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The purpose of this paper is to review the current application areas of shape memory alloy (SMA) actuators in intelligent robotic systems and devices.

This paper analyses how actuation and sensing functions of the SMA actuator have been exploited and incorporated in micro and macro robotic devices, developed for medical and non‐medical applications. The speed of response of SMA actuator mostly depends upon its shape and size, addition and removal of heat and the bias force applied. All these factors have impact on the overall size of the robotic device and the degree of freedom (dof) obtained and hence, a comprehensive survey is made highlighting these aspects. Also described are the mechatronic aspects like the software and hardware used in an industrial environment for the control of such nonlinear actuator and the type of sensory feedback devices incorporated for obtaining better control, positioning accuracy and fast response.

SMA actuators find wide applications in various facets of robotic equipments. Selecting a suitable shape, fast heating and cooling method and better intelligent control technique with or without feedback devices could optimize its performance.

The frequency of SMA actuation purely depends on the rate of heat energy added to and removed from the actuator, which in turn depends upon interrelated nonlinear parameters.

For increasing the dof of robots, number of actuators also have to be increased that leads to complex control problems.

Explains the suitability of SMA as actuators in smart robotic systems, possibility of miniaturisation. It also highlights the difficulties faced by the SMA research community.

The purpose of this paper is to propose a mathematical model for voltage super-lift dc-dc power converter in continuous conduction mode (CCM). Using the presented mathematical model, the analysis of dynamics of power stage for voltage super-lift dc-dc power converter can be performed.

The proposed method is based on the average state space model using the state equations of the dc-dc power converter. In the proposed method, the converter is represented as a set of differential equations derived for each switching state of the power switch in terms of inductor current and capacitor voltage. The proposed method describes the dynamic behaviour of the system. The controller is designed to meet performance requirement of the system such as to maintain the dynamics such as stability, steady-state accuracy and the speed of response of the system. Using the obtained model, the analysis of dynamic response of the voltage super-lift dc-dc power converter can be performed.

The converter is modelled and verified using conventional circuit analysis method employing state-space averaging technique, and their corresponding transfer function is also derived. The dynamics of the converter is investigated using frequency response characteristics obtained using MATLAB programming environment. In addition, to improve the stability of the converter, proportional-integral controller is designed using Ziegler–Nichols tuning rules, and the effect of the compensator in the plant is also investigated.

The proposed method can be used for analysing the dynamics of power stage for voltage super-lift DC-DC power converter.

The purpose of this paper is to gain trajectory-tracking controllers for autonomous aircraft are optimized using a modified evolutionary, or genetic algorithm (GA).

The GA design utilizes real representation for the individual consisting of the collection of all controller gains subject to tuning. The initial population is generated randomly over pre-specified ranges. Alternatively, initial individuals are produced as random variations from a heuristically tuned set of gains to increase convergence time. A two-point crossover mechanism and a probabilistic mutation mechanism represent the genetic alterations performed on the population. The environment is represented by a performance index (PI) composed of a set of metrics based on tracking error and control activity in response to a commanded trajectory. Roulette-wheel selection with elitist strategy are implemented. A PI normalization scheme is also implemented to increase the speed of convergence. A flexible control laws design environment is developed, which can be used to easily optimize the gains for a variety of unmanned aerial vehicle (UAV) control laws architectures.

The performance of the aircraft trajectory-tracking controllers was shown to improve significantly through the GA optimization. Additionally, the novel normalization modification was shown to encourage more rapid convergence to an optimal solution.

The GA paradigm shows much promise in the optimization of highly non-linear aircraft trajectory-tracking controllers. The proposed optimization tool facilitates the investigation of novel control architectures regardless of complexity and dimensionality.

The addition of the evolutionary optimization to the WVU UAV simulation environment enhances significantly its capabilities for autonomous flight algorithm development, testing, and evaluation. The normalization methodology proposed in this paper has been shown to appreciably speed up the convergence of GAs.

The paper provides a flexible generalized framework for UAV control system evolutionary optimization. It includes specific novel structural elements and mechanisms for improved convergence as well as a comprehensive PI for trajectory tracking.

In computer application scenario, data mining task is rarely utilized in power system, as an enhanced part, this work presented data mining task in power systems, to overcome frequency deviation issues. Load frequency control (LFC) is a primary challenging problem in an interconnected multi-area power system.

This paper adopts lion algorithm (LA) for the LFC of two area multi-source interconnected power systems. The LA calculates the optimal gains of the fractional order PI (FOPI) controller and hence the proposed LA-based FOPI controller (LFOPI) is developed.

