforcement learning is developing rapidly and shows its power to solve difficult problems such as robotics and game of GO. Meanwhile, satellite attitude control systems are still using classical control technics such as proportional – integral – derivative and slide mode control as major solutions, facing problems with adaptability and automation.

In this paper, an approach based on deep reinforcement learning is proposed to increase adaptability and autonomy of satellite control system. It is a model-based algorithm which could find solutions with fewer episodes of learning than model-free algorithms.

Simulation experiment shows that when classical control crashed, this approach could find solution and reach the target with hundreds times of explorations and learning.

This approach is a non-gradient method using heuristic search to optimize policy to avoid local optima. Compared with classical control technics, this approach does not need prior knowledge of satellite or its orbit, has the ability to adapt different kinds of situations with data learning and has the ability to adapt different kinds of satellite and different tasks through transfer learning.

The purpose of this paper is to reduce the strain and vibration during robotic machining.

An intelligent approach based on particle swarm optimization (PSO) and adaptive iteration algorithms is proposed to optimize the PD control parameters in accordance with robotic machining state.

The proposed intelligent approach can significantly reduce robotic machining strain and vibration.

The relationship between robotic machining parameters is studied and the dynamics model of robotic machining is established. In view of the complexity of robotic machining process, the PSO and adaptive iteration algorithms are used to optimize the PD control parameters in accordance with robotic machining state. The PSO is used to optimize the PD control parameters during stable-machining state, and the adaptive iteration algorithm is used to optimize the PD control parameters during cut-into state.

The purpose of this paper is to propose a guaranteed cost control design procedure for model-based cyber–physical assembly (CPA) systems. To reflect the cyber–physical environment, the network-induced delays and disturbances are introduced in the mathematical model.

Based on the linear matrix inequality approach, the guaranteed cost controller is designed such that the guaranteed cost can be satisfied and the corresponding convex optimization algorithm is provided. Moreover, H-infinity theory is used to deal with the disturbance with the given H-infinity attenuation level.

By constructing appropriate Lyapunov–Krasovskii functionals, delay-dependent sufficient conditions are established in terms of linear matrix inequalities and the controller design procedure is given.

A simplified CPA model is given based on which the designed controller can allow us to control the closed-loop CPA systems with the guaranteed cost.

The purpose of this paper is analysis of the sensorless control system of induction machine with broken rotor for diagnostic purposes. Increasing popularity of sensorless controlled variable speed drives requires research in area of reliability, range of stable operation, fault symptoms and application of diagnosis methods.

T transformation used for conversion of instantaneous rotor currents electrical circuit representation to space vector components is investigated to apply with closed‐loop modeling algorithm. Evaluation of the algorithm is based on analysis of asymmetry influence to the orthogonal and zero components of space vector representation. Multiscalar model of the machine and selected structures of state observers are used for sensorless control system synthesis. Proposed method of frequency characteristics calculation is used for state observers analysis in open‐loop operation.

New algorithm of applying the T transformation allows for closed‐loop and sensorless control system simulation with asymmetric machine due to broken rotor. Compensating effect of the closed‐loop control system with speed measurements and diagnosis information in control system variables are identified. Proposed frequency analysis of state observers is presented and applied. Variables with amplified characteristic frequency components related to rotor asymmetry are compared for selected structures of state observers and with closed‐loop and open‐loop operation. Method of improving the sensorless system stability is proposed.

In closed‐loop and sensorless control system rotor fault can be diagnosed by using PI output controllers variables. Compensating effect of mechanical variables sets limitation to specified diagnosis methods. Rotor asymmetry affects sensorless control system stability depending on estimator structure.

This paper concentrates upon sensorless control system operation with machine asymmetry and indicates rotor fault symptoms.

