Background: Functional electrical stimulation (FES) is the most commonly used system for restoring functions after spinal cord injury (SCI). Objective: In this study we investigated feedback PID and feedforward-feedback P-PID controllers for regulating the elbow joint angle. Methods: The controllers were tuned based on a nonlinear muculoskeletal model containing two links, one joint with one degree of freedom and two muscles in the sagittal plane that was simulated in MATLAB using Sim Mechanics and Simulink toolboxes. The first tune of the PID and P-PID controllers was done by trial and error. Then, the coefficients were optimized by genetic algorithm (GA). For checking the robustness of the controllers, we compared the amount of rise time, settling time, maximum overshoot and steady state error under three conditions: the first was when the initial angle of the joint was fixed and only the desired angles changed; the second was with a fixed step as input and various initial angles; and the last condition was with different maximum forces for muscle. Results: Genetic controllers had better performance than the trial and error tuned controllers. The amounts of settling time were not so different for the controllers in condition 1 but had more variations in condition 2 and had really better results in genetic P-PID in condition 3. The overshoot was pretty less in PIDs than in P-PIDs and the steady state error was almost zero for all of the controllers. Conclusion: Genetic controllers had a better performance than the trial and error tuned controllers. The rise time was much less in P-PIDs than in PIDs.

The body freedom flutter phenomenon is one of the aeroelastic instabilities that occurs due to the coupling of the aeroelastic bending mode of the wing with the short-period mode in the flight dynamics of the aircraft. By using the aeroservoelastic model and applying closed loop control, this phenomenon can be suppressed in the operating conditions of the aircraft and the velocity of this event can be increased. The simplest model aircraft capable of displaying this instability includes the flexible wing and the planar flight dynamics model. For this purpose, the wing structure is modeled using the Euler-Bernoulli beam and, the theory of minimum variable state is used to model unstable aerodynamics to make the conditions suitable for modeling the system in state space. In the control section, the elevator is used as the control surface and LQR theory with Kalman filter is used to body freedom flutter suppression. Finally, the effect of adding a closed loop control to increase the body freedom flutter velocity and the limitations of this work are studied.

This paper concerns the design of a neural state observer for nonlinear dynamic systems with noisy measurement channels and in the presence of small model errors. The proposed observer consists of three feedforward neural parts, two of which are MLP universal approximators, which are being trained off-line and the last one being a Linearly Parameterized Neural Network (LPNN), which is being updated on-line. The off-line trained parts are able to generate state estimations instantly and almost accurately, if there are not catastrophic errors in the mathematical model used. The contribution of the on-line adapting part is to compensate the remainder estimation error due to uncertain parameters and/or unmodeled dynamics. A time delay term is also added to compensate the arising differential effects in the observer. The proposed observer can learn the noise cancellation property by using noise corrupted data sets in the MLPs off-line training. Simulation results in two case studies show the high effectiveness of the proposed state observing method.

Sediment transport as a complicated and important phenomenon has attracted a lot of researchers during the last century; however there are some formulae to evaluate sediment loads in aquatic systems. Most of them still face two major problems: firstly, lack of accuracy and secondly, involvement of many parameters which makes them more challenging.Artificial Neural Networks are known as model-free universal function approximators well suited to deal with real life engineering problems including time series predictions and parameter estimation. In this paper, sediment loads are predicted using two different types of multilayer feedforward neural networks, namely Multi-Layer perception (MLP) and Radial Basis Function (RBF). The input variables for both structures are considered to be flow discharge, mean flow depth and width, mean bed material's diameter and water surface slope and the output is sediment discharge. Some different cases have been studied. The results are promising. It has been also observed that mean square prediction errors for the developed MLP is equal to 0.0063 while the devised RBF networks produces much larger mean square errors, namely 0.01260. This indicates that the MLP-load-predictor outperforms the RBF-predictor.

The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural network convergence rate is relatively slow and solutions can be trapped at local minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train the feedforward neural network. This particle swarm optimization feedforward neural network is applied to model the daily rainfall-runoff relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall, antecedent rainfall and antecedent runoff, while the output is current runoff. Particle swarm optimization feedforward neural network simulated the current runoff accurately with R = 0.872 and E2 = 0.775 for the training data set and R = 0.900 and E2 = 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward neural network method can be successfully used to model the rainfall-runoff relationship in Bedup Basin and it could be to be applied to other basins.

In this paper, the inverse dynamics solution for feedforward control of cooperative flexible manipulators is investigated. The internal dynamics of flexible manipulators are unstable, and to obtain a bounded solution to the inverse dynamics problem, the constrained nonlinear optimization method is used. In the optimization method, the aim is to minimize the elastic energy of the manipulators despite several constraints. These constraints include: 1) dynamic equations, 2) Spatial and force trajectory, 3) kinematic constraints limiting the movement of manipulators, 4) constraints related to superfluous variables and 5) constraints of the generalized α method for the stability of the solution. The method used for dynamic modeling is based on the Lagrange equation and finite element discretization. Lagrange multipliers have been used to control the internal forces applied to the payload, and to prevent the change of direction in force control, an inequality constraint has been added to the optimization constraints. This method is implemented on flexible cooperative manipulators and has the ability to control the path of the payload and the force applied to it.

In this paper, the load frequency control problem of microgrids in islanded operation mode is tackled using Fuzzy feedforward PI (FFPI) controller. To this end, a feedforward loop is considered in the control structure of the microgrid in addition to the classical feedback controller wherein a proportional integrator controller is used. The disturbance signal, which can be load variations or renewable energy resources uncertainties, is estimated using a disturbance observer. The understudy microgrid includes wind turbine and solar cells as renewable sources, and a diesel generator and loads. A fuzzy controller is also used for pitch angle control of the wind turbine, which may smooth out the generated power and improve the frequency control of microgrid. To show the capability of the proposed strategy, two different scenarios are considered and the obtained simulation results easily approve the efficiency of the proposed structure for load frequency control of microgrid in islanded operation mode.

One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust adaptive control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust adaptive control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

An iterative tuning method is presented to obtain the multi-input multi-output (MIMO) feedforward controller coefficients to improve disturbance rejection in non-minimum phase (NMP) MIMO systems. In the NMP systems, eliminating the effect of disturbances may cause instability and also can impose extra costs to control the entire system. For this purpose, a simple feedforward controller structure is proposed. The unknown variables of the feedforward controller are calculated using LMIs such that the H∞ norm of the transfer function matrix from disturbance to output is minimized. By taking advantage of the frequency sampling techniques into account and using some iterative algorithms, a new tractable method is constructed to solve the problem. Also, a condition based on the right half plane (RHP) zero direction for the NMP system has been proposed to improve the disturbance rejection property of these systems. To obtain optimal coefficients, the algorithm is repeated several times to reach the best answer. The method employs convex technics and CVX software to perform calculations. The efficiency of the method is shown in various practical examples using different performance indicators such as integral of absolute error (IAE), integral of squared error (ISE), integral of time multiplied by absolute error (ITAE), integral of time multiplied by squared error (ITSE).