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Vehicle suspension systems are necessary for a stable and secure journey. To guarantee a smooth and comfortable ride for both passengers and cargo, active suspension systems are utilized. These systems operate by adjusting hydraulic or pneumatic actuators in response to road conditions, aiming to minimize vertical acceleration within the vehicle or cargo compartment. In the pursuit of enhancing comfort during transportation, this paper presents an H2 optimal control strategy for a full vehicle active suspension system. The proposed controller is engineered to mitigate road disturbances and to ensure a stable and comfortable ride for vehicle occupants. The proposed controller is designed to enhance driving performance, particularly in scenarios involving unknown road disturbances on the full vehicle system. Different road profiles are used to verify the proposed controller's efficacy and showcase its design's effectiveness. A Proportional-Integral-Derivative (PID) controller is also utilizedfor the sake of comparison with the proposed H2 optimal control where three error measures are used to quantify the performance for both controllers. According to the simulation results and performance measures, the H2 optimal control strategy outperformed the PID one. [ABSTRACT FROM AUTHOR]

In previous era, coal and oil like non‐renewable resources were mostly used for power generation, thus causing a severe environmental problem as well as high economic. An intelligent maximum power point tracking (MPPT) system was proposed to overwhelm the above‐mentioned impacts, which tracks the maximum generation of photovoltaic (PV) under various atmospheric conditions. Moreover, a modified DC‐DC converter was introduced to low inrush current and switching oscillation. The proposed model working process was split into three steps: first, design the normal PV with a grid‐integrated power system. Second, create a dataset with the aid of a designed PV model, and finally, design an intelligent MPPT controller based on that dataset. The difference among the MPPT and PV voltage was fed to an optimal controller that generates an appropriate pulse signal of the modified DC‐DC converter. The proposed intelligent tracking system was analyzed under various circumstances like static, dynamic and combined shading conditions. The proposed model offers 99.5% efficiency and very low harmonics values like 0.15% current total harmonic distortion (THD) and 0.46% voltage THD. The outcome verifies that the intelligent MPPT approach performs well than that of the currently used controllers in solar power generation and conditioning systems. [ABSTRACT FROM AUTHOR]

Objective. The problem of designing controllers that implement a given programmed movement of a controlled object and the problem of determining the movement of a dynamic system are two main problems in classical control theory. This article discusses the solution of direct and inverse optimal stabilization problems. The state vector is assumed to be completely available for measurement.Method. Based on the optimality ratio linking the weight coefficients of the quadratic quality functional and the optimal gain matrix, which closes the control object, it is proposed to use a numerical method for determining the functional matrices. Mathematical models of autonomous fully controlled objects were used for the study, the formation of which was carried out randomly, in particular, according to the normal distribution law.Result. The initial stage of the solution is associated with modal synthesis, the result of which is a proportional regulator that provides stabilization of the control object by the location of the poles of the synthesized system. The next step is to determine the weighting coefficients of the functional by numerically solving the optimality ratio. The final stage is the solution of the direct optimal stabilization problem, which is based on the Lagrange variational problem. As a result, the optimal regulator is calculated, which, when switched on in a closed system instead of a modal one, reduces the duration of the transient process.Conclusion. The proposed approach of the authors allows minimizing to a certain extent the transients of the adjusted control system.

In the wind energy system, an appropriate and effective voltage regulation of the converter is essential to guarantee the maximum power point operation. A technique based on direct pulse width modulation controller is presented in the suggested paper for the boost converter, whose working principle is based on single gain (k). Apart from developing the controller, the main contribution of this work is to tune the gain k on run time in accordance with the wind's velocity. For this purpose, initially, a thorough investigation has been executed with regard to k values and wind parameters, which will ensure the robust response of the controller. Thereafter, the direct values of duty cycle (D) of the converter are computed through the fundamental relation of the proposed controller. A comparative study for different wind conditions has been conducted to validate the performance of the proposed controller over previous controllers. The simulation results highlight a superior steady response, where the proposed controller shows a steady state error of 0.35% as compared to P&O and FOIC, where their errors are 3.5% and 1.5%, respectively. While the proposed controller's convergence response time is 110 ms, the response time of FOIC is 135 ms, and P&O is maximum while achieving its maximum power point. [ABSTRACT FROM AUTHOR]

