An Acrobat robot is a Two-link surface robot that emulates the behavior of an acrobat man. This paper designed a dynamic output feedback control strategy to overcome system challenges such as its balancing and swing. This method is employed for the optimal control of the nonlinear model of the acrobat robot. It is important to note that dynamic feedback controllers are preferable to static ones due to their more effective control performances alongside with more degrees of freedom in achieving control objectives. In addition, the presented approach is incorporated with fuzzy control to optimize and update the parameters of the proposed controller. Simulations are performed in several cases consisting of the nominal system and considering the noise and disturbances. Simulation results demonstrate that the proposed controller in the form of fuzzy dynamic output feedback has a good performance compared to the static and dynamic output feedback in various situations.

In this paper, the energy equations of a manipulator, including Two flexible links, is extracted based on the Euler- Bernoulli beam assumptions. The robot can carry a specified mass as the load on its end- effector. Applying assumed modes method and considering a finite number of modes, the dynamical motion equations of the arm is achieved using Euler- Lagrange equations. The control and the vibration damping of the arm links is implemented using an active controller. This matter is done by applying a specified voltage on Two considered piezoelectric layers, the embedding location of which is external layers of beam. The algorithm of vibration control is designed according to the Lyapunov's second method. In addition, the path planning of the end- effector is performed by making use of an equivalent Two link rigid arm. Regarding the fact that the robot weight is included as well, and also it starts its own motion from the rest, the static deflection is calculated from Castigliano's method and is used as the initial conditions of beam. The performed simulation shows that the path of the controlled motion of flexible robot follows that of equivalent rigid manipulator end-effector with a good approximation.

In the dynamic manipulation of objects, the aim is to throw an object by a robot to the desired target even outside the reachable workspace. In this paper, the concept of the throw-able workspace or dynamic manipulation workspace is defined as a set of points which the robot is able to throw the object at them. Thus, in order to obtain the maximum dynamic manipulation workspace which means the farthest points that object can be manipulated, it is necessary to solve the optimal throwing problem. To this end, the optimal throwing problem is defined as the optimal control problem solved using the indirect solution method based on the fundamental theorem of the calculus of variations. By applying the throwing equation as a moving boundary condition, the derived optimality conditions construct a Two-point boundary value problem which its solution results in the optimal throwing. Finally, an algorithm is presented to calculate the maximum dynamic manipulation workspace. Then, simulation results are presented for a single link robot in order to evaluate the defined concept as well as the effectiveness of the proposed method for problem-solving.

Using robot manipulators for high accuracy applications require precise value of the kinematics parameters. Since measurement of kinematics parameters are usually associated with errors and accurate measurement of them is an expensive task, automatic calibration of robot link parameters makes the task of kinematics parameters determination much easier. In this paper a simple and easy to use algorithm is introduced for correction and calibration of robot kinematics parameters. Actually at several end-effecter positions, the joint variables are measured simultaneously. This information is then used in five different algorithms; least square (LS), particle swarm optimization (PSO), Genetic algorithms (GA), quadratic particle swarm optimization (QPSO) and simulated annealing particle swarm optimization (Sa_PSO) for automatic calibration and correction of the kinematics parameters. This process was also tested experimentally via a three degree of freedom manipulator which is actually used as a coordinate measuring machine (CMM). The experimental Results prove that the intelligent algorithms are useful for both parameter identification and calibration of link parameters.

In this paper, a new type of iterative learning control systems with fractional order known as iterative learning control with fractional order derivative and iterative learning control with fractional proportional–derivative for linearized systems of single-link robot arm is introduced. First order derivative of classic Arimoto is used for tracking error in updating law of derivative iterative learning control. The suggested method in this paper implements tracking error for updating control law of iterative learning of fractional order. For the first time, nonlinear robot system is linearized by input feedback linearization. Then, convergence analysis of iterative learning control law of type PDa is studied. In the next step, we define criteria for parameters optimization of proposed controller by using Biogeography-based optimization algorithm. Both updating laws of fractional order iterative learning control (Da-type ILC and PDa-type ILC) are applied on linearized robot arm and performance of both controllers for different value of a is presented. For improving the performance of closed loop system, coefficient of fractional order iterative learning control (proportional kp and derivative kD coefficients and a) is optimized by BBO algorithm. Proposed iterative learning control is compared with common type of system.

In this paper, an adaptive wavelet neural neTwork tracking controller is studied for solving control and stability problem of a class of uncertain nonlinear systems. The considered systems in this paper are of the discrete-time form in pure-feedback structure and include the backlash and external disturbance. The backlash nonlinearity input appears non-symmetric in the systems. These systems are more general than those in the previous work. There are major difficulties for stabilizing such systems and in order to overcome the difficulties, by using prediction function of future states, the systems are transformed into an n-step-ahead predictor. The wavelet neural neTworks are used to approximate the unknown functions and unknown backlash in the transformed systems and the adaption laws are to update neural weights and to compensate for the unknown parameter of backlash. Based on the Lyapunov theory, it is shown that the proposed controller guarantees that all the signals in the closed-loop system are bounded and the tracking error converges to a small neighborhood of zero. The simulation of a Single-link robot arm system is provided to verify the effectiveness of the control approach in the paper. Finally, in order to validate, the results of the proposed method are compared with the results of PID and sliding mode controller.

In this paper, to solve the output tracking problem of a single-link flexible joint manipulator, Polytopic Linear Models (PLMs) of the dynamics are made to take advantage of this method. Although linear control methods are very useful due to their powerful theories and simplicity, they can only be used in a neighborhood of the equilibrium point. One way to solve this problem is a PLMs-based method that linearizes the dynamics around several operating points. Therefore, in this paper, after calculating the PLMs of the manipulator, a state feedback control is applied to the derived linear dynamics that are augmented with the dynamics of the output tracking error. An extended method is used to decompose the scheduling space to construct PLMs, which is the segregation method improved with an extra aggregation. In order to avoid creating a large number of local models, an axis-oblique decomposition strategy is used instead of an axis-orthogonal decomposition. In addition, the scheduling functions of the PLMs are determined such that overlaps between the regions are avoided. By this selection, the output tracking problem becomes as a Linear Matrix Inequality (LMI) problem instead of a bilinear matrix inequality problem, which is more difficult to solve and may not lead to an optimal global solution.

Human walking is one of the most robust and adaptive locomotion mechanisms in nature, involves sophisticated interactions between neural and biomechanical levels. It has been suggested that the coordination of this process is done in a hierarchy of levels. The lower layer contains autonomous interactions between muscles and spinal cord and the higher layer (e.g. the brain cortex) interferes when needed. Inspiringly, in this study, we present a hierarchical control architecture in order to control under-actuated and high degree of freedom systems with limit cycle behavior and it is implemented for the walking control of a 3-link biped robot. In this architecture, the system is controlled by independent control units for each joint at the lower layer. In order to stabilize the system, these units are driven by a sensory feedback from the posture of the robot. A central stabilizing controller at the upper layer arises in case of failing the units to stabilize the system and take the responsibility of training the lower layer controllers. We show that using this architecture, a highly unstable system can be stabilized with identical simple controller units even though they do not have any feedback from all other units and the robot.