For the performance analysis, the proposed algorithm compared with various algorithm is given as, 80.6% lesser than the FOPI algorithm, 2.5% lesser than the GWO algorithm, 2.5% lesser than the HSA algorithm, 4.7% lesser than the BBO algorithm, 1.6% lesser than PSO algorithm and 80.6% lesser than the GA algorithm.

The LFOPI controller is the proposed controlling method, which is nothing but the FOPI controller that gets the optimal gain using the LA. This method produces better performance in terms of converging behavior, optimization of controller gain, transient profile and steady-state response.

The purpose of this paper is to improve dynamic performance of the permanent magnet synchronous motor (PMSM) field oriented control by means of optimizing its PI controller’s coefficients.

In this paper, Krill Herd (KH) optimization algorithm is proposed to optimize PI controller coefficients. Moreover, drive control system and steady state estimator are implemented in discrete mode. In order to evaluate the performance of the proposed algorithm, a comparison is carried out between KH and PSO algorithms. It is noteworthy that PSO algorithm has presented the best dynamic response among the studied approaches in literature.

By using the proposed optimization algorithm, the system tracks the reference speed with a very low error. In addition, the discrete mode simulations do not require initiating integrators. Moreover, it significantly increases the processing speed. The proposed approach is carried out in both sensor and sensorless drive systems. The simulation results indicate lower torque ripple, shorter settling time, lower overshoot, faster dynamic response and higher robustness against speed, torque and machine parameter changes.

The PI controller is extensively used in practical drive systems, due to its simple structure and implementation as well as its acceptable performance. On the other hand, PMSM benefits from high power density, high efficiency, low torque ripple, low inertia, and high performance in a wide speed range. This paper presents an efficient approach to optimize PI coefficients of the PMSM drive system.

This paper proposes a new algorithm (KH algorithm) to determine the coefficients of PI controllers in order to improve dynamic performance of the PMSM drive system.

This study aims to present a method for the conceptual design and simulation of an aircraft flight control system.

The design methodology is based on particle swarm optimization (PSO). PSO can be used to improve the performance of conventional controllers. The aim of the present study is threefold. First, it attempts to detect and isolate faults in an aircraft model. Second, it is to design a proportional (P) controller, a proportional derivative (PD) controller, a proportional-integral (PI) controller and a fuzzy controller for an aircraft model. Third, it is to design a PD controller for an aircraft using a PSO algorithm.

Conventional controllers, an intelligent controller and a PD controller-based PSO were investigated for flight control. It was seen that the P controller, the PI controller and the PD controller-based PSO caused overshoot. These overshoots were 18.5, 87.7 and 2.6 per cent, respectively. Overshoot was not seen using the PD controller or fuzzy controller. Steady state errors were almost zero for all controllers. The PD controller had the best settling time. The fuzzy controller was second best. The PD controller-based PSO was the third best, but the result was close to the others.

This study shows the implementation of the present algorithm for a specified space mission and also for study regarding variation of performance parameters. This study shows fault detection and isolation procedures and also controller gain choice for a flight control system. A comparison between conventional controllers and PD-based PSO controllers is presented. In this study, sensor fault detection and isolation are carried out, and, also, root locus, time domain analysis and Routh–Hurwitz methods are used to find the conventional controller gains which differ from other studies. A fuzzy controller is created by the trial and error method. Integral of squared time multiplied by squared error is used as a performance function type in PSO.

The direct torque control (DTC) of induction motor (IM) drive is featured by high ripples in the electromagnetic torque and stator flux profiles because they are controlled by two hysteresis regulators. Furthermore, the machine flux is not directly measurable. Hence, it is better to reconstitute it from the instantaneous electrical equations of the machine. Once the stator flux is estimated, we can guarantee a reliable sensorless DTC control. Thus, the purpose of this research work is to ensure fast response and full reference tracking of the IM under sensorless DTC strategy with desired dynamic behavior and low ripple levels.

In this work, an improved DTC strategy, which is DTC_SVM_3L, is suggested. The first step of the designed approach is to substitute the conventional inverter feeding the motor with a three-level inverter because it guarantees reduced switching losses, improved quality of voltage waveform and low-current total harmonic distortion rate. The second aim of this paper is to make the IM operate at a constant switching frequency by using the nearest three vectors-based space vector modulation (SVM) technique rather than hysteresis controllers. The third objective of this study is to conceive a sliding-mode stator flux observer, which can improve the control performances by using a sensorless algorithm to get an accurate estimation, and consequently, increase the reliability of the system and decrease the cost of using sensors. The stability of the proposed observer is demonstrated based on the Lyapunov theory. To overcome the load change disturbance in the proposed DTC control strategy, this paper exhibits a comparative assessment of four speed regulation methods: classical proportional and integral (PI) regulator, fuzzy logic PI controller, particle swarm optimization PI controller and backstepping regulator. The entire control algorithm was tested under different disturbances such as stator resistance and load torque variations.