A new multiobjective evolutionary optimisation algorithm (MOEA) to solve multimodal, multidimensional, nonconvex, nonlinear, dynamic multiobjective optimisation problems (MOPs) is the need of the hour. The quality of an MOEA lies in a good balance between the exploration and exploitation stages of the MOEA. Utopia constrained MOEA (U-MOEA) is proposed in this paper that improves the exploitation in the replacement step to achieve a perfect balance between exploration and exploitation. The proposed U-MOEA is tested on benchmark MOPs and a multivariable controller design problem. The performance of the proposed algorithm is also compared with other MOEAs such as NSGA-II and ICMDRA concerning hyper volume, nondomination count, combined Pareto set metric, and Cmetric. The performance metrics show better hyper volume and spread metric values for the proposed algorithm, indicating the ability in attaining trade-off region closeness along with diversified Pareto front for U-MOEA when compared to the other two algorithms. Results clearly show that the proposed U-MOEA produces good convergence, diversity characteristics with many numbers of trade-off solutions in a Pareto front. [ABSTRACT FROM AUTHOR]

The Unmanned Aerial Vehicle (UAV) attitude stabilization problem has been dealt with in many previous works through applying a vast range of philosophies of control strategies. In this paper, a discrete controller based on a Linear Quadratic Regulator (LQR) plus integral action is synthesized to stabilize the attitude and altitude of a quadrotor helicopter. This kind of control strategy allows us to reduce the energy consumption rate, and the desired UAV behavior is properly achieved. Experimental tests are conducted with external disturbances such as crosswinds deliberately added to affect the performance of the aerial vehicle. This provides experimental evidence that the integral part considered in the proposed control strategy contributes to improving the performance of the vehicle under external disturbances. In fact, a comparative analysis of potential and kinetic energy consumption is developed between the Optimal Integral Controller (OIC) and a Proportional Integral Derivative Controller (PID), allowing us to determine the level of improvement of the closed-loop system when the discrete Integral Optimal Controller is applied. [ABSTRACT FROM AUTHOR]

Consider one controller design problem for UAV flight control system, while guaranteeing the closed loop output track one expected output, this new paper derives this optimal controller through our own mathematical theories. After introducing the practical UAV flight control and formulating this practical problem into one closed loop control problem, the concept about tracking control with zero steady state is defined. Then from two aspects of tracking mission, two optimal controllers are yielded through solving two different optimization problems. Although their forms are different, we establish the equivalent properties between them together. Moreover, the detailed algorithm if designing the tracking controller within the case of zero steady state is shown. Finally, a platform about the practical UAV flight system is constructed to do some simulations so that our theoretical analysis is proven.

The problem of controlling the angle of attack of the aircraft is one of the difficult and complex problems due to the problems of nonlinear kinematics, variable parameters and uncertainty model. The design of the angle of attack control according to the robustness control algorithm often leads to a higher order robustness controller. Using a higher-order controller has many disadvantages, so it is necessary to have solutions to reduce the order of the controller. This paper presents the idea of designing a low-order controller for the aircraft's angle of attack control system using the order reduction algorithm. In order to meet the requirements of performance and stability when parameters change, the optimal controller of the aircraft's angle of attack is usually of high order. The paper has used order reduction algorithms to reduce the order of high-order angle of attack controller, the results show that: 4th-order controller or 1st-order controller can be used instead of high order controller. Using a low-order controller to control the aircraft's angle of attack shows that the quality of the control system is comparable to that of a high-order controller. [ABSTRACT FROM AUTHOR]

This paper describes the operation of a single Degree of Freedom (DOF) flexible robotic arm by the optimal controller. The derived model is based on Euler- Lagrange approach, while the first and second-order (super twisting) Sliding Mode Control (SMC) is proposed as a nonlinear control strategy. Exploration of a flexible robotic arm with using state-of-the-art controllers is essential for practical applications. This is even more refreshing when these arms have joints that work independently of each other to create a smooth connection between the joints, but it still ensures the natural properties like a real human arm. This system has many similarities with the flexible link system of the MIMO model in its operational state analysis. Control laws must be followed by logical rules in a coherent whole. The next step is to design a controller to fit the structure of the system. The author also compared the use of the above controller with a system without using any controller to determine the effectiveness of using the above controller.

