Active Inference for Integrated State-Estimation, Control, and Learning

This work presents an approach for control, state-estimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent in computational neuroscience as a theory of the brain, where behaviour arises from minimizing variational free-energy. The robotic manipulator shows adaptive and robust behaviour compared to state-of-the-art methods. Additionally, we show the exact relationship to classic methods such as PID control. Finally, we show that by learning a temporal parameter and model variances, our approach can deal with unmodelled dynamics, damps oscillations, and is robust against disturbances and poor initial parameters. The approach is validated on the `Franka Emika Panda' 7 DoF manipulator.Comment: 7 pages, 6 figures, accepted for presentation at the International Conference on Robotics and Automation (ICRA) 202

Purwarupa Sistem Kendali Kestabilan Pesawat Tanpa Awak Sayap Tetap Menggunakan Robust PID

 This study has implemented stability control system of Unmanned Aerial Vehicle (UAV) using  robust PID. The aircraft stability refers to wind against in glidding condition with straight movement. Robust PID used to control aircraft motion system. Control parameters obtained from the IMU sensor roll, pitch and yaw. IMU data are computed using DCM algorithm that produces Eulerian angles. Type PID control is determined by Ziegler-Nichols methods theory of oscillations. Control system are varied three types, there are P, PI, and PID. The results have the best type of PID control with D constant value = 0 for each motion systems. PID constant value used for the aileron Kp = 2,93, Ki = 2,808  and Kd = 0, elevators Kp = 2,02, Ki = 1,731 and Kd = 0 and rudder Kp = 1,35, Ki = 0,9 and Kd = 0. Robust method using ISE (Integral Squared Error) which replaces integral PID control error. The system was tested using two mode. Mode A (Manual-PID-RobustPID) and mode B (Manual-RobustPID-PID). The result of robust PID methods is able to make the system response to disturbances better than regular PID that increase the settling time of aileron 63.67% , elevator 41.42% and rudder 57.33%

An Innovative Systematic Approach to Financial Portfolio Management via PID Control

This book discusses the theory, application, and practice of PID control technology. It is designed for engineers, researchers, students of process control, and industry professionals. It will also be of interest for those seeking an overview of the subject of green automation who need to procure single loop and multi-loop PID controllers and who aim for an exceptional, stable, and robust closed-loop performance through process automation. Process modeling, controller design, and analyses using conventional and heuristic schemes are explained through different applications here. The readers should have primary knowledge of transfer functions, poles, zeros, regulation concepts, and background. The following sections are covered: The Theory of PID Controllers and their Design Methods, Tuning Criteria, Multivariable Systems: Automatic Tuning and Adaptation, Intelligent PID Control, Discrete, Intelligent PID Controller, Fractional Order PID Controllers, Extended Applications of PID, and Practical Applications. A wide variety of researchers and engineers seeking methods of designing and analyzing controllers will create a heavy demand for this book: interdisciplinary researchers, real time process developers, control engineers, instrument technicians, and many more entities that are recognizing the value of shifting to PID controller procurement

Review of PID Controller Applications for UAVs

Unmanned Aerial Vehicles (UAVs) have gained widespread recognition for their diverse applications, ranging from surveillance to delivery services. Among the various control algorithms employed to stabilize and navigate UAVs, the Proportional-Integral-Derivative (PID) controller stands out as a classical yet robust solution. This review provides a comprehensive examination of PID controller applications in the context of UAVs, addressing their fundamental principles, dynamics modeling, stability control, navigation tasks, parameter tuning methods, challenges, and future directions

Performance Enhancement of SOPDT System with Numerically Optimized PID Controller

This paper presents a simple but effective method for designing robust PID controller. The robust PID controller design problem is solved by the maximization, on a finite interval of the shortest distance from the Nyquist curve of the open loop transfer function to the critical point -1+j0 i.e. from the knowledge of maximum sensitivity? M?_(s ). Simple formulae are derived to tune/design PID controllers to achieve the improved performance for the given process or system. From control theory we know that all the real time processes have inherent time delays and time constants associated with it. The PID tuning method elaborated in this paper is found to be superior as compared with basic PID tuning methods based on second-order plus delay-time (SOPDT) model of process. Two simulation examples are demonstrated to show the applicability and effectiveness of the given method. DOI: 10.17762/ijritcc2321-8169.160412

A Study of Advanced Modern Control Techniques Applied to a Twin Rotor MIMO System

The twin rotor MIMO system (TRMS) is a helicopter-like system that is restricted to two degrees of freedom, pitch and yaw. It is a complicated nonlinear, coupled, MIMO system used for the verification of control methods and observers. There have been many methods successfully applied to the system ranging from simple proportional integral derivative (PID) controllers, to machine learning algorithms, nonlinear control methods and other less explored methods like deadbeat control and various optimal methodologies. This thesis details the design procedure for two different control methods. The first is a suboptimal tracking controller using a linear quadratic regulator (LQR) with integral action. The second is the design of several adaptive sliding mode controller to provide robust tracking control of the TRMS. Once the design is complete the controllers are tested in simulation and their performance is compared against a PID controller experimentally. The performance of the controllers are also compared against other controllers in the literature. The ability of the sliding mode controllers (SMC) to suppress chattering is also be explored