It was ascertained that the IM, controlled with three-level inverter, exhibits good performances under the proposed DTC-SVM strategy based on a sliding-mode observer. The robustness of the suggested approach against parameter variations is also proved.

The theoretical development of the proposed control strategy is thoroughly described. Then, simulations using Matlab/Simulink software are launched to investigate the merits of the sensorless DTC-SVM command of three-level inverter-fed IM drive with different speed regulators.

Nowadays, in order to improve current applications, industry incorporates to their solution approaches artificial intelligence techniques and methodologies like Fuzzy Logic, neural networks and/or genetic algorithms (GA). Artificial intelligence techniques complement classical methodologies and include concepts that simulate the way humans solve problems or how processes work in nature. In this work, the Fuzzy Logic system cancels the effects of load perturbances in an energy plant, by implementing a secondary controller which complements the main controller. The purpose of this paper is to use GA to tune this new secondary controller. The authors particularize the proposal for three specific applications: control the angular speed and position of a Direct Current (DC) motor and control the output voltage of a DC/DC buck converter.

The authors use GA for tuning a Proportional-Integral Fuzzy Controller (PI-Fuzzy). The proposal defines a new objective function in comparison with literature approaches. The main key in the new objective function is combining the best features of Integral Square Error (ISE) function and taking out the overshoot response.

In order to demonstrate the proposed methodology based on GA tuning a PI-Fuzzy, the authors apply the literature benchmark to the solution. The results are compared with the following techniques: Robust control, continuous PID control, discrete PID control, Optimal Control, Fuzzy Control and Artificial Neural Network based control. Comparisons are presented in terms of setting time and overshot.

Results demonstrate that ISE or integral of absolute value of error function do not provide the desired response. Achieved results demonstrate the usefulness of the proposal to eliminate the overshoot of the traditional behaviour without lost any of the main features of the literature methodologies.

Deadlock is a rather undesirable phenomenon and must be well solved in flexible manufacturing systems (FMS). This paper aims to propose a general iterative deadlock control method for a class of generalized Petri nets (GPN), namely, G-systems, which can model an FMS with assembly and disassembly operations of multiple resource acquisition. When given an uncontrolled G-system prone to deadlocks, the work focuses on the synthesis of a near-optimal, non-blocking supervisor based on reachability graph (RG) analysis.

The concept of a global idle place (GIP) for an original uncontrolled G-system is presented. To simplify the RG computation of an uncontrolled G-system, a GIP is added temporarily to the net model during monitor computation steps. Starting with one token and then by gradually increasing the number of tokens in the GIP at each iteration step, the related net system is obtained. The minimal-covered-set of all bad markings of the related net system suffering from deadlock can be identified and then removed by additional monitors through an established place-invariant control method. Consequently, all related systems are live, and the GIP is finally removed when the non-blockingness of the controlled system is achieved. Meanwhile, the redundancy of monitors is also checked.

A typical G-system example is provided to demonstrate the applicability and effectiveness of the proposed method. Experiments show that the proposed method is easy to use and provides very high behavioral permissiveness for G-system. Generally, it can achieve an optimal or a near-optimal solution of the non-blocking supervisor.

In this work, the concept of GIP for G-systems is presented for synthesis non-blocking supervisors based on RG analysis. By using GIP, an effective deadlock control method is proposed. Generally, the proposed method can achieve an optimal or a near-optimal, non-blocking supervisor for an uncontrolled G-system prone to deadlocks.

The purpose of this paper is to propose an efficient current control technique based on model predictive control (MPC) for grid-connected wind conversion system. This nonlinear strategy is applied for the chopper circuit and grid-tied inverter and compared with other two conventional schemes; a traditional proportional-integral (PI) and sliding mode controller (SMC) using the same switching frequency.

Firstly, the MPC scheme uses the mathematical model to predict future behaviors of the controlled converter outputs for possible switching states. After that, the optimal voltage vector is selected by minimizing a cost function, which is defined as a sum of the absolute values of the controlled current errors. Then, the corresponding switching signals are applied to the converter switches in the next sampling period to track correctly the reference current. Thus, the MPC scheme ensures a minimal error between the predicted and reference trajectories of the considered variables.

The MPC-based algorithm presents several benefits in terms of high accuracy control, reduced DC-link voltage ripples during steady-state operation, faster transient response, lower overshoots and disturbance rejection and acceptable total harmonic distortion.

The authors introduce several simulation case studies, using PSIM software package, which prove the reliability and effectiveness of the proposed MPC scheme. Therefore, the MPC performances, during dynamic and steady-state condition, are compared with those obtained by a PI regulator and SMC to highlight the improvements, specifically the transfer of smooth power to the grid.