Machining vibration is one of the most important constraints on productivity. This vibration may cause increase in machining costs, lower accuracy of products, and decrease tool life. Active control is one of the conventional methods for dealing with vibration in machining, but designing an optimized Controller for machining process due to unknown parameters in the system is challenging. DVF control method with low computational costs and high capability in increasing the performance of the cutting tool is an effective method, but due to increasing in actuator control input, it can cause actuator saturation; thus, it is not an efficient control method. The aim of this research is implementation of a Nonlinear fractional PID Controller for increasing effectiveness and improving performance of active vibration control on a boring bar. The results of impact control tests indicate that Nonlinear PID control algorithm has good performance in reducing vibrations and increasing the damping of the structure. Using the Controller performance criteria, the optimal fractional can be chosen for the Nonlinear PID Controller, which in addition to increasing the damping of the tool, can reduce the power consumption and, thus, prevent the actuator saturation. The results of the cutting tests also show that the Nonlinear PID Controller reduces control voltage and actuator power with respect to the DVF Controller, which results in improving the boundaries of stable machining. Moreover, during impacts in machining process, such as the initial engagement of the tool, the proposed Controller results in a significant reduction in the control voltage peak.

The purpose of this paper is to design a fractional order PID (FOPID) Controller base on stochastic algorithms for attitude control of quadrotor. The model is based on usage of Fractional calculus in the PID Controllers which can provide novel and higher performance extension for FOPID Controllers. Here, the main goal is to design an optimal PID and FOPID Controller using stochastic algorithms. Therefor two stochastic algorithms are used for optimal tuning of FOPID parameters. Genetic Algorithm (GA) and Particle Swarm Optimization are compared in minimizing the performance criteria formula which leads to a better performance in controlling of the plant. A quadrotor is installed on an experimental test stand includes of accelerometers, gyroscopes and microController with one and three degrees of freedom to test the performance of PID Controller. Kalman filter is used to reduce the noises by combination of accelerometer and gyroscope output. The result of the experiments in the paper shows that the quadrotor can perform desired motions successfully with the Controller in the response of step input and while it is subjected to external disturbances.

This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of Nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search space through the APSO algorithm whereas the local search is performed by BFO algorithm. The proposed algorithm starts with APSO algorithm. In the proposed APSO, every particle dynamically adjusts inertia weight according to feedback taken from particles best memories. In this case, APSO algorithm is used to enhance global search ability and to increase convergence speed. When the change in fitness value is smaller than a predefined value, the searching process is switched to BFO to accelerate the search process and find an accurate solution. In this way, this hybrid algorithm may find an optimum solution more accurately. To demonstrate the effectiveness of the proposed algorithm, its results are compared with those obtained by Basic PSO (BPSO), Standard BFO (SBFO), BFO with PSO (PSO-BFO), BFO with GA (GA-BFO) and Differential Evolution with BFO (DE-BFO). The numerical simulations are shown the potential of proposed algorithm.

This paper presents a practical implementation for a new formula of Nonlinear PID (NPID) control. The purpose of the Controller is to accurately trace a preselected position reference of one stage servomechanism system. The possibility of developing a transfer function model for the experimental setup is elusive because of the lack of system data. So, the identified model is developed via gathering experimental input/output data. The performance of the enhanced Nonlinear NPID Controller had been investigated by comparing it with a linear PID Controller. The harmony search tuning system is built to determine the optimum parameters for each control technique based on an effective objective function. The experimental outcomes and the simulation results show that the proposed NPID Controller has a minimum rise time and settling time through constant position reference test. Also, the NPID control is faster than the linear PID control by 40% in the case of the variable position reference test.

Electro-mechanical oscillations are produced in the machines of an interconnected power network, followed by a disturbance or due to high power transfer through weak tie lines. These oscillations should be damped as quickly as possible to ensure the reliable and stable operation of the network. To damp these oscillations different Controllers, based on local or wide area signals, have been the subject of many papers. This paper presents the analysis of the PID of Conventional Power System Stabilizer (PSS) and Fuzzy Logic Controller.