Optimized Neural Networks-PID Controller with Wind Rejection Strategy for a Quad-Rotor

In this paper a full approach of modeling and intelligent control of a four rotor unmanned air vehicle (UAV) known as quad-rotor aircraft is presented. In fact, a PID on-line optimized Neural Networks Approach (PID-NN) is developed to be applied to angular trajectories control of a quad-rotor. Whereas, PID classical controllers are dedicated for the positions, altitude and speed control. The goal of this work is to concept a smart Self-Tuning PID controller, for attitude angles control, based on neural networks able to supervise the quad-rotor for an optimized behavior while tracking a desired trajectory.  Many challenges could arise if the quad-rotor is navigating in hostile environments presenting irregular disturbances in the form of wind modeled and applied to the overall system. The quad-rotor has to quickly perform tasks while ensuring stability and accuracy and must behave rapidly with regards to decision making facing disturbances. This technique offers some advantages over conventional control methods such as PID controller. Simulation results are founded on a comparative study between PID and PID-NN controllers based on wind disturbances. These later are applied with several degrees of strength to test the quad-rotor behavior and stability. These simulation results are satisfactory and have demonstrated the effectiveness of the proposed PD-NN approach. In fact, the proposed controller has relatively smaller errors than the PD controller and has a better capability to reject disturbances. In addition, it has proven to be highly robust and efficient face to turbulences in the form of wind disturbances

PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks

Deep reinforcement learning (RL) has shown immense potential for learning to control systems through data alone. However, one challenge deep RL faces is that the full state of the system is often not observable. When this is the case, the policy needs to leverage the history of observations to infer the current state. At the same time, differences between the training and testing environments makes it critical for the policy not to overfit to the sequence of observations it sees at training time. As such, there is an important balancing act between having the history encoder be flexible enough to extract relevant information, yet be robust to changes in the environment. To strike this balance, we look to the PID controller for inspiration. We assert the PID controller's success shows that only summing and differencing are needed to accumulate information over time for many control tasks. Following this principle, we propose two architectures for encoding history: one that directly uses PID features and another that extends these core ideas and can be used in arbitrary control tasks. When compared with prior approaches, our encoders produce policies that are often more robust and achieve better performance on a variety of tracking tasks. Going beyond tracking tasks, our policies achieve 1.7x better performance on average over previous state-of-the-art methods on a suite of locomotion control tasks.Comment: NeurIPS 202

Self-Tuning PID Control via a Hybrid Actor-Critic-Based Neural Structure for Quadcopter Control

Proportional-Integrator-Derivative (PID) controller is used in a wide range of industrial and experimental processes. There are a couple of offline methods for tuning PID gains. However, due to the uncertainty of model parameters and external disturbances, real systems such as Quadrotors need more robust and reliable PID controllers. In this research, a self-tuning PID controller using a Reinforcement-Learning-based Neural Network for attitude and altitude control of a Quadrotor has been investigated. An Incremental PID, which contains static and dynamic gains, has been considered and only the variable gains have been tuned. To tune dynamic gains, a model-free actor-critic-based hybrid neural structure was used that was able to properly tune PID gains, and also has done the best as an identifier. In both tunning and identification tasks, a Neural Network with two hidden layers and sigmoid activation functions has been learned using Adaptive Momentum (ADAM) optimizer and Back-Propagation (BP) algorithm. This method is online, able to tackle disturbance, and fast in training. In addition to robustness to mass uncertainty and wind gust disturbance, results showed that the proposed method had a better performance when compared to a PID controller with constant gains.Comment: 7 pages, 18 figures, The 30th Annual International Conference of Iranian Society of Mechanical Engineer

PID Gain Tuning for Robust Control of PMDC Motor for External Disturbance Rejection with Constrained Motor Parameter Variations through H∞

This chapter describes the controller modeling for PID gain tuning against the external disturbances with constrained internal parameter variation of the PMDC motor based on an optimization technique of H-infinity. To fit the goals in the H infinite framework, auto tuning of the PID controller gains is used. Different performance goals for tracking are preset as design objectives. Researchers in literature have presented many Robust Control techniques for motor control applications. Methods like back-stepping algorithms, fuzzy and neural based control systems, model predictive control and SMC (Sliding Mode Control) are available in literature. In this chapter, SC (speed control) of PMDC-motor is addressed with variations in outer load disturbances and internal variations of the system parameters for a particular application. C-PID (conventional PID controllers) is preferred, and equivalent robustness characteristics are established using the H-infinity development procedures. The optimization effort is to get simultaneous fast-tracking response and better disturbance rejection