Control Of Rotary Inverted Pendulum Research Articles

Stabilization control of rotary inverted pendulum using a novel EKF-based fuzzy adaptive sliding-mode controller: design and experimental validation

This article methodically develops an improved self-regulating fuzzy-adaptive Sliding Mode Controller (SMC) that strengthens the disturbance compensation capacity of the nonlinear rotary pendulum systems while effectively attenuating the chattering content and curbing the control energy consumption. The article contributes to augmenting the SMC with online adaptation tools to achieve the said objectives. It employs the conventional Gao’s power-rate reaching law as the baseline. The scaling gain and the power rate of the said reaching law are adaptively modulated via a pre-calibrated two-input state-error-driven fuzzy nonlinear function. Additionally, the sign function in the law is also replaced with an odd-symmetric nonlinear fuzzy function to address the hard limits imposed by the former. Finally, the membership functions of the fuzzy function are self-regulated using the Extended-Kalman-Filter to improve the compensator’s adaptability in handling the system’s rapidly changing control requirements under exogenous disturbances. The aforementioned propositions are verified by performing customized and reliable hardware-in-loop experiments on the Quanser single-link rotary pendulum platform. As compared to baseline SMC law, the proposed control procedure contributes a ∼45.2%, ∼48.5%, and ∼34.8% reduction in position-regulation errors, control energy consumption, and peak overshoots, respectively. The experimental assessment validates the proposed control system’s enhanced robustness and chattering-suppression capability.

Rotary Inverted Pendulum Control with Pole Placement

The inverted pendulums are multivariable and highly unstable dynamic systems. The inverted pendulum has been used to answer many modern control and control system designs because it has several problems relating to the system model of nonlinearity, difficulty, and inactivity. In this research, the main topic is the rotatory inverted pendulum. Circular path to eliminate the path that is on the pendulum that is traversed by the transversal path. In this paper, the Inverted Rotatory Pendulum is analyzed by state feedback which is adjusted by pole placement. The result of design selection in the system is very important to pay attention to the area where the pendulum will reach the point of agreement.

Swing up and stabilization control of rotary inverted pendulum based on energy balance, fuzzy logic, and LQR controllers

Rotary Inverted Pendulum (RIP) mimics the behavior of many practical control systems like crane mechanism, segway, unicycle robot, traction control in vehicles, rocket stabilization, and launching. RIP is a fourth-order nonlinear open-loop unstable dynamical system and is widely used for testing the effectiveness of the newly developed control algorithms. In this paper, a Hybrid Control Scheme (HCS) based on energy balance and fuzzy logic controllers is proposed to implement the swing up and stabilization control of RIP. In the proposed control scheme, the fuzzy logic-based state feedback gains are dynamically tuned in real-time by minimizing the absolute error between the desired and actual states to get robust control performance. The proposed HCS is also compared with the conventional Linear Quadratic Controller (LQR) for this application. The comparative results show that the proposed fuzzy logic-based hybrid control scheme gives the optimal control performance in terms of achieving satisfactory transient, steady-state, and robust responses from a given RIP system, as compared to the conventional LQR based control scheme. The proposed control scheme is also relatively less complex with a low computational cost and provides desired response characteristics as compared to the existing ones in the literature.

Difference-Based Mutation Operation for Neuroevolution of Augmented Topologies

In this paper, a novel search operation is proposed for the neuroevolution of augmented topologies, namely the difference-based mutation. This operator uses the differences between individuals in the population to perform more efficient search for optimal weights and structure of the model. The difference is determined according to the innovation numbers assigned to each node and connection, allowing tracking the changes. The implemented neuroevolution algorithm allows backward connections and loops in the topology, and uses a set of mutation operators, including connections merging and deletion. The algorithm is tested on a set of classification problems and the rotary inverted pendulum control problem. The comparison is performed between the basic approach and modified versions. The sensitivity to parameter values is examined. The experimental results prove that the newly developed operator delivers significant improvements to the classification quality in several cases, and allow finding better control algorithms.

