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The purpose of this paper is to describe a generic method and tool for assessing the reliability and robustness of the product development process.

By extending the integrated definition for function modelling (IDEF0)‐based modelling approach, the paper demonstrates how to calculate the effectiveness of the process and the quality of the process output based on the quality of inputs, the controls and the tools used within the process. To illustrate and validate the proposed approach, it is applied to a case study of a product development process incorporating incomplete, fuzzy and uncertain inputs and resources.

Demonstrates the effectiveness of the tool in providing a quantified assessment of the process as well as its ability to identify those critical areas which will yield a significant improvement in the outcome of the product development process.

The technique is a valuable tool to assess the robustness and sensitivity of the process to changes in the quality of inputs, controls and tools, and can be integrated into businesses processes and management systems, and used as a tool to support continuous business and manufacturing decisions at any point of time.

A distinctive feature of tilt-rotor UAVs is that they can be fully actuated, whereas in fixed-angle rotor UAVs (e.g. common-type quadrotors, octorotors, etc.), the associated dynamic model is characterized by underactuation. Because of the existence of more control inputs, in tilt-rotor UAVs, there is more flexibility in the solution of the associated nonlinear control problem. On the other side, the dynamic model of the tilt-rotor UAVs remains nonlinear and multivariable and this imposes difficulty in the drone's controller design. This paper aims to achieve simultaneously precise tracking of trajectories and minimization of energy dissipation by the UAV's rotors. To this end elaborated control methods have to be developed.

A solution of the nonlinear control problem of tilt-rotor UAVs is attempted using a novel nonlinear optimal control method. This method is characterized by computational simplicity, clear implementation stages and proven global stability properties. At the first stage, approximate linearization is performed on the dynamic model of the tilt-rotor UAV with the use of first-order Taylor series expansion and through the computation of the system's Jacobian matrices. This linearization process is carried out at each sampling instance, around a temporary operating point which is defined by the present value of the tilt-rotor UAV's state vector and by the last sampled value of the control inputs vector. At the second stage, an H-infinity stabilizing controller is designed for the approximately linearized model of the tilt-rotor UAV. To find the feedback gains of the controller, an algebraic Riccati equation is repetitively solved, at each time-step of the control method. Lyapunov stability analysis is used to prove the global stability properties of the control scheme. Moreover, the H-infinity Kalman filter is used as a robust observer so as to enable state estimation-based control. The paper's nonlinear optimal control approach achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs. Finally, the nonlinear optimal control approach for UAVs with tilting rotors is compared against flatness-based control in successive loops, with the latter method to be also exhibiting satisfactory performance.

So far, nonlinear model predictive control (NMPC) methods have been of questionable performance in treating the nonlinear optimal control problem for tilt-rotor UAVs because NMPC's convergence to optimum depends often on the empirical selection of parameters while also lacking a global stability proof. In the present paper, a novel nonlinear optimal control method is proposed for solving the nonlinear optimal control problem of tilt rotor UAVs. Firstly, by following the assumption of small tilting angles, the state-space model of the UAV is formulated and conditions of differential flatness are given about it. Next, to implement the nonlinear optimal control method, the dynamic model of the tilt-rotor UAV undergoes approximate linearization at each sampling instance 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 inputs vector. The linearization process is based on first-order Taylor series expansion and on the computation of the associated Jacobian matrices. The modelling error, which is due to the truncation of higher-order terms from the Taylor series, is considered to be a perturbation that is asymptotically compensated by the robustness of the control scheme. For the linearized model of the UAV, an H-infinity stabilizing feedback controller is designed. To select the feedback gains of the H-infinity controller, an algebraic Riccati equation has to be repetitively solved at each time-step of the control method. The stability properties of the control scheme are analysed with the Lyapunov method.

There are no research limitations in the nonlinear optimal control method for tilt-rotor UAVs. The proposed nonlinear optimal control method achieves fast and accurate tracking of setpoints by all state variables of the tilt-rotor UAV under moderate variations of the control inputs. Compared to past approaches for treating the nonlinear optimal (H-infinity) control problem, the paper's approach is applicable also to dynamical systems which have a non-constant control inputs gain matrix. Furthermore, it uses a new Riccati equation to compute the controller's gains and follows a novel Lyapunov analysis to prove global stability for the control loop.

There are no practical implications in the application of the nonlinear optimal control method for tilt-rotor UAVs. On the contrary, 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 which can be transformed to the linear parameter varying (LPV) 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. The stability properties of the Galerkin series expansion-based optimal control approaches are still unproven.