This paper proposes an optimized control policy over type one diabetes. Type one diabetes is taken into consideration as a Nonlinear model (Augmented Minimal Model), which is implemented in MATLAB-SIMULINK. This Model is developed in consideration of the patient's conditions. There are some uncertainties in the regarded model due to factors such as blood glucose concentration, daily meals or sudden stresses. Moreover, there are distinct approaches toward the elimination of these uncertainties. In here, a meal is fed to the model as an input in order to omit these uncertainties. Also, different control methods could be chosen to monitor the blood glucose level. In this paper, a Fractional Order PID is utilized as the control method. Thereafter, the control method and parameters are tuned by conducting genetic algorithm, as a powerful evolutionary algorithm. Finally, the output of the optimized Fractional order PID and traditional PID control method, which had the same parameters as the Fractional PID except the fractions, are compared. At the end, it is concluded by utilizing Fractional Order PID, not only the Controller performance improved considerably, but also, unlike the traditional PID, the blood glucose concentration is maintained in the desired range.

The issue of position/force control of collaborative robotic systems moving a payload is proposed in this paper. The proposed approach must be able to maintain the orientation/position of the payload on the reference trajectory while applying a limited force to the object through the robot's end-effector. With this in mind, linear/Nonlinear PID control schemes have been proposed to achieve accurate and robust tracking performance. Lyapunov's stability analysis is utilized to confirm the stability of the controlled system. It proves that the controlled system is stable, while the object’s orientation/position tracking errors are uniformly ultimately bounded (UUB) in any bounded region of state space. It also presents some conditions for proper selection of the linear/Nonlinear PID Controllers’ gains in the form of two theorems. The proposed Controllers apply to two coordinated 3DOF robotic arms that carry a payload. The simulation results tested two types of trajectories, including simple and complex paths. The results are also compared to those of a strong state-of-the-art approximator, the Chebyshev Neural Network (CNN).

In this paper, an algorithm is proposed to improve the constrained PID control design based on the convex-concave optimization. The control system is designed by optimizing a performance cost function, taking into account the stability and efficiency constraints with frequency domain analysis in which the sensitivity and complementary sensitivity concepts are used. It is shown, using a counter example, the previous methods are not effective for some systems, the optimization problem becomes unbounded and interrupted. To solve the problem, conditions where the optimization problem fails to have a response are analyzed and the previous limitations are eliminated by representing a new designing method. The performance of the proposed scheme is shown by applying it to the counter example. Moreover, the control system is designed for an unstable system.

In this paper, designing optimal PID Controller using modified particle swarm optimization is presented. The advantage of this new method compared to conventional methods of Controller design is that it is not limited to a certain class of systems. In designing phase, sum of rising time, settling time, overshoot and integral of squared-error are minimized. There kind of particle swarm optimization algorithms such as ePSO, mPSO and sPSO are compared with other methods of optimization including Ant Colony. The results clearly show how superior the new proposed method is to the other methods. The proposed method differs from the other optimization algorithms in such a way that, the proposed algorithm does not need a velocity equation. The position of the particle is updated directly by extrapolating the current particle position with the global best particle position obtained so far.

Designing of a PID Controller is a very common method for industrial process control and due to its very simple and efficient function; it is used in a wide variety of industrial applications. PID Controller to reduce the steady state error and dynamic response of the system is used. PID Controller design is an inevitable problem in setting the coefficients need to try a lot of trial and error, therefore the optimization of parameters in this Controller is attention of many researcher and there are many methods to find optimal parameters of PID Controller. Fast and exactly adjustment of the parameters optimized Controller is to create high quality answers. In this paper, an optimized tuning method for PID Controller is presented. In this method the PSO algorithm is used to design the parameters of an AVR (Automatic Voltage Regulation) system using various fitness functions. Easy implementation, stable convergence characteristic and high computational efficiency are among advantages of presented method.