The Unmanned Aerial Vehicle (UAV) attitude stabilization problem has been dealt with in many previous works through applying a vast range of philosophies of control strategies. In this paper, a discrete controller based on a Linear Quadratic Regulator (LQR) plus integral action is synthesized to stabilize the attitude and altitude of a quadrotor helicopter. This kind of control strategy allows us to reduce the energy consumption rate, and the desired UAV behavior is properly achieved. Experimental tests are conducted with external disturbances such as crosswinds deliberately added to affect the performance of the aerial vehicle. This provides experimental evidence that the integral part considered in the proposed control strategy contributes to improving the performance of the vehicle under external disturbances. In fact, a comparative analysis of potential and kinetic energy consumption is developed between the Optimal Integral Controller (OIC) and a Proportional Integral Derivative Controller (PID), allowing us to determine the level of improvement of the closed-loop system when the discrete Integral Optimal Controller is applied.

Rotary Unmanned Aerial Vehicles (RUAVs) suffer in average Northern Irish winters due to heavy wind preventing vital tasks from being performed in the economy, such as search and rescue or civil engineering observations. This work provides enhanced stability of RUAVs under wind disturbances by using metaheuristic algorithms to select optimal controller gains. Previous work demonstrated how Particle Swarm Optimization can be used to tune optimal controllers; this work uses a machine learning algorithm (Genetic Algorithm) to tune the controller. Simulations carried out on Full State Feedback, Full State Compensator and Linear Quadratic Gaussian controllers tuned by a variety of techniques revealed that the Genetic Algorithm outperformed conventional manual tuning by 20% and Particle Swarm Optimization by 17% in performance measured in settling time. The proposed method tunes the feedback gains and Kalman filter by Genetic Algorithm, which outperforms the manually tuned conventional schemes and "GA-Hybrid" approach. The conditions required to employ Reinforcement Learning as an alternative method for RUAV stabilization in future scope is also explored. [ABSTRACT FROM AUTHOR]

In this paper, based on a linearized mathematical model of the dynamics of a quadrotor, an optimal controller is constructed to control the spatial motion of a quadrotor when it is raising to a given altitude with simultaneous heading maneuver in a horizontal plane. Moreover, the maneuver trajectory must satisfy certain boundary conditions. The feature of this paper is the application of LQR synthesis to optimize the control of the translational motions of a quadrotor along all three axes of the inertial coordinate system, as well as to control its rotational motion relative to the vertical axis. The effectiveness of the developed control laws has been proven by modeling the above-described spatial motion of a quadrotor using the MATLAB package. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the operating conditions of traction drives of the electric locomotives with asynchronous traction motors. The process of change of the catenary system voltage was found to have a stochastic character. The method of current controller synthesis based on the Wiener–Hopf equation was proposed to enable efficient performance of the traction drive control system under the condition of the stochastic nature of the catenary system voltage and the presence of interferences, when measuring the stator current values of the tractor motor. Performance simulation of the proposed current controller and the current controller used in the existing vector control systems of the traction drives used in the electric locomotives was implemented. The results of the performance simulation of the proposed current controller were compared with the performance of the current controller in existing vector control systems of the traction drives. The results are applicable to the design of vector control systems of traction drives in electric locomotives and to the study of the influence of performance of electric traction drives in electric locomotives on the quality indicators of the power supplied by the traction power supply system under the actual operating conditions of the locomotive. [ABSTRACT FROM AUTHOR]

This paper investigates the attitude and orbit control for the combined spacecraft formed after a target spacecraft without the autonomous control ability is captured by a service spacecraft. The optimal controller of fully-actuated system is proposed to realize the attitude and orbit stabilization control of combined spacecraft. The stability of the system is proved by introducing Lyapunov function. Numerical simulation of the combined spacecraft and physical experiment based on the combined spacecraft simulator (CSS) are completed. Both simulation and experiment results demonstrate the effectiveness and practicability of the optimal controller of fully-actuated system. [ABSTRACT FROM AUTHOR]