The proposed nonlinear optimal control method is suitable for using in various types of robots, including robotic manipulators and autonomous vehicles. By treating nonlinear control problems for complicated robotic systems, the proposed nonlinear optimal control method can have a positive impact towards economic development. So far the method has been used successfully in (1) industrial robotics: robotic manipulators and networked robotic systems. One can note applications to fully actuated robotic manipulators, redundant manipulators, underactuated manipulators, cranes and load handling systems, time-delayed robotic systems, closed kinematic chain manipulators, flexible-link manipulators and micromanipulators and (2) transportation systems: autonomous vehicles and mobile robots. Besides, one can note applications to two-wheel and unicycle-type vehicles, four-wheel drive vehicles, four-wheel steering vehicles, articulated vehicles, truck and trailer systems, unmanned aerial vehicles, unmanned surface vessels, autonomous underwater vessels and underactuated vessels.

The proposed nonlinear optimal control method is a novel and genuine result and is used for the first time in the dynamic model of tilt-rotor UAVs. The nonlinear optimal control approach exhibits advantages against other control schemes one could have considered for the tilt-rotor UAV dynamics. For instance, (1) compared to the global linearization-based control schemes (such as Lie algebra-based control or flatness-based control), it does not require complicated changes of state variables (diffeomorphisms) and transformation of the system's state-space description. Consequently, it also avoids inverse transformations which may come against singularity problems, (2) compared to NMPC, the proposed nonlinear optimal control method is of proven global stability and the convergence of its iterative search for an optimum does not depend on initialization and controller's parametrization, (3) compared to sliding-mode control and backstepping control the application of the nonlinear optimal control method is not constrained into dynamical systems of a specific state-space form. It is known that unless the controlled system is found in the input–output linearized form, the definition of the associated sliding surfaces is an empirical procedure. Besides, unless the controlled system is found in the backstepping integral (triangular) form, the application of backstepping control is not possible, (4) compared to PID control, the nonlinear optimal control method is of proven global stability and its performance is not dependent on heuristics-based selection of parameters of the controller and (5) compared to multiple-model-based optimal control, the nonlinear optimal control method requires the computation of only one linearization point and the solution of only one Riccati equation.

In actual application of a DC-DC boost converter, the input voltage and resistive load may be changed frequently, and these variations deteriorate the conventional controller performance. The purpose of this paper is to present an observer-based control scheme for a DC-DC boost converter with an unknown resistive load and input voltage.

To estimate the unknown input voltage and resistive load, a nonlinear observer is designed by using the Lyapunov stability theorem. In addition, the closed-loop stability of the proposed control scheme for the DC-DC boost converter is proven. To convert the continuous control input to discrete mode, a sigma–delta modulator is used.

The proposed control scheme is validated in different situations. The adaptive structure of the proposed control scheme is tested by the input voltage, load and reference signal variation, and the simulation results confirm the capability of the proposed observer-based control strategy.

The contribution of this paper is twofold: according to nonlinear controller design, the feedforward term of the nonlinear controller is obtained via the observer, and unlike the proportional–integral controller, performance deterioration in the input voltage and load variations are unraveled. The effectiveness of this method is validated by experimental implementation in the presence of load and input voltage variations, and the experimental results confirm the efficacy of the proposed strategy.

The purpose of this paper is to focus on the accurate and steady control on trajectory tracking for wafer transfer robot, suppress the vibration and reduce the contour error.

The wafer transfer robot dynamic model is modeled. Through analyzing the characteristics of wafer transfer robot, cross‐coupled synchronized control is proposed based on the contour error model in task space to improve synchronization of the joints; the shaping for the joints by input shaper in task space is applied to suppress the vibration of the end effector during trajectory tracking. Then combining the cross‐coupled synchronized control with input shaping is proposed to improve accuracy and suppress the vibration.

The combination of cross‐coupled synchronized control and input shaping control method can improve the contour accuracy and reduce the vibration simultaneously during trajectory tracking. And the control method can be used to control the trajectory of wafer transfer robot.

The transfer station is in the center of the robot body. When the transfer station may deviate from the center of the robot body, the synchronizing performance of three axes on the same plane must be considered.

The proposed method can be used to solve the vibration and synchronizing performance problems on similar SCARA robots in semi‐conductor and liquid crystal display industry.

The proposed control method takes advantage of the cross‐coupled synchronized control and input shaping control method. This combination has improved contour accuracy and reduced vibration than applying other methods, and it has achieved better performance than using single one control method only.