Real-Time Stabilization Control of a Rotary Inverted Pendulum Using LQR-Based Sliding Mode Controller

Inverted pendulum has been a benchmark system in dynamics and control theory. This system has an inherit feature of being nonlinear, unstable and underactuated. Due to which, it is widely known as a test bench to evaluate the capability and performance of emerging control algorithms. Furthermore, the inverted pendulum system is also known to resemble many real-world problems including Segway’s, humanoid robots, to name a few. The literature on inverted pendulum system reveals a wide range of controllers. The linear quadratic regulator (LQR) is one of the well-known optimal controllers; however, it lags in robustness. The sliding mode controller is known for its robustness characteristics; however, it has a problem of non-robust reachability phase. To solve the problems in both the controllers while retrieving their advantages, this paper presents the design of a hybrid controller which is a combination of LQR and sliding mode controller for the robust control of rotary inverted pendulum. This controller uses LQR as a baseline controller for optimal performance and a sliding mode controller to robustly control the system against matched uncertainties. The robustness of the proposed controller is validated on a real-time rotary inverted pendulum subjected to an input disturbance. The obtained simulation as well as experimental results show that the proposed controller performed satisfactorily with robustness to matched uncertainties. Furthermore, the problem of chattering in the controller is dealt by smoothening the control input with insignificant loss in robustness.

Novel Fuzzy Preview Controller for Rotary Inverted Pendulum under Time Delays

Novel Fuzzy Preview Controller for Rotary Inverted Pendulum under Time Delays

Design and Analysis of Linear Quadratic Regulator for a Non-Linear Positioning System

The control of rotary inverted pendulum is a case of classical robust controller design of non-linear system applications. In the control system design, a precise system model is a pre-requisite for an enhanced and optimum control performance. This paper describes the dynamic system model of an inverted pendulum system. The mathematical model was derived, linearized at the upright equilibrium points and validated using non-linear least square frequency domain identification approach based on measured frequency response function of the physical system. Besides that, a linear quadratic regulator (LQR) controller was designed as the balancing controller for the pendulum. An extensive analysis was performed on the effect of the weighting parameter Q on the static time of arm, balance time of pendulum, oscillation, as well as, response of arm and pendulum, in order to determine the optimum state-feedback control vector, K. Furthermore, the optimum control vector was successfully applied and validated on the physical system to stabilize the pendulum in its upright position. In the experimental validation, the LQR controller was able to keep the pendulum in its upright position even in the presence of external disturbance forces.

Genetic Algorithm Tuned Fuzzy Logic Controller for Rotary Inverted Pendulum

In this study, a Genetic Algorithm (GA) is proposed to search for the optimal input membership functions of the fuzzy logic controller. With the optimal membership function, the fuzzy logic controller can efficiently control a rotary inverted pendulum. The advantage of the proposed method is tuning the parameters of membership functions automatically rather than tuning them manually. In genetic algorithm, these parameters are converted to a chromosome which is encoded into a binary string. Because the membership functions are symmetric to zero, the length of each chromosome could be reduced by half. The computation time will also be shorter with the shorter chromosomes. Moreover, the roulette wheel selection is chosen as reproduction operator and one-point crossover operator and random mutation operator are also used. After the genetic algorithm completes searching for optimal parameters, the optimal membership function will be introduced to the fuzzy logic controller. Finally, simulation results show that the proposed GA-tuned fuzzy logic controller is effective for the rotary inverted pendulum control system with robust stabilization capability.

Virtual laboratory for sliding mode and PID control of rotary inverted pendulum

AbstractThis paper presents a new virtual laboratory tool which teaches sliding mode control (SMC) and proportional–integral–derivative (PID) control to graduate students. Additionally, it describes performance differences between two control methods graphically. This educational virtual laboratory tool contains the control of rotary inverted pendulum. This system is a typical example of nonlinear and under‐actuated systems and also well‐known in control engineering for practicing different control theories. At first, the nonlinear dynamic equations of the inverted pendulum are presented. Then a virtual laboratory tool is designed for SMC and PID. After that, the results are analyzed. The validity of designed tool is verified by an experiment. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 400–409, 2013