The article concerns the problem of structure-and-parametric optimization of a cascade automatic control system (CACS) by an example of a boiler power controller and a fuel controller. This CACS, which is a part of automatic control systems for power units, consists of two loops, viz. of an inner loop (which purpose is stabilization of the system) and an outer loop (designed for the adjustment) and, also, of two controller, viz. an outer controller (which is a basic one) provided for stabilization of the output value of the object (in our case, of the actual power unit capacity) and of an inner controller (which is an auxiliary one) provided to regulate fuel consumption. The internal controller builds up the control action with the aid of the boiler load controller of the power unit. As compared to single-loop automatic control systems, the cascade system provides better quality of transient control due to the higher performance of the internal loop of the system. This advantage is especially noticeable when compensating for disturbances that come through the channel of regulating impact. The article presents two methods of setting, viz. the fuel controller and the boiler power controller. The application of these methods can improve the quality of power control and reduce fuel consumption in transient modes in comparison with the setting of these controllers of a typical power unit automatic power control system. The results of computer simulation of transient processes in CACS for input step surge and internal perturbation confirm the advantages of the methods are presented in this article.

We consider the optimal control problem for solutions of the parabolic equation with the right-hand side of the form ε F (y), where ε > 0 is a small parameter, with the coercive objective functional and bounded control. We use the formula of the optimal controller of the unperturbed problem and substantiate the form of the approximate controller with switching for the original problem. [ABSTRACT FROM AUTHOR]

In this study, conical shell vibration with distributed piezoelectric layers on the shell surface is controlled by a distributed optimal controller. Two piezoelectric layers are distributed on the conical shell surface with the same geometry and they are segmented into the same numbers of patches. One piezoelectric layer is considered to be a sensor layer and the other one is considered to be an actuator layer. An optimal controller with various constants for each piezoelectric patch is determined with an optimal input voltage. The conical shell electromechanical equations of motions with piezoelectric layers are extracted. The Galerkin method is used for obtaining the time domain equations and after that optimal constants of the controller are determined. Various kinds of distribution for the piezoelectric layer are considered and their effects on the conical shell vibration control are evaluated. For a better assessment, free vibration response, forced vibration response with concentrated and distributed force, and the frequency response of the considered system are computed and compared with the uncontrolled response. The results show the high impact of the optimal controller on the vibration mitigation of the conical shell and also the actuator applied voltage amplitude is considerably low. The applicability of the piezoelectric layer in the conical shell vibration mitigation is vividly determined by using an optimal controller which decreases the actuator applied voltage amplitude dramatically. • Optimal control of conical shell with piezoelectric layers. • Evaluation of external point force and distributed force on the conical shell vibration control. • Free and Forced vibration response, frequency response of conical shell with optimal control. • Lower levels of applied voltage on the actuator with higher levels of vibration mitigation. [ABSTRACT FROM AUTHOR]

One of the major challenges in the real-time control of structures is the uncertainty in identifying the mechanical properties of the system, including mass, stiffness, and damping. However, the presence of uncertainty in time-varying systems (under severe environmental factors, the dynamic characteristics of the system change due to damage) is more noticeable. In this article, to deal with the problem of parametric uncertainties in seismic motion control of structures, an adaptive optimal controller is applied. The design procedure of the controller is straightforward, and there is no need to have the values of the structural parameters. In the proposed scheme, only the states of the system are measured, and it is not necessary to use the ground acceleration data which are difficult to be measured in real time. It is shown that the performance of the designed controller converges to the well-known linear quadratic regulator. Simulation results reveal that considerable reductions of the dynamic responses during the earthquakes are achieved using the utilized adaptive optimal controller, and also it is robust against any variation in the structural parameters due to the damages. [ABSTRACT FROM AUTHOR]

Summary: In a complex power system, the power quality in terms of reliability and stability is highly influenced by generation regulation. The frequency or power deviation caused by the imbalance between generation and load can be minimized by automatic generation control (AGC). For this purpose, a new hybrid PIλD‐Fuzzy PIλD (hPIλD‐FPIλD) controller is introduced by concocting the property of both PIλD and Fuzzy PIλD controllers. A fuzzy based Symbiotic Organism Search (FSOS) is proposed by using fuzzy inference system to decide some parameters of the SOS algorithm. The renewable energy source such as small hydro plant (SHP) integrated with power system (Two area thermal‐hydro‐nuclear and three area thermal system) are considered. The proposed approaches (hPIλD‐FPIλD and FSOS) are substantiated by contributing improved performance of SHP and power systems integrated with SHP. The impact of hPIλD‐FPIλD, FSOS, and SHP in the power system is demonstrated in three test systems. The efficacy of the proposed hPIλD‐FPIλD controller is demonstrated with improves responses of second, third, and fourth order systems. The proposed algorithm (FSOS) is demonstrated by solving nine benchmark equations. Further, sensitivity and robustness analysis of the system with and without SHPs are observed by varying some parameters of the system. The evidence to validate the superiority of proposed approaches is achieved from the simulation analysis. [ABSTRACT FROM AUTHOR]