This study examines the interplay of formal types of control (input, behavior and outcome) exercised on municipally owned corporations (MOCs). It further investigates whether particular informal contingencies (trust and interdependence) predict affiliation to the derived municipal control configurations.

The paper applies an exploratory cluster analysis based on survey data from 243 top-level managers of German MOCs. It then investigates the clustered municipal control configurations using binomial logistic regression.

The exploratory analysis reveals four municipal control configurations: (1) input-dominated control, (2) outcome-dominated control, (3) mixed input/outcome control and (4) “neglect of formal control”. As expected, both of the informal contingencies demonstrate strong predictive power. More precisely, trust increases the likelihood of belonging to the dominant outcome control cluster and interdependence increases the likelihood of belonging to the mixed input/outcome control cluster. Surprisingly, the neglect of formal control cluster is characterized by low trust and low interdependence.

The study sheds light on the widely assumed but understudied interplay of different formal controls in hybrid governance settings. Furthermore, the analysis stresses the importance of trust and interdependence when explaining hybrid control configurations.

The stability and input voltage saturation is a common problem associated with an active magnetic bearing (AMB) system. The purpose of this paper is to design a control scheme that stabilizes the single degree of freedom AMB system and also tackle the problem of input voltage saturation in the AMB system.

The proposed control technique is a combination of two separate control schemes. First, the Backstepping control scheme is designed to stabilize and control the AMB system and then Chebyshev neural network (CNN)-based compensator is designed to tackle the input voltage saturation when the system control action is saturated.

The mathematical and simulation results are presented to validate the effectiveness of proposed methodology for single-degree freedom AMB system.

This paper introduces a CNN-based compensator with Backstepping control strategy to stabilize and tackle the problem of input voltage saturation in the 1-DOF AMB systems.

The paper aims to present a new concept based on a multi‐agent approach in the area of nonlinear model predictive control (MPC) for fast systems.

A contribution to decentralized implementation of MPC is made. The control of the nonlinear system subject to constraints is achieved via a set of actions taken from different agents. The actions are based on an analytical solution and a neural network is used to monitor the closed system using a supervisory loop concept.

The high online computational need to solve an optimal control actions in nonlinear MPC, which results in a non‐convex optimization, is compared with the new proposed concept. Simulation results show that this approach has very remarkable performances in time computing and target arrival.

In practice, each MPC problem of the individual agent in multi‐agent MPC can run in parallel at the same time, instead of in serial, one agent after another. A parallel processor can be useful for real time implementation. However, it is estimated that how much time can be gained by performing the computations in parallel instead of in serial.

The proposed concept discussed in the paper has the potential to be applied to systems with rapid dynamics.

The multi‐agent MPC compares favorably with respect to a numerical optimization routine and also offers a solution for non‐convex optimization problems in single‐input single‐output 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 study aims to propose an offline exploratory method that consists of two stages: first, the authors focus on completing the kinematics model of the system by analyzing the Jacobians in the vicinity of the starting point and deducing a virtual input to effectively navigate the system along the non-holonomic constraint. Second, the authors explore the sensorimotor space in a predetermined pattern and obtain an approximate mapping from sensor space to chained form that facilitates controllability.

In this paper, the authors tackle the controller acquisition problem of unknown sensorimotor model in non-holonomic driftless systems. This feature is interesting to simplify and speed up the process of setting up industrial mobile robots with feedback controllers.

The authors validate the approach for the test case of the unicycle by controlling the system with time-state control policy. The authors present simulated and experimental results that show the effectiveness of the proposed method, and a comparison with the proximal policy optimization algorithm.

This research indicates clearly that feedback control of non-holonomic systems with uncertain kinematics and unknown sensor configuration is possible.

The purpose of this article is to evaluate performance of minimum energy controllers thoroughly on a tiltrotor aircraft.

Minimum energy controllers are designed for tiltrotor aircraft models for helicopter and airplane modes. Performance of minimum energy controllers is evaluated with respect to several criteria.

Minimum energy controllers can be used for tiltrotor aircraft flight control system design. These controllers show satisfactory performance when noise intensities and variance bounds vary.

Minimum energy controllers can be implemented for tiltrotor aircraft flight control system design.

In this paper, minimum energy controllers are applied for tiltrotor aircraft flight control system design and the performance of minimum energy controllers is evaluated deeply on a complex physical system (i.e. tiltrotor aircraft).