The reliability of accounting internal control systems (AICS) is often viewed as a primary concern of auditors. Over the past three decades, several reliability models have been proposed for internal control. The main goal of these models is to provide an objective approach to evaluate the reliability of internal control systems. In addition, the models seek to assess the degree of audit reliance that can be placed on internal controls. This paper has a two‐fold objective: (1) to present an overview of the descriptive and prescriptive reliability models developed for the design and evaluation of internal control systems, and (2) to discuss the effects of various factors on the reliability assessment. Furthermore, two methods to estimate process reliabilities are presented and several numerical examples are provided to show the detailed calculations of the reliability and economic efficiency of accounting internal control systems.

Voltage source inverter-fed permanent magnet synchronous motors (VSI-PMSMs) are widely used in industrial actuation and mechatronic systems in water pumping stations, as well as in the traction of transportation systems (such as electric vehicles and electric trains or ships with electric propulsion). The dynamic model of VSI-PMSMs is multivariable and exhibits complicated nonlinear dynamics. The inverters’ currents, which are generated through a pulsewidth modulation process, are used to control the stator currents of the PMSM, which in turn control the rotational speed of this electric machine. So far, several nonlinear control schemes for VSI-PMSMs have been developed, having as primary objectives the precise tracking of setpoints by the system’s state variables and robustness to parametric changes or external perturbations. However, little has been done for the solution of the associated nonlinear optimal control problem. The purpose of this study/paper is to provide a novel nonlinear optimal control method for VSI-fed three-phase PMSMs.

The present article proposes a nonlinear optimal control approach for VSI-PMSMs. The nonlinear dynamic model of VSI-PMSMs undergoes approximate linearization around a temporary operating point, which is recomputed at each iteration of the control method. This temporary operating point is defined by the present value of the voltage source inverter-fed PMSM state vector and by the last sampled value of the motor’s control input vector. The linearization relies on Taylor series expansion and the calculation of the system’s Jacobian matrices. For the approximately linearized model of the voltage source inverter-fed PMSM, an H-infinity feedback controller is designed. For the computation of the controller’s feedback gains, an algebraic Riccati equation is iteratively solved at each time-step of the control method. The global asymptotic stability properties of the control method are proven through Lyapunov analysis. Finally, to implement state estimation-based control for this system, the H-infinity Kalman filter is proposed as a state observer. The proposed control method achieves fast and accurate tracking of the reference setpoints of the VSI-fed PMSM under moderate variations of the control inputs.

The proposed H-infinity controller provides the solution to the optimal control problem for the VSI-PMSM system under model uncertainty and external perturbations. Actually, this controller represents a min–max differential game taking place between the control inputs, which try to minimize a cost function that contains a quadratic term of the state vector’s tracking error, the model uncertainty, and exogenous disturbance terms, which try to maximize this cost function. To select the feedback gains of the stabilizing feedback controller, an algebraic Riccati equation is repetitively solved at each time-step of the control algorithm. To analyze the stability properties of the control scheme, the Lyapunov method is used. It is proven that the VSI-PMSM loop has the H-infinity tracking performance property, which signifies robustness against model uncertainty and disturbances. Moreover, under moderate conditions, the global asymptotic stability properties of this control scheme are proven. The proposed control method achieves fast tracking of reference setpoints by the VSI-PMSM state variables, while keeping also moderate the variations of the control inputs. The latter property indicates that energy consumption by the VSI-PMSM control loop can be minimized.

The proposed nonlinear optimal control method for the VSI-PMSM system exhibits several advantages: Comparing to global linearization-based control methods, such as Lie algebra-based control or differential flatness theory-based control, the nonlinear optimal control scheme avoids complicated state variable transformations (diffeomorphisms). Besides, its control inputs are applied directly to the initial nonlinear model of the VSI-PMSM system, and thus inverse transformations and the related singularity problems are also avoided. Compared with backstepping control, the nonlinear optimal control scheme does not require the state-space description of the controlled system to be found in the triangular (backstepping integral) form. Compared with sliding-mode control, there is no need to define in an often intuitive manner the sliding surfaces of the controlled system. Finally, compared with local model-based control, the article’s nonlinear optimal control method avoids linearization around multiple operating points and does not need the solution of multiple Riccati equations or LMIs. As a result of this, the nonlinear optimal control method requires less computational effort.