Rotary Unmanned Aerial Vehicles (RUAVs) suffer in average Northern Irish winters due to heavy wind preventing vital tasks from being performed in the economy, such as search and rescue or civil engineering observations. This work provides enhanced stability of RUAVs under wind disturbances by using metaheuristic algorithms to select optimal controller gains. Previous work demonstrated how Particle Swarm Optimization can be used to tune optimal controllers; this work uses a machine learning algorithm (Genetic Algorithm) to tune the controller. Simulations carried out on Full State Feedback, Full State Compensator and Linear Quadratic Gaussian controllers tuned by a variety of techniques revealed that the Genetic Algorithm outperformed conventional manual tuning by 20% and Particle Swarm Optimization by 17% in performance measured in settling time. The proposed method tunes the feedback gains and Kalman filter by Genetic Algorithm, which outperforms the manually tuned conventional schemes and “GA-Hybrid” approach. The conditions required to employ Reinforcement Learning as an alternative method for RUAV stabilization in future scope is also explored.

This paper deals with the analysis of the operating conditions of traction drives of the electric locomotives with asynchronous traction motors. The process of change of the catenary system voltage was found to have a stochastic character. The method of current controller synthesis based on the Wiener–Hopf equation was proposed to enable efficient performance of the traction drive control system under the condition of the stochastic nature of the catenary system voltage and the presence of interferences, when measuring the stator current values of the tractor motor. Performance simulation of the proposed current controller and the current controller used in the existing vector control systems of the traction drives used in the electric locomotives was implemented. The results of the performance simulation of the proposed current controller were compared with the performance of the current controller in existing vector control systems of the traction drives. The results are applicable to the design of vector control systems of traction drives in electric locomotives and to the study of the influence of performance of electric traction drives in electric locomotives on the quality indicators of the power supplied by the traction power supply system under the actual operating conditions of the locomotive.

This paper presents an optimal control algorithm for connected cruise control (CCC) system in the presence of the stochastic communication delays. In the CCC system, wireless vehicle-to-vehicle (V2V) communication is the key factor to achieve comprehensive environment perception while it gives rise to delays in the meantime. In order to compensate for the influence of the stochastic delays and develop an optimal controller for the CCC system, we first analyze the vehicle dynamics with the car-following model. Then, the mathematical platoon model is formulated with heterogenous platoon structure. With the objective of minimizing the deviations of vehicle's headway and velocity, a linear quadratic optimization problem is formulated. The optimal control strategy is solved in an iterative manner by using a backward recursion which can be divided into two steps. In particular, the optimal control gain is iteratively derived off-line and the control strategy is calculated on-line. In addition, the control gain convergence, the stability analysis, and the effect of the packet losses and state noises are analyzed. Finally, the stability and performance of the proposed control algorithm are verified by numerical simulations to highlight its superiority compared with the existing algorithms. [ABSTRACT FROM AUTHOR]

In this study, near optimal tracking of a class of non‐linear systems is addressed. Adaptive (approximate) dynamic programming (ADP) approach is used to calculate the optimal control in closed form. ADP has been widely used to resolve optimal regulation and tracking problems of non‐linear control systems. Despite advances in the so called supervised and unsupervised ADP techniques for optimal tracking, they have a main draw back. That is, the optimal controller needs to be recalculated for every particular reference trajectory. The main goal of this work is to address this issue for a class of non‐linear systems. Finally, this approach is applied on a Delta robot and the performance of the method is analysed experimentally. [ABSTRACT FROM AUTHOR]

This study presents an extremum seeking‐proportional–integral and derivative (ES‐PID) controller design for brushless direct current motors and its implementation in electric vehicles. The ES‐PID controller aims to simultaneously maintain a speed set‐point and reduce torque ripples in the presence of load‐torque disturbances. The proposed ES‐PID combines the simplicity of a PID controller with an extremum seeking approach, a model‐free optimisation approach, thereby resulting in an optimal controller that can be realised on simple hardware (with limited computing power and memory). In addition, a theoretical analysis of the proposed ES‐PID control scheme in terms of stability and convergence is also presented. Performance evaluation of the proposed ES‐PID controller using both simulations and hardware experiments are presented. The results clearly illustrate the ability of the controller to track speed set‐point and reduce torque ripples in the presence of load‐torque variations. [ABSTRACT FROM AUTHOR]

