A survey of adaptive control technology in robotics

Previous work on the adaptive control of robotic systems is reviewed. Although the field is relatively new and does not yet represent a mature discipline, considerable attention has been given to the design of sophisticated robot controllers. Here, adaptive control methods are divided into model reference adaptive systems and self-tuning regulators with further definition of various approaches given in each class. The similarity and distinct features of the designed controllers are delineated and tabulated to enhance comparative review

The dynamic control of robotic manipulators in space

Described briefly is the work done during the first half year of a three-year study on dynamic control of robotic manipulators in space. The research focused on issues for advanced control of space manipulators including practical issues and new applications for the Virtual Manipulator. In addition, the development of simulations and graphics software for space manipulators, begun during the first NASA proposal in the area, has continued. The fabrication of the Vehicle Emulator System (VES) is completed and control algorithms are in process of development

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance

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

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

Dual Design PID Controller for Robotic Manipulator Application

This research introduces a dual design proportional–integral–derivative (PID) controller architecture process that aims to improve system performance by reducing overshoot and conserving electrical energy. The dual design PID controller uses real-time error and one-time step delay to adjust the confidence weights of the controller, leading to improved performance in reducing overshoot and saving electrical energy. To evaluate the effectiveness of the dual design PID controller, experiments were conducted to compare it with the PID controller using least overshoot tuning by Chien–Hrones–Reswick (CHR)  technique. The results showed that the dual design PID controller was more effective at reducing overshoot and saving electrical energy. A case study was also conducted as part of this research, and it demonstrated that the system performed better when using the dual design PID controller. Overshoot and electrical energy consumption are common issues in systems that can impact performance, and the dual design PID controller architecture process provides a solution to these issues by reducing overshoot and saving electrical energy. The dual design PID controller offers a new technique for addressing these issues and improving system performance. In summary, this research presents a new technique for addressing overshoot and electrical energy consumption in systems through the use of a dual design PID controller. The dual design PID controller architecture process was found to be an effective solution for reducing overshoot and saving electrical energy in systems, as demonstrated by the experiments and case study conducted as part of this research. The dual design PID controller presents a promising solution for improving system performance by addressing the issues of overshoot and electrical energy consumption

Experimental comparison of parameter estimation methods in adaptive robot control

In the literature on adaptive robot control a large variety of parameter estimation methods have been proposed, ranging from tracking-error-driven gradient methods to combined tracking- and prediction-error-driven least-squares type adaptation methods. This paper presents experimental data from a comparative study between these adaptation methods, performed on a two-degrees-of-freedom robot manipulator. Our results show that the prediction error concept is sensitive to unavoidable model uncertainties. We also demonstrate empirically the fast convergence properties of least-squares adaptation relative to gradient approaches. However, in view of the noise sensitivity of the least-squares method, the marginal performance benefits, and the computational burden, we (cautiously) conclude that the tracking-error driven gradient method is preferred for parameter adaptation in robotic applications

MODELLING AND CONTROL OF A TWO-LINK RIGID-FLEXIBLE MANIPULATOR

The literature lacks data on the reliability of 3D models created by Autodesk Inventor software and imported to MATLAB Simulink software in comparison to mathematically generated models. In this contribution, a two-link rigid-flexible manipulator modelled in two different methods was demonstrated, one of which is using Lagrange equations and Finite Element Method to generate a mathematical model of the manipulator, and the other is creating a 3D model with the aid of Autodesk Inventor then import to MATLAB Simulink, both models were subsequently controlled by three types of controllers, conventional PID controller, LQR controller, and LQG controller. The research demonstrated the performance of the two models with response to the three types of controllers. Achieved results have proven that the Autodesk Inventor is considered a reliable tool for modelling mechanical systems. Results have also confirmed that modern controllers, i.e., LQR and LQG controllers perform much better than conventional PID controllers with regards to the manipulator movement. The implementation of Autodesk Inventor along with MATLAB Simulink indicates that the Autodesk Inventor can be considered as an instrumental tool for designers and engineers. The results enable future developments in the frontier area of robotics and mechanical systems, where sophisticated models could be generated by Autodesk Inventor instead of being modelled mathematically which will benefit engineers and designers by saving time and effort consumed in modelling using mathematical equations, and by reducing the potential errors associated with such modelling technique

Performance Comparison of Several Control Algorithms for Tracking Control of Pantograph Mechanism

A sort of parallel manipulator known as a pantograph robot mechanism was created primarily for industrial requests that required high precision and satisfied speed. While tracking a chosen trajectory profile requires a powerful controller. Because it has four active robot links and one robot passive link in place of just two links like the open chain does, it can carry more loads than the open chain robot mechanism while maintaining accuracy and stability. The calculated model for a closed chain pantograph robot mechanism presented in this paper takes into account the boundary conditions. For the purpose of simulating the dynamics of the pantograph robot mechanism, an entire MATLAB Simulink has been created. The related Simscape model had been created to verify the pantograph mathematical model that had been provided. Five alternative tracking controllers were also created and improved using the Flower Pollination (FP) algorithm. The PID controller, which is used in many engineering applications, is the first control. An enriched Fractional Order PID (FOPID) controller is the second control. The third control considers an improved Nonlinear conventional PID (NLPID) controller, and the parameters for this controller were likewise determined using (FP) optimization using the useful objective function. Model Reference Adaptive Control (MRAC) with PID Compensator is the fourth control. The Fuzzy PD+I Control is the last and final controller. A comparison of the different control methods was completed. A rectangular trajectory was chosen as the end effector of the pantograph robot\u27s position reference because it displays performance during sharp edges and provides a more accurate study. The proposed controllers were used for this task to analyse the performance. The outcomes demonstrate that the Fuzzy PD+I control outperforms the PID, FOPID, NLPID, and MRAC with PID Compensator controllers in terms of performance. In the case of the Fuzzy PD+I control, the angles end effector has a lower rise time, a satisfied settling time, and low overshoot with good precision

Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 -6 and (PSO)=3.4948 -4