Voltage source inverter-fed permanent magnet synchronous motors (VSI-PMSMs) are widely used in industrial actuation and mechatronic systems in water pumping stations, as well as in the traction of transportation systems (such as electric vehicles and electric trains or ships with electric propulsion), The solution of the associated nonlinear control problem enables reliable and precise functioning of VSI-fd PMSMs. This in turn has a positive impact in all related industrial applications and in tasks of electric traction and propulsion where VSI-fed PMSMs are used. It is particularly important for electric transportation systems and for the wide use of electric vehicles as expected by green policies which aim at deploying electromotion and at achieving the Net Zero objective.

Unlike past approaches, in the new nonlinear optimal control method, linearization is performed around a temporary operating point, which is defined by the present value of the system’s state vector and by the last sampled value of the control input vector and not at points that belong to the desirable trajectory (setpoints). Besides, the Riccati equation, which is used for computing the feedback gains of the controller, is new, as is the global stability proof for this control method. Comparing with nonlinear model predictive control, which is a popular approach for treating the optimal control problem in industry, the new nonlinear optimal (H-infinity) control scheme is of proven global stability, and the convergence of its iterative search for the optimum does not depend on initial conditions and trials with multiple sets of controller parameters. It is also noteworthy that the nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state-dependent Riccati equations (SDRE). The SDRE approaches can be applied only to dynamical systems that can be transformed to the linear parameter varying form. Besides, the nonlinear optimal control method performs better than nonlinear optimal control schemes which use approximation of the solution of the Hamilton–Jacobi–Bellman equation by Galerkin series expansions.

This paper aims at presenting a cyclical evaluation model of information security (IS) maturity. The lack of a security evaluation method might expose organizations to several risky situations.

This model was developed through the definition of a set of steps to be followed to obtain periodical evaluation of maturity and continuous improvement of controls.

This model, based on controls present in ISO/IEC 27002, provides a means to measure the current situation of IS management through the use of a maturity model and provides a subsidy to take appropriate and feasible improvement actions, based on risks. A case study is performed, and the results indicate that the method is efficient for evaluating the current state of IS, to support IS management, risks identification and business and internal control processes.

It is possible that modifications to the process may be needed where there is less understanding of security requirements, such as in a less mature organization.

This paper presents a generic model applicable to all kinds of organizations. The main contribution of this paper is the use of a maturity scale allied to the cyclical process of evaluation, providing the generation of immediate indicators for the management of IS.

Admittance control is a typical complaint control methodology. Traditionally, admittance control systems are based on a dynamical relationship described by Voigt model. By contrast, after changing connection of spring and damper, Maxwell model produces different dynamics and has shown better impact absorption performance. This paper aims to design a novel compliant control method based on Maxwell model and implement it in a robot catching scenario.

To achieve this goal, this paper proposed a Maxwell model based admittance control scheme. Considering several motion stages involved in one catching attempt, the following approaches are adopted. First, Kalman filter is used to process the position data stream acquired from motion capture system and predict the subsequent object flying trajectory. Then, a linear segments with parabolic blends reaching motion is generated to achieve time-optimal movement under kinematic and joint inherent constraints. After robot reached the desired catching point, the proposed Maxwell model based admittance controller performs such as a cushion to moderate the impact between robot end-effector and flying object.

This paper has experimentally demonstrated the feasibility and effectiveness of the proposed method. Compared with typical Voigt model based compliant catching, less object bounding away from end-effector happens and the success rate of catching has been improved.

The authors proposed a novel Maxwell model based admittance control method and demonstrated its effectiveness in a robot catching scenario. The author’s approach may inspire other related researchers and has great potential of practical usage in a widespread of robot applications.