Due to the vital role of financial systems in today’s sophisticated world, applying intelligent controllers through management strategies is of crucial importance. We propose to formulate the control problem of the macroeconomic system as an optimization problem and find optimal actions using a reinforcement learning algorithm. Using the Q-learning algorithm, the best optimal action for the system is obtained, and the behavior of the system is controlled. We illustrate that it is possible to control the nonlinear dynamics of the macroeconomic systems using restricted actuation. The highly effective performance of the proposed controller for uncertain systems is demonstrated. The simulation results evidently confirm that the proposed controller satisfies the expected performance. In addition, the numerical simulations clearly confirm that even when we confined the control actions, the proposed controller effectively finds optimal actions for the nonlinear macroeconomic system.

This article investigates an adaptive reinforcement learning (RL) optimal control design problem for a class of nonstrict-feedback discrete-time systems. Based on the neural network (NN) approximating ability and RL control design technique, an adaptive backstepping RL optimal controller and a minimal learning parameter (MLP) adaptive RL optimal controller are developed by establishing a novel strategic utility function and introducing external function terms. It is proved that the proposed adaptive RL optimal controllers can guarantee that all signals in the closed-loop systems are semiglobal uniformly ultimately bounded (SGUUB). The main feature is that the proposed schemes can solve the optimal control problem that the previous literature cannot deal with. Furthermore, the proposed MPL adaptive optimal control scheme can reduce the number of adaptive laws, and thus the computational complexity is decreased. Finally, the simulation results illustrate the validity of the proposed optimal control schemes. [ABSTRACT FROM AUTHOR]

This study mainly concerns the linear quadratic optimal control and stabilisation problems for large‐scale systems with constraint, the constraint is imposed on the weighted control and state, which arises from coordination problem for multi‐agent systems. The main contributions of this study are: first, the optimal controller is developed via using the maximum principle and decoupling the forward and backward difference equations (FBDEs); secondly, by defining the Lyapunov function with the optimal cost function, the necessary and sufficient stabilisation conditions for the large‐scale systems are derived. The main techniques adopted in this study are the maximum principle and the solution to the FBDEs. It is noted that the optimal control strategy involves the centralised information of the weighted state average, but this can be easily accessed for the large‐scale‐system network. [ABSTRACT FROM AUTHOR]

Asymmetrical faults in the grid cause oscillations in dc‐link capacitor voltage or overcurrent in the converters. In this study, an optimal controller with a fixed structure is introduced based on the command generator tracker (CGT) to restrict the fault currents and increase stability performance under asymmetrical grid faults. For this purpose, first, the integrated dynamical model including grid‐connected converters and dc‐link voltage are introduced. Then, using the optimal control techniques, a CGT is designed to enhance the fault ride through performances. Next, the proposed controller is applied to the system including converter and dc‐link at the source side and the dynamic performances of the closed‐loop system under different fault conditions are determined. Finally, the simulation results are compared with the other conventional controllers and the dynamic performances of the proposed controller are evaluated. The results indicate that the proposed controller provides stability for the system and satisfies allowable constraints of converter current for different grid fault types such as voltage sags, asymmetrical and symmetrical faults. [ABSTRACT FROM AUTHOR]

We consider magnetic actuation and control of a spherical neutrally buoyant magnetic microrobot via magnetic coil setups and seek to design an optimal controller to reduce the required energy and coils' currents. We showed that in currently employed setups, where the actuation frequency is few tens of Hertz, the nonlinear dynamics of the system can be well approximated by a set of linear constrained ones. The approximated model is obtained by consciously overlooking the rotational dynamics and the inertia terms in translational dynamics. We acquired the linear quadratic regulation (LQR) controller for the approximated model which is a constrained time-varying system. Finally, 3D manipulation of a microrobot in a custom coil setup via the developed controller is simulated. The results show a significant reduction in necessary actuation effort with respect to previous common controllers. [ABSTRACT FROM AUTHOR]