Auto-tuning of PID Controllers for Robotic Manipulators Using PSO and MOPSO

This work proposes two approaches to automatic tuning of PID position controllers based on different global optimization strategies. The chosen optimization algorithms are PSO and MOPSO, i. e. the problem is handled as a single objective problem in the first implementation and as a multiobjective problem in the second one. The auto-tuning is performed without assuming any previous knowledge of the robot dynamics. The objective functions are evaluated depending on real movements of the robot. Therefore, constraints guaranteeing safe and stable robot motion are necessary, namely: a maximum joint torque constraint, a maximum position error constraint and an oscillation constraint. Because of the practical nature of the problem in hand, constraints must be observed online. This requires adaptation of the optimization algorithm for reliable observance of the constraints without affecting the convergence rate of the objective function. Finally, Experimental results of a 3-DOF robot for different trajectories and with different settings show the validity of the two approaches and demonstrate the advantages and disadvantages of every method

A Practical Approach for the Auto-tuning of PD Controllers for Robotic Manipulators using Particle Swarm Optimization

An auto-tuning method of PD controllers for robotic manipulators is proposed. This method suggests a practical implementation of the particle swarm optimization technique in order to find optimal gain values achieving the best tracking of a predefined position trajectory. For this purpose, The integral of the absolute error IAE is used as a cost function for the optimization algorithm. The optimization is achieved by performing the desired movement of the robot iteratively and evaluating the cost function for every iteration. Therefor, the necessary constraints that guarantee a safe and stable movement of the robot are defined, which are: a maximum joint torque constraint, a maximum position error constraint and an oscillation constraint. A constraint handling approach is suggested for the optimization algorithm in order to adapt it to the problem in hand. Finally, the efficiency of the proposed method is verified through a practical experiment on a real robot

Evolutionary algorithm based controller for double link flexible robotic manipulator

The paper investigates the development of intelligent hybrid collocated and non-collocated PID controller for hub motion and end point vibration suppression of double-link flexible robotic manipulator. The system was modelled using multi-layer perceptron neural network structure based on Nonlinear Autoregressive Exogenous (NARX) model. The hybrid controllers are incorporated with optimization algorithm that is ABC and PSO to find out the parameters of the PID controllers. Numerical simulation was carried out in MATLAB/Simulink to evaluate the system in term of tracking capability and vibration suppression for both links. The results show that PSO revealed the superiority over ABC in controlling the system

PUMA 560 Trajectory Control Using NSGA-II Technique With Real Valued Operators

In the industry, Multi-objectives problems are a big defy and they are also hard to be conquered by conventional methods. For this reason, heuristic algorithms become an executable choice when facing this kind of problems.The main objective of this work is to investigate the use of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) technique using the real valued recombination and the real valued mutation in the tuning of the computed torque controller gains of a PUMA560 arm manipulator. The NSGA-II algorithm with real valued operators searches for the controller gains so that the six Integral of the Absolute Errors (IAE) in joint space are minimized. The implemented model under MATLAB allows an optimization of the Proportional-Derivative computed torque controller parameters while the cost functions and time are simultaneously minimized.. Moreover, experimental results also show that the real valued recombination and the real valued mutation operators can improve the performance of NSGA-II effectively

The Efficiency of an Optimized PID Controller Based on Ant Colony Algorithm (ACO-PID) for the Position Control of a Multi-articulated System

In this article, a robot manipulator is controlled by the PID controller in a closed loop system with unit feedback. The difficulty of using the controller is parameter tuning, because the tuning parameters still use the trial and error method to find the PID parameter constants, namely Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). In this case the Ant colony Optimization algorithm (ACO) is used to find the best gain parameters of the PID. The Ant algorithm is a method of combinatorial optimization, which utilizes the pattern of ants search for the shortest path from the nest to the place where the food is located, this concept is applied to tuning PID parameters by minimizing the objective function such that the robot manipulator has improved performance characteristics. This work uses the Matlab Simulink environment, First, after obtaining the system model, the ant colony algorithm is used to determine the proper coefficients p, i, and Kd in order to minimize the trajectory errors of the two joints of the robot manipulator. Then, the parameters will implement in the robot system. According to the results of the computer simulations, the proposed method (ACO-PID) gives a system that has a good performance compared with the classical PID

Optimization of a P/PI Cascade Motion Controller for a 3-DOF Delta Robot

An auto-tuning method for a Delta robot’s P/PI cascade motion controller using multi-objective optimization algorithm is proposed. The implemented control structure consists of two controllers: A feedforward controller based on a model of the inverse dynamics of the robot, and a cascade P/PI controller to compensate for unmodeled effects. The auto-tuning is achieved in the sense of optimizing the control parameters in three stages. In the first stage, the feedback control parameters are optimized after neglecting the feedforward control term. The goal is to minimize the position error in tracking an excitation trajectory, which is used as well to identify the dynamic model parameters in the second stage. After that, the feedforward compensation term is computed offline based on the desired trajectory. In the final stage, the P/PI parameters are optimized again after adding the feedforward controller. Experimental results on an industrial 3-dof Delta robot validates the efficiency of the proposed method

Multi-objective tuning for torque PD controllers of cobots

Collaborative robotics is a new and challenging field in the realm of motion control and human-robot interaction. The safety measures needed for a reliable interaction between the robot and its environment hinder the use of classical control methods, pushing researchers to try new techniques such as machine learning (ML). In this context, reinforcement learning has been adopted as the primary way to create intelligent controllers for collaborative robots, however supervised learning shows great promise in the hope of developing data-driven model based ML controllers in a faster and safer way. In this work we study several aspects of the methodology needed to create a dataset to be used to learn the dynamics of a robot. For this we tune several PD controllers to several trajectories, using a multi-objective genetic algorithm (GA) which takes into account not only their accuracy, but also their safety. We demonstrate the need to tune the controllers individually to each trajectory and empirically explore the best population size for the GA and how the speed of the trajectory affects the tuning and the dynamics of the robot.Comment: Accepted for presentation at the CPS workshop 202

Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman’s ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms

A graph-theory-based C-space path planner for mobile robotic manipulators in close-proximity environments

In this thesis a novel guidance method for a 3-degree-of-freedom robotic manipulator arm in 3 dimensions for Improvised Explosive Device (IED) disposal has been developed. The work carried out in this thesis combines existing methods to develop a technique that delivers advantages taken from several other guidance techniques. These features are necessary for the IED disposal application. The work carried out in this thesis includes kinematic and dynamic modelling of robotic manipulators, T-space to C-space conversion, and path generation using Graph Theory to produce a guidance technique which can plan a safe path through a complex unknown environment. The method improves upon advantages given by other techniques in that it produces a suitable path in 3-dimensions in close-proximity environments in real time with no a priori knowledge of the environment, a necessary precursor to the application of this technique to IED disposal missions. To solve the problem of path planning, the thesis derives the kinematics and dynamics of a robotic arm in order to convert the Euclidean coordinates of measured environment data into C-space. Each dimension in C-space is one control input of the arm. The Euclidean start and end locations of the manipulator end effector are translated into C-space. A three-dimensional path is generated between them using Dijkstra’s Algorithm. The technique allows for a single path to be generated to guide the entire arm through the environment, rather than multiple paths to guide each component through the environment. The robotic arm parameters are modelled as a quasi-linear parameter varying system. As such it requires gain scheduling control, thus allowing compensation of the non-linearities in the system. A Genetic Algorithm is applied to tune a set of PID controllers for the dynamic model of the manipulator arm so that the generated path can then be followed using a conventional path-following algorithm. The technique proposed in this thesis is validated using numerical simulations in order to determine its advantages and limitations